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1 ISPRS Commission V Symposium 'Image Engineering and Vision Metrology' SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT T. Rabbani a, F. A. van den Heuvel b, G. Vosselman c a* Section of Optical and Laser Remote Sensing, TU Delft, The Netherlands - b CycloMedia Technology B.V., 4180 BB Waardenburg, The Netherlands - c Department of Earth Observation Science, ITC,Hengelosestraat 99, Enschede, the Netherlands - Working Group V/I,III KEY WORDS: Laser scanning, Point cloud segmentation, Reverse Engineering, Industrial Reconstruction ABSTRACT For automatic processing of point clouds their segmentation is one of the most important processes. The methods based on curvature and other higher level derivatives often lead to over segmentation, which later needs a lot of manual editing. We present a method for segmentation of point clouds using smoothness constraint, which finds smoothly connected areas in point clouds. It uses only local surface normals and point connectivity which can be enforced using either k-nearest or fixed distance neighbours. The presented method requires a small number of intuitive parameters, which provide a tradeoff between under- and over-segmentation. The application of the presented algorithm on industrial point clouds shows its effectiveness compared to curvature based approaches. 1 INTRODUCTION 1.1 Problem statement Segmentation is the process of labeling each measurement in a point cloud, so that the points belonging to the same surface or region are given the same label. For the problem of industrial reconstruction the point cloud is usually acquired using a laser scanner. Unlike structured light based instruments which provide 2 1 2D data, most of the laser scanners provide an unstructured point cloud. Even in the case of 2 1 2D range images, once two or more such images have been registered, the resulting data loses its D character and has to be represented as an unstructured 3D point cloud. Based on these observations the presented method would work only with unstructured point clouds, and other data representations can be easily converted to this format if required. 1.2 Previous work Different approaches for segmentation suggested in literature differ mainly in the method or criterion used to measure the similarity between a given set of points and hence for making the grouping decisions. Once such a similarity measure has been defined, segments can be obtained by grouping together the points whose similarity measure is within given thresholds and which are spatially connected. Most of the segmentation methods presented in the literature are for depth-maps as due to their 2 1 2D nature operations from traditional image processing can be directly applied. Three main varieties of range segmentation algorithms are discussed below: Edge-based segmentation Edge based segmentation algorithms have two main stages: edge detection which outlines the borders of different regions, followed by the grouping of the points inside the boundaries giving the final segments. Edges in a given depth map are defined by the points where changes in the local surface properties exceed a given threshold. The local surface properties mostly used are surface normals, gradients, principal curvatures, or higher order derivatives. Some of the typical variations on the edge-based segmentation techniques are reported by Bhanu et al. (1986); Sappa and Devy (2001); Wani and Arabnia (2003) Surface-based segmentation The surface based segmentation methods use local surface properties as a similarity measure and merge together the points which are spatially close and have similar surface properties. These methods are relatively less sensitive to the noise in the data, and usually perform better when compared to edge based methods. For surface based segmentation methods two approaches are possible: bottom-up and top-down. Bottom up approaches start from some seed-pixels and grow the segments from there based on the given similarity criterion. The selection of the seed points is important because the final segmentation results are dependent on it. Top-down methods start by assigning all the pixels to one group and fitting a single surface to it. Then as long as a chosen figure of merit for fitting is higher than a threshold they keep on subdividing the region Parvin and Medioni (1986); Xiang and Wang (2004). Most of the reported methods for range segmentation use a bottom-up strategy Scanline-based segmentation The third category of range segmentation methods is based on scan-line grouping. In the case of range images each row can be considered a scan-line, which can be treated independently of other scan-lines in the first stage. A scan-line grouping based segmentation method is presented by Jiang et al. 248
2 IAPRS Volume XXXVI, Part 5, Dresden September 2006 (1996) for the extraction of planar segments from the range image. It uses the fact that a scan line on any 3D plane makes a 3D line. It detects the line segments in the first stage, followed by the grouping of the adjacent lines with similar properties to form planar segments. Some typical variations on this method are presented by Natonek (1998) and Khalifa et al. (2003). Sithole and Vosselman (2003) have used profiles in different directions for the segmentation of air-borne laser scanner data. These profiles are generated by collecting points within a cylindrical volume around a given direction. A comparison of methods for finding planar segments in range images was done by Hoover et al. (1996). Based on his comparison framework, two methods for segmentation of range images into curved regions were compared by Powell et al. (1998). The first segmentation method of Besl and Jain (1988) (hereafter named BJ method) has two stages. The first stage of coarse segmentation is based on estimating the mean and Gaussian curvature for each point and using their signs for classification into 8 different surface types. This rough segmentation is refined by the second step of region growing, which is based on fitting bivariate polynomial surfaces. The comparison found the BJ method to result in severe over-segmentation even on very simple scenes with low noise. The major reason for this failure was the error in the estimation of principal curvatures from the noisy range data. The BJ segmentation method has 38 different parameters, 10 of which were iteratively optimized to get the best possible results. The speed of this method was very slow, and even on range images which do not have a high cost for searching the neighborhood points, it took more than 6 hours. The second segmentation method used for the comparison was by Jiang et al. (1996) (hereafter JBM method). This method also consists of two stages. In the first stage the scan lines of the range image are segmented into a set of curves by using a splitting method. In the second stage these edges are grouped together to make surfaces. This method has 10 parameters and the comparison found it to perform much better than the BJ method, both in terms of time (30 seconds compared to 6 hours of the BJ method) and the quality of results. Looking at the comparison, the JBM method would be a good choice for the segmentation of industrial scenes, but there are some serious limitations. First of all, the method is based on the grouping of scan lines which do not exist for unstructured point clouds. For airborne laser scanner data similar scan lines created by collecting and joining points in a tubular volume have been used by Sithole and Vosselman (2003) for segmentation, but there the data is 2 1 2D. Extensions of this idea for 3D point clouds would require choosing a few preferred directions for scan-lines, making the results of segmentation orientation dependent. 1.3 Problems with existing methods We observed the following problems with existing segmentation techniques as applied to our problem of processing industrial point clouds: Point cloud Normals + residual calculation Region growing Segmentation Figure 1: Flowchart of the segmentation algorithm. 1. Many approaches are tailored only for planar surfaces, which are too limiting for industrial scenes. 2. Although principal curvature based approaches can handle curved objects, the unreliable estimation of the curvature from noisy point clouds leads to high rates of over-segmentation. Furthermore, the objects like torus and sphere are always over-segmented because they are not one of the 8 different surface classes identifiable based on the signs of the principal curvatures. The sensitivity of curvature estimates from range data has been analyzed in Trucco and Fisher (1995), where it is suggested that at least for planar segmentation of range data principal curvatures should not be used. 3. Many segmentation methods have a large number of parameters, whose meaning and effects on final segmentation are not always clear. Most of the comparisons used separate iterative optimization methods to find the best set of parameters. 4. Most of the methods are tailored for application to 2 1 2D range images. Sometimes their extension to 3D unstructured point clouds is quite simple like replacement of 8-neighbors with k-nearest neighbors. But in other cases, like defining scan lines for 3D point clouds, there is no straight forward extension, and most approaches introduce new limitations. 5. There are some model-based approaches to segmentation which segment and recognize surface types at the same time Marshall et al. (2001). These approaches do not fit in our pipeline of separate segmentation and object recognition through Hough transform and thus cannot be applied to our problem. 1.4 Objectives and motivation Noting these weaknesses we decided to develop a simple segmentation strategy that follows the following guidelines: 249
3 ISPRS Commission V Symposium 'Image Engineering and Vision Metrology' 1. We will assume a raw unstructured 3D point cloud as the input to the algorithm. Although the assumptions about structure of data (range image, TIN etc) can make the job of neighbor search faster, they at the same time make the algorithm less general purpose. 2. We will use only surface normals as they can be reliably estimated even in the presence of noise (provided the neighborhood is sufficiently big to average out effects of noise). 3. The algorithm should allow the user to choose between the degree of over and under-segmentation by changing a few parameters. In our modeling pipeline the segmentation is followed by the stage of object recognition, which processes each segment separately. That stage can detect multiple objects in one segment (under segmentation), but if an object is split into multiple segments (over-segmentation), its detection and correction would be more difficult. 4. The algorithm should have a low time and space complexity. Furthermore, we aim to use a minimum number of parameters having a physically intuitive meaning. 2 SEGMENTATION ALGORITHM As stated earlier the basic purpose of the presented segmentation algorithm is to subdivide the input point cloud into meaningful subsets, while avoiding both under and over-segmentation with a preference for under-segmentation. The segmentation method has two steps, Normal estimation and region growing. The details of these steps are given in algorithm 1 and further explained below. See also Figure Normal estimation The normal for each point is estimated by fitting a plane to some neighboring points (Figure 2(c)). This neighborhood can be specified in two different methods. K nearest neighbors (KNN) In this method for a given point we select the k points from the point cloud having the minimum distance. The distance metric used can be Euclidean, Manhattan, or any other distance metric obeying the triangle inequality. As the number of points k is fixed, the method adapts the area of interest (AOI) according to the point density. Assuming that the point density is an indicator of the measurement noise (which is usually the case as for a given laser scanner the density falls down inversely with the distance and the angle of incidence), this results in overall better estimation of the normals as a bigger AOI is used in the areas of lower point density (Figure 2(a) and 2(b)). Moreover, this method always uses the given number of points and avoids degenerate cases (e.g. a point having no neighbors). Algorithm 1 Segment a given point cloud using smoothness constraint Inputs: Point cloud = {P}, point normals {N}, residuals {r}, neighbor finding function Ω(.), residual threshold r th, angle threshold θ th Initialize Region List {R} Φ, Available points list {A} {1 P count } while {A} is not empty do Current region {R c }, Current seeds{s c } Φ Point with minimum residual in {A} P min insert P min {S c }&{R c } remove P min {A} for i = 0 to size({s c }) do Find nearest neighbors of current seed point {B c } Ω(S c {i}) for j = 0 to size({b c }) do Current neighbor point P j B c {j} if {A} contains P j and cos 1 ( N{S c {i}}, N{P j } ) < θ th then {R c } {A} if r{p j } < r th then insert P j {S c } end if end if end for end for Add current region to global segment list {R c } insert insert P j remove P j {R} end while Sort {R c } according to the size of the region. Return {R c } Search for KNN can be optimized using different space partitioning strategies like k-d trees Arya et al. (1998); Goodman and O Rourke (1997). Fixed distance neighbors (FDN) This method uses a given fixed AOI, and for each query point, selects all the points within this area. The distance metric used is usually Euclidean but can be changed similar to KNN. For FDN search the number of points changes according to the density of the point cloud. As the number of points is directly proportional to the density of the points in the neighborhood, this method does not have the adaptive behavior of KNN. Compared to KNN, here the number of points is less in the areas of low density (high noise) and as a result the estimation of the normals is on the whole contains more noise. This method is more suitable if the density of the points does not change a lot throughout the data. Similar to KNN there are optimized methods for doing FDN searching Goodman and O Rourke (1997); Willard (1985) One of the above described methods for neighborhood search are also used during the stage of region growing (Section 2.2). 250
4 IAPRS Volume XXXVI, Part 5, Dresden September 2006 (a) (b) (c) Mean squared error (mm 2 ) Residual of plane fitting vs curvature of cylinder Radius of cylinder (mm) (d) 1/r 2 residual (with noise) residual (no noise) Figure 2: (a-b)adaptive change in selection area for k- neighbors for different point densities (a) hight density, 50 KNN (b) low density, 50 KNN (c) Normal estimation by fitting a plane to the points in the neighborhood (d)residual of the plane fitting gives an approximation to the local surface curvature Plane fitting To fit any surface to a set of given points, in a least squares sense, we want to find that set of parameters that minimizes the sum of squares of the orthogonal distances of the points from the estimated surface. In general this is a nonlinear least squares problem, but as shown below, in case of planes this can be reduced to an eigenvalue problem. Local connectivity The points in a segment should be locally connected. This constraint would be enforced The plane can be parameterized with its normal n = ( ) n x n y n z, by using only the neighboring points (through KNN and its distance from the origin ρ. This is also called Hesse or FDN) during region growing. normal form of the plane. The distance of any given point p = ( ) p x p y p z from the plane is given by n p ρ Surface smoothness The points in a segment should locally provided n n = 1. This is a constrained problem and can make a smooth surface, whose normals do not be solved using Lagrange multipliers. The solution results in an eigen-value problem for more discussion see Hoppe et al. (1992). vary too much from each other. This constraint Residual as approximate curvature The residual in the plane fitting can arise either from noise or from nonconformity of the neighborhood of a point to the planar model. The second case hints that the residual can be used to find areas of high curvature. Of course we do not get the principal curvatures and their direction from this approximation, but still the edges and the areas of high surface normal variation can be detected based on high residual values of plane fitting. To check the relationship between curvature and residual of plane fitting we generated data consisting of cylinders of different radii. The normals for these cylinders were estimated by fitting planes to 40 k-nearest neighbors, and then plotted against 1 r (Figure 2(d)). There we see that for 2 the case of no noise the residuals are quite similar to 1 r ex- 2 (a) (b) (c) Figure 3: Comparison of segmentation for a toroidal surface (a) point cloud (b) segmentation using presented approach (c)curvature based segmentation cept a difference of scale. In the presence of noise the trend remains the same but in addition to a scale factor there is also a shift related to the amount of noise. This supports our idea of using residuals of plane fitting as indicator of areas of high curvature. The regions of high curvature are detected by introduction of r th in Algorithm 1. In Figure 4(a), 4(e), 4(g) we show the residual of plane fitting as color. As expected the areas on edges and points of high curvature have higher residuals. 2.2 Region growing The next step in the segmentation process is region growing. This stage uses the point normals and their residuals, in accordance with user specified parameters to group points belonging to the smooth surfaces. This grouping tries to avoid over-segmentation at the cost of under-segmentation. This stage is based on the enforcement of these two constraints. would be enforced by having a threshold (θ th ) on the angles between the current seed point and the points added to the region. Additionally, a threshold on residual values r th makes sure that smooth areas are broken on the edges. The process of region growing proceeds in the following steps. 1. Specify a residual threshold r th. Alternatively, calculate this threshold automatically using a specified percentile of the sorted residuals (95+% can be a representative number). 2. Define a smoothness threshold in terms of the angle between the normals of the current seed and its neighbors. If the smoothness angle threshold is expressed in radians it can be enforced through dot product as 251
5 ISPRS Commission V Symposium 'Image Engineering and Vision Metrology' follows knp ns k > cos(θth ). As the direction of normal vector has a 180o ambiguity we have to take the absolute value of the dot product. 3. If all the points have been already segmented go to step 7. Otherwise select the point with the minimum residual as the current seed. (a) (b) (c) (d) (e) (f) (g) (h) 4. Select the neighboring points of the current seed. Use KNN or FDN with the specified parameters for this purpose. The points that satisfy condition 2 add them to current region. The points whose residuals are less than rth add them to the list of potential seed points. 5. If the potential seed point list is not empty, set the current seed to the next available seed, and go to step Add the current region to the segmentation and go to step Return the segmentation result Region growing tries to group points belonging to smooth surface patches together. Although, we want to avoid oversegmentation but still we do not want the whole point cloud coming out as one segment. The inclusion of residual threshold (rth ) makes sure, that we can strike a balance between the above mentioned extremes. As rth 0 we go towards more segments with the extreme case being each point belonging to one segment. Similarly as rth we have less segments and the extreme case of the whole point belonging to one segment. We can differentiate between the following cases which may lead to the start of a new segment during the process of region growing. Figure 4: Results of segmentation (a) residuals of data set 1 (b) segmentation 1 (c) data set 2 (d) segmentation 2 (e) residuals of data set 3 (f) segmentation 3 (g) residuals 4 (h)segmentation 4 Step edge A step edge is defined by two planes which have the same orientation but different offset from the origin. The segmentation algorithm leads to their separation provided the offset between planes is greater than the AOI for the neighborhood search. For KNN this depends both on the value k and point density, while for FDN it is equal to the fixed distance specified by the user. 3 RESULTS The presented algorithm was applied to four sets of point clouds acquired from four industrial sites. The results are shown in Figure 4. For these results the θth was set to 15o and 30 nearest neighbors were used (k = 30); rth was automatically calculated by the 98th percentile of the plane fitting residuals. In the results we see both goals of grouping smooth areas and avoiding over-segmentation have been successfully achieved. There are areas where large under-segmentation occurred, but it can be explained based on the values the parameters θth and rth. Intersection edge A intersection edge is defined by the intersection of two surfaces, whose surface normal at the intersection line make an angle greater than the given threshold (cos 1 n1 n2 > θcurv ). An example of such an edge would be the edge coming from the intersection of the two planar sides of a box. The surfaces on both side of the edge would be segmented because of the smoothness constraint (θth ). Additionally the points on the edge would be marked unsuitable for inclusion in the next generation seeds, as they will have residuals greater than rth. For example in Figure 4(d) a whole U-section of pipe is segmented as one region, because it is smoothly connected. Similarly in Figure 4(h) the L-junctions of pipes have been grouped as one region rather than being split into two pipes and one curve. As for the presented results the residual threshold rth was calculated using the percentile method, it leads to data dependent values. In Figure 5 we took two segments from the results of Figure 4(f) and segmented them again. As now the data is more limited the threshold rth is lower and more strict. This leads to segmentation having more regions along with some over-segmentation. The effectiveness of the method to detect smoothly varying surface patches is shown best in Figure 3. While the method of curvature based segmentation leads to high oversegmentation (Figure 3(c)), our method divides the data in only one segment (Figure 3(b). 252
6 IAPRS Volume XXXVI, Part 5, Dresden September 2006 D. W., Fitzgibbon, A. W. and Fisher, R. B., An experimental comparison of range image segmentation algorithms. PAMI 18(7), pp Hoppe, H., DeRose, T., Duchamp, T., McDonald, J. and Stuetzle, W., Surface reconstruction from unorganized points. In: Proceedings of ACM SIGGRAPH 92, ACM Press, New York, NY, USA, pp Jiang, X. Y., Bunke, H. and Meier, U., Fast range image segmentation using high-level segmentation primitives. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV 96), IEEE Computer Society, Washington, DC, USA, p. 83. Figure 5: Effects of changing r th on the final segmentation. Two segments are re-segmented but with lower r th resulting in more segments Thus by choosing a proper value for r th the required balance between under and over-segmentation can be achieved. 4 CONCLUSIONS A segmentation algorithm for dividing a given unstructured 3D point cloud into a set of smooth surface patches has been presented. The algorithm uses only surface normals as a measure of local geometry, which are estimated by fitting a plane to the neighborhood of the point. As fitting of higher order surfaces to noisy point clouds is quite error prone, we approximate the local curvature by the residual of plane fitting. The method has two parameters (θ th and r th ), which have intuitively clear meaning. Both k nearest neighbor and fixed distance neighbor variations of the algorithm are possible. The results on point clouds acquired from industrial sites were presented that show the effectiveness of the method and its ability to choose between under-segmentation and over-segmentation. References Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R. and Wu, A. Y., An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of ACM 45(6), pp Besl, P. J. and Jain, R. C., Segmentation through variable-order surface fitting. PAMI 10(2), pp Bhanu, B., Lee, S., Ho, C. C. and Henderson, T., Range data processing: representation of surfaces by edges. In: Proceedings of the Eighth International Conference on Pattern Recognition, pp Goodman, J. E. and O Rourke, J., Handbook of discrete and computational geometry. CRC Press, Inc., Boca Raton, FL, USA. Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P. J., Bunke, H., Goldgof, D. B., Bowyer, K. K., Eggert, Khalifa, I., Moussa, M. and Kamel, M., Range image segmentation using local approximation of scan lines with application to cad model acquisition. Machine Vision Applications 13(5-6), pp Marshall, A. D., Lukács, G. and Martin, R. R., Robust segmentation of primitives from range data in the presence of geometric degeneracy. PAMI 23(3), pp Natonek, E., Fast range image segmentation for servicing robots. In: The IEEE International Conference on Robotics and. Automation, pp Parvin, B. and Medioni, G., Segmentation of range images into planar surfaces by split and merge. In: CVPR 86, pp Powell, M. W., Bowyer, K. W., Jiang, X. and Bunke, H., Comparing curved-surface range image segmenters. In: Proceedings of the Sixth ICCV, IEEE Computer Society, Washington, DC, USA, p Sappa, A. D. and Devy, M., Fast range image segmentation by an edge detection strategy. In: Third International Conference on 3-D Digital Imaging and Modeling, pp Sithole, G. and Vosselman, G., Automatic structure detection in a point-cloud of an urban landscape. In: 2nd Joint Workshop on Remote Sensing and Data Fusion over Urban Aereas (Urban 2003). Trucco, E. and Fisher, R. B., Experiments in curvature-based segmentation of range data. PAMI 17(2), pp Wani, M. A. and Arabnia, H. R., Parallel edgeregion-based segmentation algorithm targeted at reconfigurable multiring network. Journal of Supercomputing 25(1), pp Willard, D. E., New data structures for orthogonal range queries. SIAM Journal of Computing 14(1), pp Xiang, R. and Wang, R., Range image segmentation based on split-merge clustering. In: 17th ICPR, pp
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text:
1 Restaurants in Okinawa
Okinawa, Japan is famous for its culinary expertise. We make booking reservations everywhere from traditional restaurants to unique cafes simple for you so you can leisurely enjoy a quality meal. Peruse our favorite spots in Okinawa, Japan and make a reservation at what will soon be your new favorite restaurant or cafe!
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subset: commoncrawl


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Cyber Warfare
In the last five years, the topic of cyber warfare has received much attention due to several so-called "cyber incidents" which have been qualified by many as State-sponsored cyber attacks. This book identifies rules and limits of cross-border computer network operations for which States bear the international responsibility during both peace and war. It consequently addresses questions on jus ad bellum and jus in bello, in addition to State responsibility. By reference to treaty and customary international law, actual case studies (Estonia, Georgia, and Stuxnet), and the Tallinn Manual, the book illustrates the applicability of current international law and argues for an obligation on the State to prevent malicious operations emanating from networks within their jurisdiction. This book is written for academics in public international law and practitioners from the military and other public security sectors. (Series: International Law - Vol. 14) [Subject: Public International Law, Treaty Law, Computer Technology Law, Military Studies, Security Studies]
Publication Date: 5/9/2014
Format: Cloth


text:
New York State Muzzle Loaders Association
The Association
Who we are!
By Laws
GT Info.
NYSMLA Spring Shoot
NYSMLA Fall Shoot
NYSMLA Clubs Map
Events & Club Shoots
2017 Shoot Dates
St. Lawrence Longrifles
Sharon Mtn Muzzleloaders
Little Beard Club
Land of the Seneca's
Marble City Muzzleloaders
Shoot Results
Moose River
Snap Shots
Affiliated Clubs
Affiliate Club News
Honeywell Sportsmen's Day
In Memory - Last Relay
50 Club
New York State Muzzleloaders Association, Inc.
Dan Fullington-President Ron Smith-1st Vice President Doug Rifanburg-2nd Vice President Dave Lis-Treasurer
Nancy Currier-Recording Sec./Membership
Directors: Region 1 & 2-Open Vince Bernstein-Region 3 Region 4 & 5 - Open
Jim Currier-Region 6 Jim Van Giesen-Region 7 Bob Wood-Region 8 & 9
Eric Bratt-Group Tight'ner Editor & Conservation Council Representative
Jim Currier-Shoot Director Mary Smith-NYSMLA Merchandise
Website: Vickie Fullington - firstname.lastname@example.com or email@example.com


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text:
Although the warmer months are popular for training, scorching summer temperatures can quickly mean serious trouble for athletes.
The latest round of severe thunderstorms packing heavy rain and high winds will roll across the Midwest and part of the Plains through Thursday.
The last place you'd expect to land during a relaxing summer getaway is in the emergency room, but it happens more often than we think.
As storms repeat over parts of the southern United States, the risk of flooding and travel disruptions will increase at the local level this week.


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text:
The Sodium Iodide Clock
Impact on Society
Relevant Scientific detail: Ingredients
Sodium iodide is an iodide salt that is formed into an injectable medicine.
Sodium sulfite: Soluble in water. Reacts with hot water, steam or acids to produce corrosive material
Citric acid is a common food additive and is found in everything from sodas to candies
citric acid
sodium iodate
Let's Get Started!
Hans Heinrich Landolt
The Experiment Demonstrates:
This experiment demonstrates the idea of kinetics, the branch of chemistry or biochemistry concerned with measuring and studying the rates of reactions.
What is a sodium iodide clock?
Hans Heinrich Landolt
Discovered in 1886
Known for his humor, friendliness, puntuality, and cigar
The Sodium Iodide Clock shows how chemicals react to each other. This will benifit us in future medicine and scientific studies.
A chemical reaction
4 chemicals
sodium sulfide
sodium iodate
citric acid
a starch solution
Creates 2 solutions
When mixed, they create a solution that....
...changes color over time.
sodium sulfite
It is called a clock reaction because the amount of time it takes to react can be changed by the amount of each ingredient put in.
Fun Fact: Although most people believe that only sugar can raise your blood sugar levels, starch can produce the same results.
...Why Call It A Clock?
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The Sodium Iodine Clock
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|Very professional job on complete home windows installation. Enjoyed working with Dan Cook, Dale Snyder, Nick & his crew. Everything cleaned up nicely after completion of the work. Definietly will recommend ODW to others. (Actual Survey Excerpt)
|The gentlemen who installed the storm doors did an excellent job. He was neat, polite, and the doors look great and work beautifully. I also am very pleased with the recommendations made by the salesmen (Dan). The door looks great. (Actual Survey Excerpt)
|The installation was quick & they did an excellent job! They even fixed my siding that was falling under the large window they installed & showed me how everything worked before they left. I will continue to recommend ODW to my family & friends. (Actual Survey Excerpt)
|We chose ODW b/c they were up front about pricing & so easy to work with! I felt good knowing I was being taken care of & at a fair price! Very happy. Will be using for windows too! (Actual Survey Excerpt)
|The guys were in and out the same day. The price was lower than what it cost me for an entry door that I purchased from Lowes. (Actual Survey Excerpt)
|Paul was a terrific salesperson. Informative, but not pushy. I appreciate that. The crew that installed the windows and doors were courteous, fast, and cleaned up after themselves. Great group! (Actual Survey Excerpt)
|Omaha Door & Window has done a fantastic job replacing exterior and storm doors at our residence. From the salesmen to the installers everyone has been nice, polite, and professional. It was our pleasure to do business with this fine company. They can expect our repeat business. (Actual Survey Excerpt)
|I am very pleased to have had (Aaron) to do my work. Such a gentleman, very professional, kept moving all the time. I hope you pay him well! In today's world you don't find many like Aaron! (Actual Survey Excerpt)
|We have lived in Omaha & Bellevue our entire lives. Your company has a great reputation and never have we heard anything bad from people we know that have used you! The windows were installed in a timely manner and your service people were very nice. (Actual Survey Excerpt)
Garage Door Testimonials
|Excellent service from start to finish! Couldn't be more pleased with customer service, installer, product & very fair price! Our family considers Omaha Door & Window our only business for repairs & remodeling. (Actual Survey Excerpt)
|Everything was done in very professional timely manner. The installer explained everything he was going to do & completely cleaned up after the installation. Very good, neat job.(Actual Survey Excerpt)
|Your installer showed up early, was very courteous and did a wonderful job. He went the extra mile by getting lights on the door opener and demonstrated how the features operated. (Actual Survey Excerpt)
|Very professional arrived on time, checked openings before starting, all workmanship was expertly done, the job was clean when he left. (Actual Survey Excerpt)


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Data from: The wheat curl mite Aceria tosichella (Acari: Eriophyoidea) is a complex of cryptic lineages with divergent host ranges: evidence from molecular and plant bioassay data
Date Published: November 21, 2012
DOI: http://dx.doi.org/10.5061/dryad.sq63c
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|Host acceptance for mtDNA lineages
|25 times
|Results of experiments showing population growth for different WCM mtDNA lineages when transferred from source host plants to test plants. Column headings: host.org - original host, host.nov - novel host, feme - no. of females settled, progeny - no. of progeny found after 14 days, lineage - mtDNA lineage
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Hands up if you love a desk tour blog post!
*Both my hands up, waving them about like I just don't care*
I'm a nosey parker and love seeing how people choose to organise their lives. As beautiful as these desk tours are however, most of them just don't apply to me or provide me with any actionable inspiration. I don't work from home and when I do blog, it's usually from my bed. The only desk I see on a regular basis is the one at my 9-5 job, which I know doesn't sound super glamorous but I'm lucky enough to work somewhere where I don't have to hide the quirky and can bring in super cute stationery and no one would blink twice or treat me like a little girl.
I've been at my current work for over a year and now have a system of organisation that works for me, and keeps me on top of the fast-passed social media and copywriting environment. I'm not going to share a picture of my actual office but I've included links below and the photo above is stationery I actually own.
Desk Planner
Magazine File
Since my job is primarily a copywriting job, managers usually pass me magazines and brochures that are incredible sources of inspiration. The industry magazines are eventually moved to an official filling cabinet but I like to keep a copy of all our recent holiday brochures (and those of our suppliers) beside me for quick reference. And this woodland creature themed magazine file is the cutest, no?
Titled Sticky Notes
These cute owl sticky notes (that I don't think you can buy now) were one of the first things I bought when I started my current job. I still have them and they live propped up against my aforementioned magazine file. I don't use them that often as - truth be told - they don't stick very well, but they do come in handy for quick reminders that need that need to be kept within my eye-line.
List of phone numbers
Every now and then a Word document is sent round with a list of phone numbers and we're asked to print it off for our own reference. I'm a complete rebel and make my own with phone number that I know I need close at hand. I keep it sellotaped to the edge of my desk top monitor.
Stapler, paper clips, poly pockets, and highlighters!
What's your must-have stationery, either at work or for your home office?


text:
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Happy Birthday To Me!!!
I just want give God all the glory for allowing to see another year! just came back from the Pocono Resort and I had a blast. A huge thank you to my wonderful husband for an amazing birthday weekend! I love you so so much. Happy Birthday to all the Mom that was born in the month of June! May God continue to bless you all with many more bless years ahead. Stay bless all
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About Loudenie Jason
Current: Spring Valley, New York
Birth: June 20
On Moms.com since: Nov 23, 2013
I'm a christian who love my Lord and savior, I'm a wife to the most amazing handsome man of God and I have the best job in the world witch is being a mother to my amazing, gorgeous, smart little lady that I love greatly.


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How to Tell If You Should Break Up
5 Signs You Don't Really Like Him
Knowing whether or not you like a guy should be easy enough, but all too often I hear friends arguing a case, mostly to themselves, for why they keep seeing someone. There may be something to say for giving a guy a chance, but the longer you stay, the harder it is to leave. Here are five signs to save you from the lukewarm romance of Mr. Good Enough.
- "He's really nice": If you find yourself rationalizing that he's a nice guy, a good guy who you should give a chance, then be friends. Maybe he'll grow on you?
- He's perfect on paper: Throw the paper away, and accept that he who is perfect on paper is almost never perfect in real life — and vice versa!
- You forget to respond: Whether it's his email, text, IM, or call, if something else is so interesting that you forget about him, then you might as well forget him now.
- You like his friends better than him: If you'd rather hang out with him and his friends than with him alone (or you're using them to gain access to something), then you might be in it for the wrong reasons. Or the right!
- He always calls you: Unless you're playing a game, the making and breaking of plans should be fairly even.
Do you have any signs to add?
Image Source: Pressman Studio


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NeuroQore, Inc., is set to commercialize a new patented monophasic repetitive transcranial magnetic stimulation (rTMS) system with a potentially improved remission rate. The first unit of the NeuroQore rTMS system was installed at the Royal Ottawa Mental Health Centre in July 2015 and successfully passed tolerability testing on healthy volunteers in a pilot project. The company is now in the midst of recruiting patients for the next round of testing. Preliminary clinical data is promising and has prompted installation of more units to recruit more patients faster.
Major depressive disorder affects approximately 4.7% world population annually and 1 in 3 do not recover with medications or therapy. For treatment-resistant depression (TRD), electroconvulsive therapy (ECT) is effective (60-70% remission), but hampered by the need for anesthesia, significant cognitive side effects, and high stigma. Less than 1% of TRD population receive ECT annually.
Repetitive transcranial magnetic stimulation (rTMS) is an emerging therapeutic brain stimulation technique that does not require anesthesia, has no cognitive side effects, and is widely accepted by patients. rTMS is currently administered by a technician or nurse in an outpatient clinical setting. The potency and efficacy of conventional biphasic rTMS (45-55% response, 30-35% remission) falls between medications and ECT, but rTMS offers the advantages of high tolerability and accessibility.
NeuroQore monophasic rTMS system with an improved remission rate has potential to be a disruptive innovation that results in improved health care worldwide. NeuroQore is looking to effectively integrate into existing health-care facilities and coordinated with ongoing treatments as a complement to medications and therapy.
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San Francisco: 415 590 3003
Newport Beach: 949 397 1333
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Thank you for visiting NeuroQore.com
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"I remember, I remember, / The house where I was born, / The little window where the sun / Came peeping in at morn."
Related quotes
"For Heaven's sake discard the monstrous wig which makes the English judges look like rats peeping through bunches of oakum"
"Upon the Nipples of Julia's Breast: Have ye beheld (with much delight) A red rose peeping through a white? Or else a cherry (double graced) Within a lily? Center placed? Or ever marked the pretty beam A strawberry shows half drowned in cream?"
"Putting these criminals behind bars is the only way to stop these Peeping Tom marauders who not only violate our privacy but also our safety."


text:
Did you know before your BFP?(35 Posts)
MoreBoober Mon 04-Mar-13 23:45:46
gilly86 Tue 05-Mar-13 00:46:21
catlady1 Tue 05-Mar-13 00:59:09
gilly86 Tue 05-Mar-13 01:12:08
catlady1 Tue 05-Mar-13 01:20:01
roofio87 Tue 05-Mar-13 11:12:34
pegster Tue 05-Mar-13 11:46:21
EuroShaggleton Tue 05-Mar-13 11:49:27
fromwesttoeast Tue 05-Mar-13 11:56:29
kalidasa Tue 05-Mar-13 12:10:14
MinkyWinky Tue 05-Mar-13 12:26:29
MoreBoober Tue 05-Mar-13 23:15:38
twinklestar2 Wed 06-Mar-13 00:07:02
crazycatlady82 Wed 06-Mar-13 01:12:34
crazyhead Wed 06-Mar-13 07:06:41
EuroShaggleton Wed 06-Mar-13 07:12:30
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MoreBoober Wed 06-Mar-13 13:12:12
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text:
Miss Susie's Steamboat
Miss Susie had a steamboat
The steamboat had a bell - ding ding (action of tugging bell cord)
Miss Susie went to heaven
The steamboat went to... (second person jumps in)
Hello operator, please give me number 9
And if you disconnect me
I'll chop off your... (third person jumps in or 1st person jumps out)
Behind the old piano
There sat a piece of glass
Miss Susie slipped upon it
And broke her big fat... (second person jumps out and/or 1st person jumps in)
Ask me no more questions
Please tell me no more lies
The boys are in the bathroom
Zipping up their... (third person jumps out and/or 2nd person jumps in)
Flies are in the meadow
The bees are in the park
Miss Susie and her boyfriend
Are kissing in the D-A-R-K (all jumpers back in)
Then it begins again with the jumpers rotating in and out miming actions to the words. Each time it begins again the ropes go faster as does the song.
If you are the copyright holder of this poem and it was submitted by one of our users without your consent, please contact us here and we will be happy to remove it.


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Question about HP Computers & Internet
Posted by andreakusy on
Why did you update the BIOS? (Just wondering)
It depends... some BIOS manufactures allow it and some doesn't. What is the manufacturer's name of the BIOS?
Open your case look on the motherboard find what looks like a big watch battery carefully remove the battery leave it out for about a minute and put it back in. This will clear CMOS and restore BIOS to factory settings. Hope this helps
Posted on Feb 07, 2009
SOURCE: I have freestanding Series 8 dishwasher. Lately during the filling cycle water hammer is occurring. How can this be resolved
a 6ya expert can help you resolve that issue over the phone in a minute or two.
best thing about this new service is that you are never placed on hold and get to talk to real repairmen in the US.
the service is completely free and covers almost anything you can think of (from cars to computers, handyman, and even drones).
click here to download the app (for users in the US for now) and get all the help you need.
Posted on Jan 02, 2017
Tips for a great answer:
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Pretty much like all the other YHA'S - basic but they do the job. Has really good facilities and even a swimming pool. Location is very good. Staff were quite friendly and let us check out slightly late when my boyfriend hurt his back. Wifi...More
Getting you more information on this room
Getting you more information on this room
"Ground rooms don't have as many stairs, but upper rooms have balconies."
"I will recommend you to stay on the top floor to have a better view of the yard from your own balcony, if you..."


text:
The Camel Yamaha Team has today in Milan, Italy, officially launched the new M1 with which they will try to defend the championship earned in the hands of Valentino Rossi. Along with the 2005 title holder, Colin Edwards, they hope to notch the...
The Camel Yamaha Team has today in Milan, Italy, officially launched the new M1 with which they will try to defend the championship earned in the hands of Valentino Rossi. Along with the 2005 title holder, Colin Edwards, they hope to notch the 2006 FIM Road Racing World Championship Grand Prix (MotoGP) title.
The bikes were uncovered by the Festi group of acrobats, who descended from the roof to reveal the small changes in decoration from the M1 that was used during the pre-season tests.
The most important difference is in the numbers that now are surrounded by a white plate on the front of each bike, while other slight graphical changes are also visible on the sides.
"This new bike is really beautiful!", exclaimed Rossi, who this season aims to win a sixth consecutive World Championship title. "It's exciting to be starting another season with Yamaha; these last two seasons together with them have probably been the best of my career. It's also nice to be with Colin again, we're very close in our team."
About the new Yamaha M1 and the challenge to come, the Italian commented: "The M1 is already very good and we've been working very closely to develop it with Yamaha, which has done a very good job. This year I have many new young adversaries who will be challenging me, and I think there is going to be some spectacular racing!"
Rossi`s team mate, Edwards, gave his impressions of the new machine in front of 300 guests and the international media. "I really like the new bike design, it looks really great. I had the retro-style number five like this on the bike at Laguna Seca last year and I've used it again for this year."
The ceremony was held exactly one month before the first race of the Continental Circus. The riders will take the green flag on March 26th on the Jerez de la Frontera circuit in Spain.
Masao Furusawa, Executive Officer for Engineering Operations at Yamaha Motor Company, took the opportunity to comment on the keys of the project for this year. "Even though the basic M1 concept for 2006 is the same. This is a more sophisticated bike, especially with the increasing RPM of the engine. It is very important for the progression of the bike to keep Valentino and Colin together, as they are not just riders for us, but a key part of the development of the M1."
Davide Brivio, the Team Director for the Yamaha Factory Team for the fifth year running, stated his point of view about the new look of the bike. "I think that everybody will really like the new livery, and especially the riders` numbers on the white squares. We needed to come up with something original for the livery without moving away from the Camel yellow, which we all really like, and now I think this final livery is going to look really spectacular on track", he said.
Fans and the media will be able to see the new M1 of Camel Yamaha team and all of the 2006 field of this week in the Official tests at Catalunya, Spain. The MotoGP machines will get get on-track action for three days from Friday through Sunday in the event that is known as Grand Prix Cero.


text: This article is a stub. You can help Camerapedia.org by expanding it. 1975, M42 mount 35mm SLR. Retrieved from "http://camerapedia.wikia.com/wiki/Vivitar_400/SL?oldid=88692" Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you've made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. Categories: Stub Japanese 35mm SLR 42mm screw mount Vivitar V Add category Cancel Save


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City College of New York vs NJIT Wrap - NJIT Wins 99-60
NJIT blew out City College of New York on Monday, running up the score in a 99-60 win at Fleisher Center.
Jake Duncan had 20 points for NJIT and Terrence Smith chipped in with 12 points. Winfield Willis had 6 assists for them, while Odera Nweke was the team's top rebounder, grabbing 15 boards.
City College of New York got 11 points from Anthony Lewis in a losing cause and another 8 points from Nathan Lopez. Nathan Lopez contributed 4 assists for them, while Anthony Lewis had 8 rebounds.
NJIT hit the break leading 51-29 before taking care of business in the second half.
NJIT had a 53-24 advantage in the rebounding department as they won the battle in the paint.


text: Redd White Image Gallery Sprite Gallery Standard spritesEdit NormalNormal/talkingConfidentConfident/talkingShruggingShowing offSulkingSulking/talkingSurprisedShockedShocked/talkingSweating Sweating/talkingTwitchingBreakdownAdd a photo to this galleryFull body spritesEdit At the witness standAdd a photo to this gallery Retrieved from "http://aceattorney.wikia.com/wiki/Redd_White_-_Sprite_Gallery?oldid=80845" Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you've made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. Categories: Character sprites Add category Cancel Save


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A little drama has seen people taking to their heels at the Murtala Muhammed Airport 1 after an orderly shot himself and injured a female passenger.
People took to their heels at the ticketing area of the general aviation terminal at the Murtala Muhammed Airport 1 in Lagos State on Friday after hearing gunshot sound.
According to ChannelsTV, the orderly of President Buhari's Senior Special Assistant on National Assembly Matters, Senator Ita Enang accidentally shot himself and injured one female passenger.
Both of them were initially rushed to the FAAN clinic before they were transferred to the Lagos University Teaching Hospital (LUTH) to receive more treatment.
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text: Restaurant.com: 80% off of Certificates This post may contain affiliate links. Please see my full disclosure policy for details. January 10, 2011 By Tracie +1 Flip Tweet Share PinShares 0 Restaurant.com is offering gift certificates at 80% off between January 10th & 13th. Just use the code SPOON at checkout to get the savings. +1 Flip Tweet Share PinShares 0


text:
We stayed in a one bedroom apartment on the 9th floor on our recent visit to Melbourne for the tennis. Even though this hotel is in my opinion in need of a little bit of an update we would definitely stay again.
It had a few drawbacks, ie, the 1980's style television, the living area and the bedroom is a bit smaller than some others I have experienced in my travels, wasnt a fan of the tiny shower with the soap holder that stabbed you every time you turned and although road noise wasn't an issue, i certainly heard the trains... we found it to be non stop and took us about 3 nights before we got used to the noise...
But now the good points!
The staff were fantastic, friendly and helpful, all you could ask. Location is first class...sure its not in the most glamorous part of melbourne but it has fast food across the road as well as Michels Patisserie if you want a cheaper breakfast...
Restaurants/Southbank/Crown are like a 5 minute walk, the DFO on South Wharf is a 10 minute walk, groceries can be bought at the Woolies at the Spencer St station round the corner and if you decide to go to Harbour Town the circle line tram station is right in front.
The bed which was a king size was fantastic and if you chose to cook in your apartment, the kitchen facilities are as good as some houses i have seen, but with so much choice to eat out not sure why you would bother!
So yes, love Melbourne and next time we visit we will most likely stay here again!
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