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Real Moms Are The Real Stars In New Video Series
SHELTON, Conn., Sept. 30, 2013 /PRNewswire/ -- Playtex ®, the baby experts, announced today the launch of "Here's the Deal", a series of three videos featuring Tia Mowry-Hardrict and Tamera Mowry-Housley talking with other real moms about what it's really like to be a mom. The "Here's the Deal" video series offers humorous insights that showcase relatable parenting challenges from moms' lives, in an entertaining way.
(Logo: http://photos.prnewswire.com/prnh/20130930/NY87990LOGO)
Tia Mowry-Hardrict and Tamera Mowry-Housley understand the challenges moms face. "We're real moms too and, trust me, we get that things don't always go according to plan," said Tia Mowry-Hardrict. "Whether things go off course during a diaper change, or at meal time, you have to have a strategy and the right tools to adjust on the fly, which is exactly what these videos are all about." The new "Here's the Deal" videos are featured on the Playtex YouTube channel ( http://www.youtube.com/user/PlaytexBaby) so women everywhere can log on and watch the moms discussing everything from play date etiquette to how to manage a diaper disaster!
"We feel so lucky to have had the opportunity to work with such knowledgeable moms on the development of this series," said Tia Mowry-Hardrict and Tamera Mowry-Housley. "No amount of reading or research can offer the same level of knowledge that other moms can share from past experiences."As well as gaining invaluable insights, viewers of the series will receive a welcome surprise when a coupon for discount on Playtex Baby products pops up on screen! Playtex Baby is offering a specific coupon to coincide with each video, for a value of up to $5. "Playtex listens to moms, we've spent over 60 years continuously learning what babies and moms need, in order to give each new generation the best possible products," commented Christine Moroniti, Energizer Personal Care. "We understand that no two babies are alike so we worked with real moms to develop these videos to ensure that the information we're facilitating is credible and the circumstances are relatable." Visit the Playtex Baby YouTube or Facebook Page to view the series and go to www.playtexbaby.com for more information on Playtex Baby products. About Playtex® Products for Infant Care Playtex Infant Care, a division of Energizer Personal Care, is a leading manufacturer and distributor of infant products including Playtex® Nurser with Drop-Ins® Liners, Playtex® VentAire® bottles, a variety of Spill-Proof Guaranteed cups including The First Sipster®, Sipster® and the Insulator® and Coolster®Cups, Ortho-Pro®and Binky® Pacifiers and the Diaper Genie® Disposal Systems.** Playtex and all other trademarks are licensed or owned by Playtex Products, LLC. About Energizer Personal CareA leading manufacturer of health and beauty products around the world, Energizer Personal Care is a division of Energizer Holdings Inc. (NYSE: ENR), headquartered in St. Louis, Missouri. Energizer Personal Care markets products in over 100 countries worldwide under the Playtex®, Banana Boat®, Hawaiian Tropic®, Edge, Skintimate®and Schick®brands. Playtex and all other trademarks are licensed or owned by Energizer or its affiliates. Meghan Ficarelli/Edelman(212) 704-4562 email@example.com SOURCE Playtex
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Our passion for poetry guides us to encourage people of all ages and backgrounds to participate in the infinite possibilities of the written word. We conduct workshops, contests, outreach programs and readings to promote poetry in the community.
You can also find a copy in your newsletter (download here) or on the website's gallery page. Support your Poetry Center: renew, donate, underwrite. Just DO IT.
Jennifer Perrine is the author of No Confession, No Mass, winner of the 2014 Prairie Schooner Book Prize in Poetry; In the Human Zoo, recipient of the 2010 Agha Shahid Ali Poetry Prize; and The Body Is No Machine, winner of the 2008 Devil's Kitchen Reading Award in Poetry. Jennifer teaches courses in creative writing and social justice and directs the Women's and Gender Studies program at Drake University in Des Moines, Iowa.
Phillip B. Williams is a Chicago, Illinois native. He is the author of the forthcoming book of poems Thief in the Interior. He's also co-authored a book of poems and conversations called Prime. He is a Cave Canem graduate and received scholarships from Bread Loaf Writers Conference and a 2013 Ruth Lilly Fellowship. His work has appeared or is forthcoming in Anti-, Callaloo, Kenyon Review Online, Poetry, The Southern Review, West Branch and others. Phillip received his MFA in Writing from the Washington University in St. Louis. He is the poetry editor of the online journal Vinyl Poetry.
Keith Byler is a physician with ten years in the trenches of the emergency room and is now in private practice. His poems have appeared in Emergency: True Stories From the Nation's E.R.'s, Margie: The American Journal of Poetry, Thema, and Hurricane Blues.
Crawford "Ford" Fanter is an actor, filmmaker, writer, and musician based in St. Louis where he was born and raised. During his off time, he enjoys a stiff Shirley Temple and writing short one- to two- sentence biographies about himself.
MUSIC: Erika May Randolph
The Proud to Be Consortium is proud to present volume 3 of writings by veterans and their families: Proud to Be: Writings by American Warriors, volume 3. Published by Southeast Missouri State University Press with editor Susan Swartwout, the 3rd volume has a reading and release event on Saturday, December 13, 1 pm, at the Focal Point, 2720 Sutton. The event is free and open to the public. A reception follows the reading. The consortium includes the Missouri Humanities Council, Warriors Arts Alliance, South East Missouri State University Press and The St. Louis Poetry Center.

subset: commoncrawl


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21 Jump Street 2012 BDRip XviD-Larceny
seeders: 1
leechers: 0
Cast: Johnny Depp, Caroline Aaron, Ice Cube, Holly Robinson Peete, Joe Chrest, Peter DeLuise, Peter Epstein, Charles Ferrara, Dakota Johnson, Brie Larson and others
A pair of underachieving cops are sent back to a local high school to blend in and bring down a synthetic drug ring.
Written by ROBBREDD6810.01K
21 Jump Street 2012 BDRip XviD-Larceny (Size: 1.39 GB)
Genre: Action | Comedy
IMDB Rating: 7.5/10 from 48,223 users
Directed By: Phil Lord, Chris Miller
Starring: Jonah Hill, Channing Tatum and Ice Cube
Release Name: 21.Jump.Street.2012.BDRip.XviD-Larceny
Size: 1.4 GB
Video: AVI | 624*256 | 1736 Kbps
Audio: English | AC3 | 448 Kbps
Runtime: 1h 45mn
Subtitles: English/English Forced
Plot: In high school, Schmidt (Jonah Hill) was a dork and Jenko (Channing Tatum) was the popular jock. After graduation, both of them joined the police force and ended up as partners riding bicycles in the city park. Since they are young and look like high school students, they are assigned to an undercover unit to infiltrate a drug ring that is supplying high school students synthetic drugs.
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Griffco G-Series diaphragm back pressure valves are designed to enhance the performance of chemical feed systems by applying a continuous back pressure to the chemical feed pump, while also acting as an anti-syphon valve. Robust construction ensures reliability in the rigorous service of municipal and industrial applications. Wetted materials ...
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SWAN - Model M-Flow M40 - Flow Cell - Oxygenby SWAN Analytische Instrumente AG based in Hinwil, SWITZERLAND
Flow cell for the Swansensor Oxysafe1000. Flow cell made of PVC and acrylic glass with one M40 x 1.5 sensor opening, sensor incident flow tube. Suitable for Ø 32 mm sensors with M40 adapter, e.g. SS Oxysafe1000. Removable transparent vessel allows easy sensor calibration and maintenance.
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Flow cell for the connection to tubes for one sensor with ¾' NPT thread. Made of stainless steel (SS316L).
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It may seem unusual to hear chronically blunted spitter The Kid Daytona dedicating a record to the Man Upstairs, but there's no denying it—lead EP single God Is Good is pretty damn fresh. Over Statik Selektah's sunny, sampled production, the freestyle series alum praises the Lord for all the dope things in his life. Complete with an effervescent hook by Goapele, this upbeat jam is one of many fresh cuts set for inclusion on The Interlude 2, dropping at the top of next year.
Written by richard on 10/27/11
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Interview: KryptID (Metal Band)
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November 29th, 2014
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Book Review: Exponential - Author Adam Cesare
December 11th, 2014
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Book Reviews: The Good and the Bad | Run for Your Life Nick McClusky
December 1st, 2014
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Book Review: Axes of Evil - Edited by Alex S. Johnson
November 22nd, 2014
Reviewed by: Dave Gammon
Category: Book Reviews
Film Review: Slices (2008)
SYNOPSIS: In the tradition of Creepshow, this macabre anthology features five short horror films, including "The Exterminator," in which a ruthless businessman gets a taste of his own medicine — and then some — when he's abducted by his own hatchet man. Other eerie tales include "Turnout," "The Range," "Night Scream" and "Dead Letters," in which a grieving writer discovers a way to resurrect his beloved dead wife — but at what cost? REVIEW: Written … Continue reading


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The Grammy award winning Bryan Sutton has created an extensively detailed library of bluegrass guitar lessons here at ArtistWorks. His students have unlimited access to hundreds of flatpick guitar lessons - plus guitar tabs, backing tracks, special guests, and worldwide community of bluegrass guitar enthusiasts.
Best of all, students can receive personalized feedback from Bryan using the ArtistWorks Video Exchange® learning platform. Bryan responds to each student video with unique advice for how to improve. All of Bryan's students can access the Video Exchange library, which is always expanding and may contain the key to unlock your own playing.
Top Quality Guitar Lessons
Video Exchange® Learning
Worldwide Community
Bryan Sutton is arguably the best flatpick guitarist since the late great Doc Watson. Born near Asheville, NC in 1973, Bryan Sutton has been playing guitar since the age of 8. Sutton first came to prominence as part of Ricky Skaggs' bluegrass band Kentucky Thunder. At the beginning of 1999, Bryan retired from Kentucky Thunder so he could devote himself to studio recording. He has also been the guitarist for the legendary bluegrass band Hot Rize for over a decade now.
As a leading session guitarist, he continues to appear on numerous recordings with everyone from Rhonda Vincent, the Dixie Chicks, Taylor Swift, Dolly Parton and many more.
Bryan was awarded a Grammy in 2007, and has been honored as IBMA 'Guitarist of the Year' in 2000, 2003, 2004, 2005, 2006, 2007, 2011, and 2014. In 2011 he was also named 'Specialty Instrument Player of the Year' at the Annual Academy of Country Music Awards.


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Chateau du Broutel
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Le Touquet Experience
My trip to Le Touqet was a chance event when my ex-colleague and very good friend V�ronique walked into my office and said "hey do you want to visit Le Touquet this weekend with my family" & the fun never stopped till I got back...
I started off with a TGV from Lille and got off at Etaples train station, where I had Roland and V�ro awaiting me. After our chit-chats and many jokes our first stop was the Rue ...
Hesdin to Saen Sans
We set off fairly up to time from our delightful hotel very full after a breakfast that has set the bar from now on , no parking tickets but about 1 kg of birdshit on the car. First stop Pasendale's Tyne-Cot allied war cemetery there not sure how many thousand I think 12,000 buried of whom 8,367 are unnamed , 33, 783 commemorated but never located. The museum was very simple in the style of a couple of remaining bunkers some letters, both sides, and a voice reading out names ...
Here We Go Again
... bikes, but these are Comtinental Roadattack GTs, and hopefully better wearing for this heavy bike.
When I returned from the last trip the Dunlop front tyre was strangely triangular in shape, I guess from all that time running at 45 degrees on the tournanti of the Alps. The initial feel of the Contis is good. We'll see how they manage the miles.
I stop for tea ...
Here We Go Again....
... sandwich at a layby on the A1 just before Peterborough. Thickest sausage sarny I ever had.
Then it's an easy run down the M11 and round London on the M25, which for once has no traffic jams. I'm at the Channel Tunnel 2. Hours before my scheduled departure time, but I'm invited to take an earlier train.
In the shuttle I meet three ...
Le Touquet.
... price of menus as we walked around the town. The prices had certainly risen, but we were pleasantly surprised to find that Le Jardin, which had always been expensive in the past, had a Menu du Jour which was priced at €18 for two courses. I decided to treat Neil for doing all the driving, which also ensured no cooking for either of us when we returned to the ...


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Past Exhibitions
UNRAVELED SECRETS: Sonya Kelliher-Combs»
December 2, 2006 – March 4, 2007;
Titled "Unraveled Secrets", the exhibition is comprised of a range of paintings, drawings, and mixed media needlework. More than three dozen paintings and drawings are featured in the show, as well as a mixed media needlework installation created from more than 4,000 threaded needles.
Our Land: Contemporary Art from the Arctic»
October 20, 2006 – February 4, 2007;
Originated by the Peabody Essex Museum in Salem, Massachusetts, this exhibition surveys the breathtaking beauty of art created in the studios of contemporary artists from Nunavut, the northernmost Canadian Province.
Arctic Transformations The Jewelry of Denise and Samuel Wallace»
August 17 – November 5, 2006;
A 25-year retrospective of jewelers Denise and Samuel Wallace highlighting their elaborate use of natural materials in expressing Denise's Chugach Aleut heritage.
Relations: indigenous dialogue»
July 8, 2006 – September 30, 2006;
Relations stands for the interconnection of humans, nature and spirit that define the Indigenous world, and is designed to provoke thought, argument and dialogue.
THE Magazine Show»
June 2 – July 23, 2006;
This exhibition represents artwork that was used as part of an advertising campaign to promote the Institute of American Indian Arts Museum (IAIA) collection.
Dualities: Nathan Begaye + Les Namingha + Dusty Naranjo»
May 12 – June 25, 2006;
Dualities, Nathan Begaye + Les Namingha + Dusty Naranjo May 12 – June 25, 2006


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Date: 1917 ca.
Description: Photographic postcard showing the wing panel assembly for the Lawson Military Tractor 2 (M.T.2).
Date: 1910 ca.
Description: Houses and trees line the boulevard, and in the middle is a landscaped median with a lamp post.
Description: Front entrance, surrounded by trees. A small red building is visible behind the hospital.
Date: 1912 ca.
Description: Colorized postcard image of a sun parlor at the pavilion for tuberculosis cases at the Milwaukee Hospital for Insane.
Date: 1948
Description: A 10-foot mannequin of Alice in Dairyland at an Alice display at the Wisconsin Centennial Exposition.
Date: 1905 ca.
Description: Elevated view of fourteen-story theatre building, with two red buildings on either side of it. On the street are people, horses, carriages, and trolleys. T...
Description: Exterior of classical-style building. Facade has fluted columns, decorated capitals, ornate railings, and a wide stairway leading to the entrance.
Description: View down Oneida Street, looking west, showing City Hall.
Date: 1909 ca.
Description: Railroad bridge over river and buildings at night. In the distance is a building with a clock tower.
Date: 1916 before
Description: South entrance to park. The bridge has stone lion sculptures on each of its corners, as well as ornate street lamps. A lighthouse and attached buildings ar...
Description: Elevated view of fourteen-story theatre at an angle from across the street. Several other buildings can be seen, as well as a road with pedestrians and ho...
Date: 1907 ca.
Description: Wisconsin Street, looking west from the Post Office. Includes the Pabst Building, the Wells Building and the Pfister Hotel.
Description: View down National Avenue. Storefronts line the road, and there are horse-drawn vehicles, pedestrians, and a streetcar.
Date: 1911 ca.
Description: Looking north at the Menomonee Valley. Road incorrectly identified on the postcard as twenty-second street viaduct. Pedestrians and carriages are crossing...
Description: Photograph postcard showing detail of an unidentified part of the Lawson Military Tractor 2 (M.T.2), possibly either the wing or the aileron.
Description: End of wing on left is circular. Trees are in front of hospital in a small park area.
Description: Three-quarter view from front of the interior structure of the Lawson Military Tractor 2 (M.T.2).
Description: Photographic postcard showing the elevator for the Lawson Military Tractor 2 (M.T.2).
Description: A large building on a small hill above a road. Three posts are along the road, and small rails line the the sidewalk near a small drive. A man is standing...
Date: 1915
Description: Two flags are on top of the building, and the tower is on the right.
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Hello everyone, I'm just gonna be straight without too much talking.. I'm a very spontaneous guy, sensitive when necessary with lots of happiness, love life and music! Looking for someone who's genuine :)
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28 • Rishon LeẔiyyon, Israel • Man
I'm looking for
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- Ages 21–26
- Located anywhere
- Who are single
- For new friends, long-term dating
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- White
- Height
- 5′ 5″ (1.65m)
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- Fit
- Diet
- —
- Smokes
- No
- Drinks
- Socially
- Drugs
- Never
- Religion
- Judaism
- Sign
- Gemini
- Education
- Graduated from two-year college
- Job
- Technology
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- Single
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- English (Fluently), Hebrew (Fluently)
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Well, video and music editing, can't live without my music! I'm really good with people and listening to them...and much more.
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Segmentation of an image into separate regions is useful in many computer vision problems. The separate regions may be represented by their enclosing contours. A planar graph is constructed for each image. The optimal contour is determined by the assignment of edge costs and face weights of the graph. An algorithm is provided such that given a planar graph G = (V,E) where V is a set of nodes, E is a set of edges and each edge e is provided with a non-negative cost c(e) and each face f of the planar graph is provided with a non-negative weight w(f), and given a single node v in G and single face F adjacent to v which is viewed as the exterior face, then find a directed path P in G that starts and finishes at v and minimizes ##EQU1## where cost(P) is defined as the length of the path under c, and weight (P) is the weight of the faces that are separated from F by P. The contour that minimizes ζ is the optimal contour.
1. A method of image segmentation comprising the steps of:
constructing a planar graph for an image;
specifying a band around an exterior boundary of an image segment;
assigning a cost to said exterior boundary;
assigning a cost to an enclosed region formed by said boundary; and
minimizing the ratio of the cost assigned to the exterior boundary to the cost assigned to the enclosed region formed by said boundary using a pinned ratio algorithm;
where the ratio is expressed as ##EQU11## and where cost (ei) is a non-negative edge cost of an edge i which cost is inversely proportional to the contrast between two connected pixels and weight (fj) is a non-negative face weight function representing an area enclosed by a contour j.
2. A method of image segmentation comprising the steps of:
constructing a planar graph for an image;
assigning a cost to an exterior boundary of an image segment, where said cost assigned to an exterior boundary of the image segment is an edge cost function of the local intensity edge strength;
assigning a cost to an enclosed region formed by said boundary; and
minimizing the ratio of the cost assigned to the exterior boundary to the cost assigned to the enclosed region formed by said boundary using a pinned ratio algorithm;
where the ratio is expressed as ##EQU12## and where cost (ei) is a non-negative edge cost of an edge i which cost is inversely proportional to the contrast between two connected pixels and weight (fj) is a non-negative face weight function representing an area enclosed by a contour j.
3. A method of image segmentation as set forth in claim 2, where said edge cost function is edge (i)=(255-|grad (i) |)p where p is a positive value and grad (i) is the difference of the pixel intensity values at each side of edge i.
4. A method of image segmentation as set forth in claim 2, where said edge cost function is edge (i)=Prob (X>|grad (i)|) where X is a random variable of the absolute gradient of all edges and |grad(i)| is the absolute gradient for an edge i.
5. A method of image segmentation comprising the steps of:
constructing a planar graph for an image;
assigning a cost to an exterior boundary of an image segment;
assigning a cost to an enclosed region formed by said boundary; and
minimizing the ratio of the cost assigned to the exterior boundary to the cost assigned to the enclosed region formed by said boundary using a pinned ratio algorithm, where the ratio is expressed as ##EQU13## and where cost (ei) is a non-negative edge cost of an edge i which cost is inversely proportional to the contrast between two connected pixels and weight (fj) is a non-negative face weight function representing an area enclosed by a contour j.
6. A method of image segmentation as set forth in claim 5, where the boundary cost is the cumulative probability of the gradient magnitude and the face weight is inversely proportional to the intensity variation at the face pixel.
7. A method of image segmentation as set forth in claim 5, where the face weight is set equal to unity.
8. A method of image segmentation as set forth in claim 5, where the face weight function is exponential.
9. A method of image segmentation as set forth in claim 8, further comprising specifying a point on or exterior boundary as the root node.
10. A method of image segmentation as set forth in claim 8, further comprising specifying a region of the image where the boundary is to be located.
11. A method of image segmentation as set forth in claim 8, where the lowest cost of an exterior boundary is computed by locating all high gradient points in the image and computing the cost of an exterior boundary using each high gradient point as an initializing node.
12. A method of image segmentation as set forth in claim 8, further comprising removing all nodes and boundaries outside a user specified region of the image.
13. A method of image segmentation as set forth in claim 8, further comprising drawing an initial boundary and defining a band around the boundary and computing an optimal boundary in the band.
14. A method of image segmentation as set forth in claim 8, where the cost of an exterior boundary is a weighted sum of a previous cost and the average image curvature of two pixels next to the boundary.
15. A method of image segmentation comprising the steps of:
constructing a planar graph for an image;
identifying an initializing node for computing a cost of an exterior boundary of an image segment;
assigning a cost to the exterior boundary of an image segment;
assigning a cost to an enclosed region formed by said boundary; and
minimizing the ratio of the cost assigned to the exterior boundary to the cost assigned to an enclosed region formed by said boundary using a pinned ratio algorithm;
where the ratio is expressed as ##EQU14## and where cost (ei) is a non-negative edge cost of an edge i which cost is inversely proportional to the contrast between two connected pixels and weight (fj) is a non-negative face weight function representing an area enclosed by a contour j.
16. A method of image segmentation as set forth in claim 15, where said initializing node is a high gradient magnitude edge point.
17. A method of image segmentation as set forth in claim 15, where a neighborhood of points around a selected point are initializing points.
18. A method of image segmentation as set forth in claim 15, further comprising as initializing node which is an edge pixel.
19. A method of image segmentation as set forth in claim 15, further comprising said minimizing including user selection of a initializing node.
The present invention relates to image segmentation and particularly to image segmentation where a segmented region has both an exterior boundary cost and a benefit assigned to the enclosed interior region. Specifically, the ratio between the exterior boundary cost and the enclosed interior benefit is minimized by means of a computationally efficient graph partitioning algorithm.
Segmentation is useful in a wide range of computer vision applications, but fully automatic segmentation remains a difficult problem. There has been extensive work on image segmentation as well as the broader problem of data partitioning. See, for examples, Duda and Hart's "Pattern Classification and Scene Analysis," (John Wiley and Sons: 1973), or the Proc. DIMACS Workshop on Partitioning Data Sets: With applications to psychology, vision and target tracking, I. J. Cox et al, eds (American Mathematical Society: 1994), or Jain and Dubes' "Algorithms for Clustering Data" (Prentice Hall: 1988). The goal of segmentation is to find groups of data which are both homogeneous, such that data in the same group are similar, and well separated, such that data in different groups are dissimilar. Many approaches have been proposed, which may be broadly categorized as either contour-based, region-based or a combination of both.
Contour-based methods usually attempt to partition an image based solely on local measures of dissimilarity. Conversely, region-based methods partition an image based on local measures of similarity. There has been significant progress on interactive contour-based segmentation, based upon the work of Kass et al described in an article entitled "Snakes: Active contour models," in Int. J. Computer Vision, pages 321-331 (1988) and of Blake and Zisserman described in the book entitled "Visual Reconstruction," (MIT Press: 1987). The interactive initialization of the active contour or snake near the desired boundary significantly reduces the difficulty of segmentation. However, there are several difficulties with the method, including sensitivity of the final solution to the initialization and computational requirements. Cohen, in an article entitled "Note on active contour models and balloons," in CVGIP: Image Understanding, vol. 53(2), pages 211-218 (March 1991), introduced a "balloon force" which can either inflate or deflate the contour. The purpose and effect of the "balloon force" are similar to the interior area denominator term of the novel ratio cost discussed later. Amini et al, in an article entitled "Using dynamical programming for minimizing the energy of active contour in the presence of hard constraints," in Proc. Inter. Conf. on Computer Vision (ICCV), pages 95-99 (1988), proposed using dynamic programming as part of an iterative gradient descent procedure. Montanari, in an article entitled "On the optimal detection of curves in noisy pictures," in Communications of the ACM, vol. 15(5), pages 335-345 (1971), uses dynamic programming to detect a globally optimum path through a set of pixels. Later, Geiger at al, in an article entitled "Dynamic programming for detecting, tracking, and matching deformable contours," in IEEE Trans. Pattern Anal. and Machine Intell., vol. 17(3), pages 294-302 (1993), extended this work to accomodate greater uncertainty due to the motion of snakes. Their method finds an optimal solution in polynomial time. Nevertheless, significant computational time is still required though this could be improved substantially.
More powerful techniques attempt to use both region and boundary information in a cost function. Chakraborty et al, in an article entitled "Deformable boundary finding influenced by region homogeneity," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 624-627 (June 1994), recognized the importance of combining contour and region based methods. They proposed a cost function that is the sum of a boundary cost and an a priori term that is provided by a region based segmentation method. Experimental results support combining region and boundary information. However, a global optimization cannot be guaranteed and a separate region segmentation must precede the combined method. Ronfard, in an article entitled "Region-based strategies for active contour models," in Int. J. Comput. Vision, vol. 13(2), pages 229-251 (1994), recently proposed a "region-based energy model for active contours" that again attempts to introduce region information into a contour-based algorithm. However, once again, optimization is difficult and may be susceptible to local minima. Leclerc, in an article entitled "Constructing simple stable descriptions for image partitioning," in Int. J. of Computer Vision, vol. 3, pages 73-102 (1989), proposed a partition process based on a minimum description length representation of both the intensity variation within a region and the enclosing boundary. Most recently, Zhu et al, in an article entitled "Region competition: Unifying snakes, region growing, energy/bayes/mdl for multi-band image segmentation," in Proc. Fifth Int. Conf. on Computer Vision, pages 416-423 (1995), attempted to unify snakes, region growing and energy/Bayes/MDL techniques. However, while these latter approaches offer powerful theoretical frameworks, it is often computationally difficult to minimize the associated cost functions.
Wu and Leahy, in an article entitled "An optimal graph theoretical approach to data clustering: theory and its application to image segmentation," in IEEE Trans. Pattern Anal. and Machine Intell., vol. 15(11), pages 1101-1113 (November 1993), describe an optimal graph theoretic approach to data clustering and their paper provides a good review of many graph based techniques. Wu and Leahy develop a cost function based on boundary cost alone.
The present invention overcomes the limitations of the prior art by incorporating both interior region and boundary information into the segmentation process using very efficient graph partitioning algorithms. The invention teaches the concept of "ratio regions" which minimize a new cost function based on the ratio of the cost of the perimeter of the segmented region to the benefit assigned to the region's enclosed interior. This cost function allows both boundary and interior information to be used in the partitioning process, though in a more restrictive manner than Leclerc, sipra, or Zhu et al, supra. For example, the boundary cost may be chosen to be a function of the local intensity gradient and the interior benefit to be a function of the area enclosed, in which case, the algorithm has an a priori preference for large, high contrast regions. A significant restriction is that the face weights that compose the interior benefit may not vary as a function of the segmentation, though in practice, interesting partitions are still found.
Distinguishing characteristics of the method include, (1) both interior and boundary information are incorporated into the objective function, (2) the smoothness of the contour arises in a novel way based on the global interaction between the region's boundary cost and interior benefit, and (3) a fully automatic segmentation algorithm can be developed based on this cost function that finds the globally optimium solution with a computational complexity of O(n2 log(CW)), where C and W are the boundary cost and interior benefit respectively, and n is the number of pixels in the image. The costs C and W enter the complexity of the algorithm because it is necessary to perform a binary search over theses costs, the complexity of which is logarithmic in the range of their values. If CW is polynomially bounded in n, then log(CW) becomes log(n). In practice, this may be greatly reduced by seeding the algorithm only with points of high intensity gradient, n', reducing the cost to O(n'nlog(CW)). Experimental results for fully automatic segmentation is described below. (4) For the more constrained problem domain in which user interaction is available, the computational cost can be significantly reduced. For example, if the user specifies a single point on the boundary then the complexity reduces to O(nlog(CW)). The present invention is primarily concerned with interactive segmentation. In the context of image partitioning and "ratio regions", several levels of user initialization/interaction can be supported that significantly improve the final segmentation by focusing attention on perceptually significant regions, and reduce the associated computational cost. Important considerations for interactive vision algorithms are robustness and speed and ease of use. Experimental results qualitatively demonstrate several modes of interaction ranging from specification of a single point on the contour, to specification of a rectangular or polygonal subwindow that encloses the region of interest, to a complete initialization of the boundary.
The present invention is concerned with ratio regions that minimize the ratio between the cost of the bounding contour and the benefit assigned to the enclosed region. The cost function can be efficiently minimized using graph theory, particularly if user interaction is available. The optimization of the ratio results in a process where smoothness is not enforced locally, but is an induced characteristic that is a consequence of the global optimization, e.g. an a priori preference for local high content regions from setting the numerator to be a function of the intensity derivative while the denominator measures the enclosed area. This is a novel method of imposing image faces and regularization constraints.
In principle, interior information provides useful information pertaining, for example, to the size of the region or its intensity or texture. However, it should be noted that such information is static and not a dynamic function of the segmentation process.
The relative cost associated with the boundary and interior elements provide a dynamic by which to alter the segmentation. Linear scaling of vertex or face costs does not affect the global minimum because it is equivalent to multiplying all objective functions by a constant. Therefore, non-linear scaling must be employed in order to effect a change.
A principle object of the present invention is therefore, the provision of a method of performing image segmentation taking into account both the interior region and boundary information.
Another object of the invention is the provision of a method of finding ratio regions which minimize a cost function based on the cost of the perimeter of the segmented region to a benefit assigned to the region's enclosed interior.
A further object of the invention os the provision of a method of finding ratio regions which minimize a cost function based on the cost of the perimeter of the segmented region to the area of the enclosed region.
A still further object of the invention is the provision of algorithms for finding ratio regions.
Further and still other objects of the present invention will become more clearly apparent when the following description is read in conjunction with the accompanying drawing.
FIG. 1 is a planar graph constructed from an input image;
FIG. 2 is a graphical example of contours and enclosed regions;
FIGS. 3a, 3b and 3c are images showing the affect on the pinned ratio algorithm of objects with varying areas;
FIGS. 4a and 4b illustrate automatic segmentation of an image;
FIGS. 5a, 5b, and 5c illustrate several examples in which a user-specified sub-region is provided;
FIGS. 6a and 6b illustrate representative segmentations based on the "snake" approach;
FIGS. 7a, 7b, 7c and 7d illustrate the affect of different arc costs on the segmentation;
FIG. 8 illustrates segmentation using an arc cost which incorporate images curvature; and
FIGS. 9a and 9b illustrate the adjustment of face weights to obtain different segmentation.
The primary algorithmic tool useful for practicing the invention is the pinned ratio algorithm. The algorithm operates on planar graphs where a closed contour in the image will correspond to a cycle in the planar graph. This tranformation is described below.
Formally, the pinned ratio problem is defined as follows. Given a planar graph G = (V, E), where V is a set of nodes, E is a set of edges and each edge e is provided with a nonnegative cost c(e) and each face f of the planar graph is provided with a nonnegative weight w(f), and given a single node v in G and a single face F adjacent to v which is viewed as the exterior face and find a directed path P in G that starts and finishes at v and minimizes ##EQU2## where cost(P) is defined as the length of the path under c, and weight(P) is the total weight of the faces that are separated from F by P. The algorithm is contained in Appendix A. A closely related algorithm is described in an article by S. Rao entitled "Faster algorithms for finding small separators in planar graphs," in Proc. of the 24th Annual ACM Symposium on Theory of Computing, pages 229-240 (May 1992).
In order to apply the graph algorithms, there is first constructed a planar graph for each input image. FIG. 1 illustrates a graph constructed from an input image. A four-connected neighbor system for the image is used. A graph arc is established for each neighboring pixel pair of the image as shown in the figure. The four graph arcs that surround an internal pixel enclose a single face of the graph. For an image of size p � q, the corresponding graph has O(pq) arcs and nodes.
The optimal contour is determined by the assignment of edge costs and face weights of the graph. It is crucial to assign proper edge costs and face weights so that the graph cut algorithms will minimize an objective which agrees with the desired segmentation.
First, ignore the intensity information within the region and assign a constant to all the face weights. Then the denominator of Equation 1 represents the area enclosed by the contour. Thus, large regions are preferred to small regions. Assign to each edge a cost that is inversely proportional to the contrast between the two connected pixels. That is, the cost of cutting an edge is low if the intensity difference between the two pixels (faces) is high.
The bias towards large regions must be balanced by the cost of the enclosing contour. This conflict between area and contour determines the resulting segmentation. For example, if the edge costs are too low, then a large region enclosed by weak intensity edges may be preferred over a smaller region of high contrast. Quantitatively, if the edge cost of contour A is ε and that of contour B is nε the two contours can still have the same objective function values if the area in contour B is n times that of contour A.
To better understand the situation, consider the example shown in FIG. 2, where three open contours C1, C2, and C3 are shown, where C1 and C2 enclose region A1, and contours C2 and C3 enclose region A2. If the total edge costs on C1, C2, and C3 are E1, E2, and E3 respectively, there are conditions when the closed contour C1 + C3 will be preferred to C1 +C2. The objective for C1+C2 is given by ##EQU3## the objective for C1+C3 is given by ##EQU4## It is then easy to derive that ζ1>=ζ2 if and only if ##EQU5##
Clearly, contour C3 will always be preferred over C2 if its cost is less, i.e. E3 < E2, since more area is enclosed by C1+ C3. However, in the limit, for abitrary C1, C2, E1, E2, and A1, there always exists a contour C3 such that A2 can be so large that the inequality in Equation 2 will hold, regardless of the edge costs on C3. This is because while the contour cost increases linearly with the length of the contour, the area increases quadratically. Fortunately, this does not occur in practice, since images are of finite size. Nevertheless, the algorithm may not find a small high contrast object in a large image if the edge and face weights are incorrectly chosen. That is, the cost function is not scale invariant, but biased towards large regions. FIGS. 3a-3c illustrate this point. FIG. 3a shows a large white rectangle on a dark background with white Gaussian noise added to the image. The black boundary shows that the white rectangle is easily segmented from the image. FIG. 3b is similar to FIG. 3a, but the white rectangle is now much narrower with correspondingly less area. In this circumstance, the algorithm fails to find the rectangle, but instead, finds a region with higher boundary cost but significantly larger area and correspondingly smaller ratio cost.
There are two solutions to this problem; increasing the edge costs or reducing the face costs. However, linear scaling of edge costs or face weights has no effect, since the minimum ratio solution will remain the same. It is therefore necessary to alter the costs in a non-linear fashion in order to affect the segmentation. An example of this is shown in FIG. 3c where the edge costs have been squared. In this case, the correct segmentation has been found for the narrow rectangle. An alternative solution is to confine the graph to a subregion of the image that encloses the object of interest and within which the object of interest is large.
It is very easy to encode region information into the formulation as long as a model (constant intensity, homogeneous region, or texture models) of the enclosed region is available and static. By static, it is meant that the model cannot be a function of the segmentation process. This is a limitation of the invention but nevertheless, different weight functions can be defined to serve different purposes. The general guidance is that, because the algorithm minimizes an objective which is inversely proportional to the summation of the face weights in the region, it is possible to relate the weight function to the residuals of the region model, in a way that the weight is a decreasing function of the residuals. This ensures that the better the region agrees with the model, the smaller the objective function values will be.
In the following description, unless otherwise stated, the face weights were set to unity and the denominator term favored regions of large area. However, many other face weights are possible. There are defined two such face weight functions which are appropriate for segmentations of (i) a region with a known constant intensity value, and (ii) a homogeneous region with an unknown intensity.
For a region with a known constant intensity value, let the greyscale value of the desired region be IR. The intensity model for the region is Ii =IR + εi for the ith pixel, where εi is the residual value. The face weight function is defined as:
weight(i)=exp{-αεi 2 }=exp{-α(Ii -IR)2 }. (3)
where parameter α is related to the variance of the noise process εi. This weight is used to segment regions of known intensity.
For a homogeneous region with an unknown intensity, assume a simple MRF model for the homogeneous region with an unknown intensity: ##EQU6## where the summation is over the neighboring N pixels (N = 4 in our case). Then the face weight function is given by: ##EQU7##
This weight is used to segment homogeneous regions of an unknown intensity.
This function penalizes faces near the boundary of a region, but other weight functions are possible that avoid this result. However, the primary goal is to describe how a wide variety of region information might be incorporated into the ratio cost function but one must be aware that specific proposals may not be optimal for all cases.
The edge costs are defined so that the contour is attracted to salient edges in the image, i.e. edge costs should be small near pixels of high gradient magnitude. The choice of the edge cost function (for a byte image) is as follows:
edge(i)=(255-|grad(i)|)p (5)
where grad(i) is the difference of the pixel intensity values at each side of edge i, that is, the greyscale gradient at the edge, and the maximum absolute greyscale gradient is 255. This edge cost applies when the edges of the object boundary are very strong and consistent. However, when the contours are of low contrast, the edge cost tends to be dominated by the effect of the face weight. To remedy this, it is necessary to increase the penalty difference between strong edges and weak edges. Note once again that by multiplying all the edge costs by a constant, the optimal solution remains the same because it is equivalent to multiplying all objective functions by the same constant. Appropriate nonlinear transformations can change the edge costs such that a different segmentation is found, e.g. by varying the value of p in Equation 5.
A nonlinear transform was used which also has the effect of normalizing. This edge cost measure is based on the histogram of the absolute gradient |grad(i)| for all the edges. Let X be the random variable of the absolute gradient of all the edges, the new edge cost is defined as:
edge(i)=Prob(X>|grad(i)|) (6)
The edges in the image, which usually possess high gradient magnitude, correspond to the upper tail in the histogram. So the costs of true edges are no larger than the proportion of the edge pixels in the image, which is a very small number. Note that the cost is monotonically decreasing with gradient magnitude between 0 and 1. Consideration was given to basing the edge cost simply on the frequency of the absolute gradient, i.e. edge(i)=Prob(X), but this is not monotonic and did not fit our intuition that the larger the gradient, the smaller the edge cost should be. The cost function of Equation 6 was found to give better results than that of Equation 5.
The proposed method appears to be quite different from the active contour model in that it prefers large objects in the image, while a snake has the tendency to shrink. The objective function of a snake is a weighted sum of different terms describing the curvature, the coherence, and the image forces, so that the contour is locked to salient edge features while maintaining its smoothness. The algorithm uses both border and region information, minimizing the ratio of the cost of a boundary to the benefit associated with the enclosed region. Despite these differences, the two approaches are related.
The original snake proposed by Kass et al., supra, is an energy-minimizing spline which minimizes the following energy term: ##EQU8## where εint (s)=(α(s)|vs (s)|2 +β(s)|vss (s)|2)/2 characterizes the stretch and smoothness of the snake, εimage (v(s)) represents the image forces, and εcon (v(s)) represents the external constraint forces.
When it is used for image segmentation, the external constraint forces are ignored and the images forces are taken to be εedge =-|∇I(x,y)|2. The energy function is a mixture of a coherence term which reduces the length, a smoothness term which reduces the curvature and the edge term which increases the image gradient. In particular, the smoothness measure, in terms of sum of squared curvature, is calculated based on the local geometry on the image grid.
The preferred objective function, which can be viewed as the ratio of a weighted length to a weighted area, imposes the smoothness and image forces in a different way. In a simplified case, ignoring the image information and using the unit edge cost and unit face weight assignment in Equation 1, the function reduces to: ##EQU9## which is the perimeter of the contour over the area enclosed. If we condition on the length of the perimeter, the area is maximized when the contour becomes a circle, which gives the optimal solution. The same result is obtained if the image force is nullified in the formulation of the snake (Equation 7) and the smoothness term of the sum of squared curvature is minimized. This is true because the total curvature is a constant of 2π (for a closed contour). Under this constraint, the integral of the squared curvature is minimized if the curve has constant curvature everywhere, i.e. is a circle. Furthermore, if the perimeter is allowed to change, the conditioned minimum ζ in Equation 8, achieved at a circle, is inversely proportional to the radius of the circle ##EQU10## It is interesting to note that the sum of the squared curvature in Equation 7 is also inversely proportional to the radius of the circle. In the snake energy function, the smoothness term itself favors smooth and large contours. But the stretch term prefers contours of short lengths. These two terms serve inconsistent purposes. It depends on the relative weighting between the two regularization terms whether large or small objects are preferred, which is rather ad hoc. In contrast, it is preferable to encode the smoothness constraint from a global point of view via the area and perimeter measures. This has the advantage over the local enforcement because the local curvature calculated from the neighboring pixels on the grid are subject to discretization errors. The effect of image forces takes place when we weight the perimeter and area elements according to the image features. By choosing different edge costs and face weights different segmentations can be achieved. This may be viewed as a new way to impose image forces and regularization constraints.
The contour optimization algorithm has been tested on a variety of images including medical cardiac images and outside scenes. The pinned ratio algorithm, finds the optimal contour which passes through a given node. However, segmentation can be either fully automatic or with increasing degrees of interactivity. Unless otherwise stated, the edge cost is the cumulative probability measure of Equation 6 and the face weights are set to unity.
In the automatic segmentation mode, no a priori knowledge is available about the location of the contour. The program automatically searches the image for the contour which minimizes the objective function. Since the algorithm needs an initializing node (seed) before returning the optimal closed contour it is possible, in principle, to repeatedly run this program for all possible starting nodes. In practice, some simple heuristics can be applied to greatly reduce the computation. For example, as the pixels of large gradient magnitude are likely to be on or near the object boundary, the best solution should pass some such points. Therefore, it is possible to use high gradient magnitude edge points as seeds and approximate the best contour in the image as follows: (a) first, locate all the high gradient points in the image; and (b) run the contour optimization algorithm using each of the salient gradient points as the seed or root node. Then an optimal closed contour for each edge pixel is obtained. Return the contour which has the minimum objective as the best closed contour in the image.
FIGS. 4a and 4b show results of automatic segmentation on a medical image. FIG. 4b illustrates the complex interplay between the boundary and interior costs. While larger regions exist in the image, they are bounded by lower contrast edges with correspondingly higher costs. The vertical segment on the left part of the boundary appears to cutoff a small bright region. However, a careful examination of the original image in FIG. 4a shows that there is a higher contrast vertical edge within this bright region and it is this edge that the segmentation prefers.
In many applications, user interaction is available to constrain the search range, thereby reducing the amount of computation. This interaction can be expressed in terms of (i) specifying a point on the contour, (ii) specifying a region where the contour lies in, and (iii) specifying a band around the contour.
When a point on the contour is known, this point is used as the root node for the core algorithm, which returns the optimal closed contour that passes through this point. Sometimes it may be difficult to provide a node which is exactly on the desired segmentation contour. It is more useful in practice to provide a point and the program computes the best contour near the given point. The algorithm can be extended to support this. To do so, simply define a neighborhood around the selected point, and apply the contour optimization algorithm using each pixel in the neighborhood as the root node, and then return the contour which has the minimum objective. Further computational savings can be achieved by restricting root nodes to be edge pixels, i.e. having high intensity gradient within a given neighborhood rather than any pixel.
In some applications a region of interest can be specified in the input image within which the optimal closed contour is desired. To support this, a planar graph is constructured only for the region of interest, i.e., the region enclosed by a user specified polygon, by removing all the nodes and arcs outside the polygonal region. Also, a user can either specify a particular root and find the best contour in the region which passes through the root, or let the program automatically find the best solution in the region as described above. Note that the complexity of the pinned ratio program is O(nlogn) where n is now the number of pixels in the region of interest.
FIGS. 5a-5c illustrate several examples in which a user-specified subregion is provided. Defining a subgraph within which to search, is significantly different from providing a coarse initial contour. In particular, FIG. 5a illustrates that the final optimum contour may be a significant distance from the boundary of the subregion. Similarly, in FIG. 5c, it is unlikely that a traditional snake would find the cars based on the rectangular initialization, especially because the presence of high contrast structure between the boundary and the cars would almost certainly result in a strong local minimum near these structures. However, it is not always obvious what region will be selected. For example, in FIG. 5a two bright regions are included in the polygonal subregion but only the left one is selected.
The active contour model can also be supported within the present framework, provided a closed contour is desired. To do so, an initial closed contour is drawn, and a "band" is derived around it. Then the best contour contained in the graph defined by this band, i.e., in the vicinity of the initial contour. Special care should be taken to construct the planar graph for such a segmentation task, since the segmentation should be within the band only. The graph is set up as follows: (1) first, construct a planar graph for the whole image as described in FIG. 1. (2) Then, remove all the nodes and the corresponding arcs in the region surrounding the "band". (3) To handle the "hole" region, remove all the nodes and the corresponding arcs inside the "hole". In this way, all the small faces inside the "hole" region merge together and this region collapses into a single face. This face is then assigned a weight which is the summation of the weights of all the small faces inside it.
By applying the contour optimization algorithms to this graph the only contours located are those that reside in the specified "band". FIGS. 6a and 6b illustrate representative segmentations based on this "snake" approach. In each of the image, an initial contour, in the form of a dark polygon is drawn. Then a predetermined width (in the FIG. �15), is used to form a "band" centered at the initial contour. The best contour in the "band" is then located. Note that, for a fixed width, the number of the pixels in the "band" is linear with respect to the length of the contour and the complexity is approximately O(llogl) where l is the contour length. Two examples of this form of initialization are shown in FIGS. 6a and 6b.
In the above experiments, a face weight of unity was used and the cumulative probability arc cost defined by Equation 6. It will now be shown varying the arc costs and/or face weights can affect the segmentation results. In each case, the segmentation was initialized with a single point on the desired contour. This is denoted by a cross in the corresponding figures.
In FIGS. 7a to 7d the power function in Equation 5 was different with different powers p. FIG. 7a shows the segmentation when p = 0.5 is used. This cost is very poor in discriminating the strong edges and poor edges. So the area term plays the major role in the segmentation. As a result, the segmentation encloses almost the entire image with a contour of rather poor intensity gradient. In FIG. 7b a value of p = 0.9 was used, which discriminates more strongly between strong and weak edges. The segmentation identifies half of the object boundary, but also includes a very weak edge. The power p was increased to rescale the cost nonlinearly with respect to gradient magnitude. When p = 0.95, the cost is sufficient to overcome the effect of inflating area (FIG. 7c). The segmentation using the cumulative probability cost is presented in FIG. 7d. Because of the high contrast of the boundary, the segmentation using the polynomial arc cost with power 0.95 is very similar to the one using the cumulative probability cost. However, because there are several very closely aligned edges at the boundary, the optimal contour passes through the inner and outer edges inconsistently, based on the conflicts between the area gain and the edge cost. Note that this image presents a tough problem for most contour based segmentation approaches.
A more elaborate arc cost which incorporates the image curvature can produce more consistent and smoother contours. This can be accomplished by assuming that the contour curvature "coincides with appropriate changes in intensity" as in Geiger et al, supra. This quantity can be calculated a priori for every pixel in the image. Then define a new arc cost which is a weighted sum of the previous cost and the average image curvature of the two pixels aside of the arc. This objective function prefers small curvature as well as large image gradient. This curvature is not based on the geometry of the contour. Instead, it requires that arcs on the contour have small image curvature. FIG. 8 shows the best segmentation of a brain image when image curvature is used in the arc cost. This time the outer edge is consistently located. In all the segmentations the unit face weight is used.
In a final set of experiments, the arc costs were fixed to be the cumulative probability of the gradient magnitude (Eq. 6), and the face weight was manipulated for different segmentation purposes. FIG. 9a shows segmentation of a region of a known intensity. When an intensity of 230 was used the bright region in the brain grey was located. FIG. 9b shows segmentation of a homogeneous region of an unknown intensity, where a face weight was assigned inverse proportionally to the intensity variations at the face pixel. This time it finds the large region of relatively homogeneous intensity, i.e., the boundary of the brain grey in the image. The white contour denotes the segmentation result when the face weight is assigned using Equation 4. The arc cost of cumulative probability of gradient intensity was used.
While there has been described and illustrated a preferred method for image segmentation using ratio regions, it will be apparent to those skilled in the art that variations and modifications are possible without deviating from the broad principles and spirit of the present invention which shall be limited solely by the scope of the claims appended hereto.
APPENDIX A______________________________________Gen-- list *do-- cut-- algorithm(root, g)Node *root;Graph *g;/* Build a shortest path tree in G rooted at root. */planar-- sp-- part(root, g) ;dag = slash-- construct-- dag(root,g) ;/* introduces a slash from root to the external face. Then constructs a dag based on a tree traversal of the shortest path tree where the edges are circularly ordered in a clockwise fashion around a node according to a planar embedding of G. *//* The resulting dag contains a path for each possible pinned connected circuit that does not cross the "slash" introduced into the image by "slash-- construct-- dag" at all and crosses any other path in the shortest path tree at most once. *//* Now binary search using parameterized shortest path procedure for alpha where the cost of the shortest path in the dag from the first node to the last is 0 where the cost of an arc is c' (e) = c(e) - \alpha w(e). */bin-- solution-- cst =parameterized-- shortest-- path(solution-- cst+epsilon,dag) ;if (bin-- solution-- cst<solution-- cst){max-- alpha =solution-- cst;min-- alpha = bin-- solution-- cst/2.0;}else{min-- alpha =solution-- cst/2.0;max-- alpha = solution-- cst;}while ((max-- alpha - min-- alpha > epsilon)){best-- alpha = (max-- alpha+min-- alpha)/2.0;bin-- solution-- cst =parameterized-- shortest-- path(best-- alpha,dag) ;if(bin-- solution-- cst == LARGE-- FLOAT) break;if (bin-- solution-- cst > best-- alpha)min-- alpha = best-- alpha;elsemax-- alpha = bin-- solution-- cst;}return(solution);}float parameterized-- shortest-- path(alpha,dag)float alpha;Dag *dag;{/* finds the shortest path in a dag from node 0 to node nunder cost function c(e) - alpha w(e) */}float compute-- face-- cost (i,j,prev-- adj,bgn--adjs,adjs)int i,j;Dadj *prev-- adj;int * bgn-- adjs;Dadj *adjs;/* Computes the sum of costs along a path on some face. */{}Dag *slash-- construct-- (root, g) Node *root; Graph *g;/* Introduces a slash from root to the external face. Then constructs a dag based on a tree traversal of the shortest path tree where the edges are circularly ordered in a clockwise fashion around a node according to a planar embedding of G. *//* The resulting dag contains a path for each possible pinned connected circuit that does not cross the "slash" introduced into the image by "slash-- construct-- dag" at all and crosses any other path in the shortest path tree at most once. */{}______________________________________
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Zero Day Weekly: CurrentC hacked, White House breached, APT28 exposed, Verizon shamed
A collection of notable security news items for the week ending October 31, 2014. Covers enterprise, controversies, reports and more.
Staying on top of the latest in software/hardware security research, vulnerabilities, threats and computer attacks.
Larry Seltzer has long been a recognized expert in technology, with a focus on mobile technology and security in recent years. He was most recently Editorial Director of BYTE, Dark Reading and Network Computing at UBM Tech. Prior to that he spent over a decade consulting and writing on technology subjects, primarily in the area of security. He is the author of three books and thousands of published articles and many more unpublished, private reports. Larry has been Technical Director at several test laboratories where he both directed and ran product testing, with a special interest in test automation. Larry began his career as a Software Engineer at the now-defunct Desktop Software Corporation in Princeton, NJ, on the team that wrote the NPL 4GL query language. He also worked on corporate IT and software development at Chase Econometrics. Larry is a graduate of the University of Pennsylvania with a degree in Public Policy.
Ms. Violet Blue (tinynibbles.com, @violetblue) is a freelance investigative reporter on hacking and cybercrime at Zero Day/ZDNet, CNET and CBS News, as well as a noted sex columnist. She has made regular appearances on CNN and The Oprah Winfrey Show and is regularly interviewed, quoted, and featured in a variety of publications that includes ABC News and the Wall Street Journal. She has authored and edited award-winning, best selling books in eight translations and has been a sex columnist for the San Francisco Chronicle. She has given keynote talks at such conferences as ETech, LeWeb, and the Forbes Brand Leadership Conference, and has given two Tech Talks at Google. In 2010, the London Times named Blue one of "40 bloggers who really count." Ms. Blue is the author of The Smart Girl's Guide to Privacy. Violet Blue bio courtesy of TTI Vanguard.
A collection of notable security news items for the week ending October 31, 2014. Covers enterprise, controversies, reports and more.
The 'Highly Critical' vulnerability revealed two weeks ago was widely exploited just hours after it was announced. But there's no evidence yet of actual, widespread attacks.
CurrentC, the merchant's answer to NFC payment systems, has been hacked during its pilot program.
By default, Internet Explorer on Windows client systems supports SSL version 3.0, the version recently found vulnerable to attack. Now there's another way to turn it off.
The Obama Administration has admitted that a cyberattacker was able to gain access to the US government's systems.
New McAfee research claims that a worrying number of organizations are turning off advanced firewall features in order to avoid slowing down networks.
Microsoft made a mistake by extending XP Extended Support. Extending Win7 Mainstream Support some time past the intro of Windows 10 would be different.
The older cellular encryption can be cracked by the National Security Agency, leaked documents previously showed. An upgrade to that security could make eavesdropping a lot harder.
Apps running in mobile operating systems must get permission from the user for all sorts of things. This can be confusing. Do some do it better than others?
FireEye has released a detailed report suggesting that state-sponsored attacks originating from Russia have focused on lifting military, government and security information.
Big Blue has unveiled a new enterprise solution aimed at harnessing Big Data to detect criminal activity in seconds.
The latest vector for exploits of the Shellshock bug in the Bash shell is SMTP, where the mail headers themselves trigger the exploit.
Fake transactions from Brazil take advantage of implementation errors to approve what appear to be chip card purchases without the PIN. Hint to banks: It's "Chip AND PIN," not OR.
Researcher finds malicious Tor exit node which envelops Windows EXEs inside another Windows EXE which drops malware.
A collection of notable security news items for the week ending October 24, 2014. Covers enterprise, controversies, reports and more. UPDATED.


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Search Results for 'Eastman Chemical Company'
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Published July 30, 2014
Acquisition supports company's commitment to microfibers growth platform. Read More »
By Kimberly Cloud, Marketing Communications - Microfibers
Unlock an endless array of formulations and unprecedented product design flexibility with Eastman Cyphrex microfibers. Cyphrex offers the ability to fine-tune microfiber properties for higher performance. It's a game-changing toolkit that opens… Read More »
Published February 6, 2014
Blending microfibers helps achieve precision, consistency. Read More »
Published April 25, 2013
Cyphrex microfibers unlock unlimited possibilities for product performance, quality and value for specialty nonwovens. Read More »
Published July 31, 2012
New China facility to produce 50,000 metric tons; will come online by end of 2014. Read More »
Published June 27, 2011
Acquisition supports plasticizer growth strategy. Read More »
Published October 26, 2010
Transaction is expected to close in the fourth quarter. Read More »
Published March 2, 2006
Eastman Chemical Company, through its Chinese subsidiary, Eastman (Shanghai) Chemical Commercial Co., Ltd., has entered into an exclusive, long-term agreement with West Tech Chemical Ltd. under which West Tech will build and operate a manufacturing f… Read More »
Published November 9, 2005
Eastman Chemical Company will expand production of hydrogentated hydrocarbon resin by 25% at its Middelburg, The Netherlands facility. The planned completion date for the expansion is September 2006. "Throughout the world, demand for Eastman hyd… Read More »
By Karen McIntyre, editor
Published December 18, 2014
Poise Impressa could change landscape of stress incontinence market Read More »


text: Target : free shipping on every order, Expect more. pay less. free shipping or free same day store pickup on all online orders. plus, save 5% every day with a target redcard..Eastern pennsylvania pumpkin patches, corn mazes, hayrides, This web site has a complete list of pumpkin patches, corn mazes, hayrides and more in eastern pennsylvania (and the rest of the united states, canada, and other.


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Iowa Senate Republicans advance income tax plan. TheGazette.com reports:
Sen. Randy Feenstra, R-Hull, said all 24 minority Senate Republicans have signed onto a proposal to significantly lower state personal income tax rates and simplify the Iowa tax code by offering a two-pronged approach that would eliminate federal deductibility and benefit most Iowans.
The Hull Republican said the proposed new tax structure would flatten the current nine income tax brackets into three, elimination of federal deductibility as a competitive impediment, enhance the current standard deduction for all taxpayers and provide an extra boost for blind, elderly and dependent Iowans, eliminate itemized deduction, increase personal exemption credits, and raise filing thresholds.
So far I have been unable to find the bill (though it being April 11, I'm not going to spend a lot of time looking for it today). As Senate Republicans have no chance of advancing a bill in the face of majority Democratic opposition, it's really a gesture. Still, it's nice to see that income tax reform remains alive, in spite of the Governor's indifference this year. It's also nice to see that the insistence on keeping the deduction for federal taxes is eroding. Much better to build it into a lower rate.
If they keep talking taxes, they may finally see that The Quick and Dirty Iowa Tax Reform Plan is the way to go!
Radio Iowa has more.
Megan McArdle, "Tax Breaks for Corporate Jets": The Non-Issue at the Heart of the Presidential Agenda:
This is a bit weird given that President Obama rides on what is essentially the nicest corporate jet in the world. To be fair, the President is quite right that companies do not need a tax break to buy corporate jets. But since they don't really get a tax break for buying corporate jets, we probably don't need to spend this much valuable presidential time worrying about this non-problem.
Anything to make life difficult for a high-tech U.S. manufacturer. As long as the President continues to beat dead horses like this and the "Buffett Rule," we know he is not at all serious.
Tony Nitti, Tax Aspects Of The President's FY 2014 Budget
Howard Gleckman, The Real 2014 Budget Battle May Be Over Spending, Not Taxes
William McBride, President Obama's 2014 Budget Takes another Whack at Savers (Tax Policy Blog)
Paul Neiffer, Here We Go Again!
Cara Griffith, Crafting a Better Mainstreet Fairness Act? (Tax.com)
By enacting it? How Democrats Will Destroy Progressive Government (Joseph Thorndike, Tax.com):
Sure, Democrats pay lip-service to infrastructure, education, and the like. But for the most part, they are profoundly unwilling to make a wholistic case for activist, progressive government.
Actually, they probably wouldn't get very far making the case honestly.
TaxProf, Is the IRS Stalking You on Facebook, Twitter? Is that how they caught "The Queen of IRS Tax Fraud?"
Jason Dinesen, Same-Sex Marriage, Divorce and Taxes
Me: How much K-1 loss can I deduct? Start with your basis. Part of my 2013 filing season tips series. My exciting installment on partnership debt basis goes up later this morning.
Oh, but it's for our own good. IRS Claims It Can Read People's E-Mails Without Needing a Warrant (Joseph Henchman, Tax Policy Blog).
Jack Townsend, KPMG Publication on FBAR Filing Requirements for Corporations and Executives
Russ Fox, Bozo Tax Tip #2: Nevada Corporations
Kay Bell, Top 10 things you don't want to hear from your accountant. How about "I'm calling from Brazil, thanks for the cash!"
He'd have had trouble during tax season. FYI: The Guy Who Stabbed 14 People At a Texas College Wanted To Be an Accountant When He Grew Up (Going Concern)
Christopher Bergin, Why Transparency Is Like Porn (Tax.com) No, it's not about Lululemon.
News you can use. Make Your Own Bubble in 10 Easy Steps (Bryan Caplan)


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New to Typophile? Accounts are free, and easy to set up.
Hi Does anyone know what this font may be....
A great look'in script font ! I'd like to use it on my next project if i could identify it ?!
Thanks Again !
I'm guessing hand-lettered.
Thanks Again !!!!!!


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This photo was taken on our visit to Etosha National Park in Namibia near the Namutoni Camp. The Lilac-Breasted Roller (coracais caudatus) has magnificent colours but a dreadful call.
1351 viewings at 26 October 2014
Photo has been featured in the following groups:
"Indigenous to East & Southern Africa" ,
"Fauna, Flora, Landscapes & Architecture of South Africa",
"Birds, Birds, Birds"
"Animals of Africa"
"Afrikaans is my mother tongue"
"200 – 499 views"
"The Birds".
"Top 10" in Challenge in "Fauna, Flora, Landscapes and Architecture of South Africa", "Wildlife of Southern Africa", "Indiginous to Eastern & Southern Africa" & "Afrikaans" Groups
Photographed on a Lumix DMC-FZ7
Lovely to see my favourite bird again :-) Geat shot, I know how difficult they are to photograph!
It is an amazingly colourful bird – my wife will be loading a photo she took of a Lilac-breasted in Kruger NP when she gets a chance. Thank you for your comment.
Thank you very much for the complement. Love the groups work.
– Graeme Hyde
Another great capture of the Lilac-breasted Roller. And congratulations on the feature, Graeme and Margaret. Well deserved!
We greatly appreciate your comments, and also enjoy the work in the group. Thank you.
– Graeme Hyde
Beautiful shot, I head there in August and can't wait!
CONGRATULATIONS on your place in the top 10 of the group Indigenous to East & Southern Africa in the challenge Birds indigenous to Southern Africa
Thanks to the group for their support – it always good to get Olitto comments too. Would like to visit Africa again but unfortunately it won't be soon. Still processing shots and will continue to enjoy.


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Red Carpet
Last Night's Look: Love It or Leave It?Check out the head-turning outfits Katy Perry, Lea Michele and more stars were just spotted in! Then decide – fabulous or fashion fail!
Credit: Gary Gershoff/WireImage
Updated: Friday Nov 30, 2012 | 11:20 AM EST
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Image courtesy Stardate.org
Published June 25, 2010
A partial eclipse of the full moon tomorrow may get an added boost of red color from an Iceland volcano's ash, astronomers say.
Earth's shadow will be cast across half of the moon's surface over the course of the three-hour event, which begins 3:17 a.m. PDT on June 26. The lunar eclipse will be seen throughout most of the Americas, Australia, and Asia, astronomers say.
(Related: "Year's Biggest Full Moon, Mars Create Sky Show.")
A lunar eclipse occurs when the sun, Earth, and moon align. Unlike during total lunar eclipses, when the entire moon is engulfed in Earth's darkest shadow, the moon never completely dims during a partial lunar eclipse. (Take a quiz on moon mysteries and myths.)
"The dark part of the Earth's shadow will clip the moon, and you will see the part that lies within it will get much darker than the rest," said Geza Gyuk, astronomer at the Adler Planetarium in Chicago, Illinois.
"Skywatchers will see the moon decrease in brightness and maybe even a change [in] color."
The first stages of the eclipse will be visible across central and western sections of the North American continent at dawn. Eastern parts of the continent will not see the event. For those in the Pacific Basin and Asia, the eclipse will occur late Saturday night.
Skywatchers in the U.S. central and mountain time zones will catch most of the eclipse, while those within the Pacific time zone will see it in its entirety until the moon exits the shadow completely at 6:00 am PDT.
Volcano Ash May Add Flash to Sky Show
The deepest and most interesting part of the eclipse—when Earth's shadow will fall on 54 percent of the moon's disk—occurs at 4:38 am PDT.
At this point, when the moon is at its darkest, its face may turn red from ash thrown into the upper atmosphere by recent eruptions of the Iceland volcano Eyjafjallajokull. (See pictures of lava exploding from the Iceland volcano's ice cap.)
During the height of the eclipse, sunlight refracting off the scattered dust in Earth's atmosphere is projected onto the moon—the same effect that's in play during red sunsets.
"While I haven't heard of reports of particularly fantastic sunsets occurring because of the Icelandic volcano," Gyuk said, "it might be quite pretty if the ash in the air causes an extra reddening of the light reaching the moon."
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All together now: awww! Her favorite summertime fruit tumbles across this swimsuit. A green gingham bow looks cute on the back. Cotton-elastane. Hand wash. Made in USA.
She can be pretty in pastel with this gingham swimsuit. It has a timeless, girlish look, with bows on the top and layers of pastel ruffles on the bottoms. Even more bows adorn the ruffles. The straps are adjustable; swimsuit is fully lined. Nylon-elastane, polyester. Hand wash. Imported.
Our classic two-piece swimsuit is perfect for June, as well as July, August -- you get the idea. And your girl gets bright stripes, navy straps and, no doubt, a ton of compliments. Both pieces are lined. Nylon-elastane. Hand wash. Imported.
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She can be pretty in pastel with this sweetly striped swimsuit. It has a timeless, girlish look, with halter-style ties at the neck and ruffled layers on the striped bottoms. Fully lined. Nylon-Lycra®. Hand wash. Imported.
She'll make quite a colorful splash in this groovy tie-dyed tankini, which has crisscross straps that tie over the back keyhole opening. Nylon-elastane. Hand wash. Made in USA.
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White River Fly Shop® Fluorocarbon Tippet
- Maintains strength
- Ideal for heavy and toothy fish
- High sink rate
White River Fly Shop® Fluorocarbon Tippet 10 10
great I been useing this tuppet 16 lb test for king salmon it holds up amazing i caught 8 20 lb plus fish and i was pulling on it hard dident break once and not even a little bit of streatching amazing tippet and a great price. September 14, 2011
As good as the others good stuff at a fraction of the cost of the other brands July 28, 2011
Good product I use this tippet for bass fishing. Holds up very well including fish that take you down into the weeds and such. Its quite strong and ties good knots. I haven't used it in any of the lighter trout diameters. But the heavier stuff works very well for bass and bowfin too! November 3, 2010
Quality Product Used 10# for salmon fishing in New York Oct 12 in 50 degree weather and landed a 40"/23# and 38"/17#. The fight was on each fish for over 15 minutes each with one going under a log and having to net her from the other side. The line performed better than expected and thought I would break off the way I had to lean on them every so often. After running up and down the stream I had to drag the 40# of fish a mile up hill to the car but it was worth it. I now have purchased 8# and 6# test for Steelhead fishing and am anxious to see how it holds up in 30 degree weather as it will not stretch as much as it dit at 50 degrees. I usually switcm to straight Mono at 30 degrees to prevent line break. As fluorocarbon this is the best I have used. October 26, 2010
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What is the diameter of each size?
3 years, 3 months ago
- Upstream(read all my Q&A)
A:I think it is .009 in. for the 10#
1 year, 10 months ago
- Sumner,Iowa(read all my Q&A)
A:The diameter for each size is as follows:
2lbs: 0.14mm
4lbs: 0.17mm
6lbs: 0.21mm
8lbs: 0.25mm
10lbs: 0.28mm
12lbs: 0.29mm
16lbs: 0.35mm
2 years, 7 months ago
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1 of 1
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