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Robert Baker Obituary - Vernon, NJ | The New Jersey Herald
More Obituaries for Robert Baker
Robert W.A. Baker Sr.
VERNON - Robert W.A. Baker Sr., 38, died after a long illness at home on June 25. Born to Robert W. Baker and Mary Macowiak in Teaneck, he had lived in Saddle Brook, Tuckerton and Upper Township of Cape May before moving to the Barry Lakes section of Vernon, five years ago.
Robert worked as a self- employed electrician for many years. He was an avid outdoorsman, taking great joy in fishing and camping.
Robert was predeceased by his mother, Mary and was the beloved husband for 15 years of Kathleen Baker (Cangialose) of Vernon; devoted father of Robert W.A. Baker Jr., Emily and Cassidy Baker, all at home; loving son of Robert "Wayne" Baker, of Hasbrouck Heights; and dear brother of Jonathan Baker, of Marmora.
Memorial services will be at 2 p.m., Saturday, June 30, at Ferguson-Vernon Funeral Home, 241 Route 94 (1 Vanderhoof Court for GPS), Vernon. Cremation is private. Memorial gifts in lieu of flowers to the family for the children's benefit are greatly appreciated. Information and condolences can be found at www.fergusonvernonfuneralhome.com.
Published in The New Jersey Herald on June 27, 2018
-Cheryl Reed
"Deepest and sincerest condolences to the Baker family and..."
-Rosie Felci

type: global

value: This website is a United States-based website founded in 1998, the world's largest commercial provider of online memorials. The Web site hosts obituaries and memorials for more than 70 percent of all U.S. deaths.


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0 posts edited 0 helpful flags 897 votes cast summary
PHP 5.2.13 MongoCursorException 'duplicate key error index'
Cool, glad you figured it out. I'm not sure this was our problem in the past, but upvoted your answer anyway in case someone stumbles on the same problem.
Sorry to hear that, Robbie. If the issue is bad enough, you may want to run a php-fpm setup and have Apache reverse proxy to it. If I remember correctly, we took that route eventually anyway.
Nothing works with Yosemite
Hi, author of Wookie here (sorry to weigh in this late). Wookie's specialty is going to be in handling IO. Accepting requests, calling APIs over HTTP/TCP, talking to databases, etc. If a good number of your requests require CPU-intensive tasks, you really might want to check out Hunchentoot. If only a few use CPU, you'll want to ship them to a thread pool (like lparallel) so they don't block the event loop/IO thread. Check out orthecreedence.github.io/cl-async/threading for more info!
Unix signal handling in (common) lisp
Thanks, this looks useful. There's some signal handling stuff in cl-async as well, but that only works if you're in an event loop. For general signal handling, this looks like the best option.
Thanks, didn't know you could use sg-****** as the source. This just saved me a buttload of time.
Non-recursive symbol-macrolet
I asked this question a while ago as well on reddit, got some good discussion: reddit.com/r/lisp/comments/13nqrw/…
Third-Party Signed SSL Certificate for localhost/127.0.0.1?
This is an excellent idea (DNS record pointing to 127.0.0.1). Although it's a bit hackish to bundle your key/cert with the app, I think this is pretty much the only way to go. How do you deal with expiration? Have the app download a new cert?
Thanks for the Wookie mention! I would also recommend Hunchentoot for starters. The synchronous model is about 5x easier to wrap your head around. Where Wookie really shines is when you need an app to tie different pieces together (calling out to internal or 3rd party APIs via HTTP, running database queries, grabbing values from redis, queuing larger jobs to the background, etc...anything that requires network I/O). It's not meant to do much "work" other than encoding/decoding and passing data around. Beware also, async driver support is lacking. Your favorite DB probably isn't supported (yet)
InvalidStateError when opening IndexedDB in Firefox 21
Thanks, this was the problem for me as well. Almost the equivalent of "Is the computer plugged in?"
@chbrown confirmed. I'm doing file crypto in JS (none of this [1,2,3].concat([4,5,6]) stuff) and doing for(var i = 0, n = arr2.length; i < n; i++) arr1.push(arr2[i]); seems to be the fastest of any method I've seen so far for larger datasets.
Firefox addon toolbar button disappears after first run
@Stephen awesome, thank you.
@Stephen works great, thanks for putting in the work for this. Is this hosted on github or anywhere similar so I can follow the source?
Firefox Addon: Add some functions and object to the window object
Thanks, after searching far and wide for a way to access an opened tab's window object, this is the one thing that finally worked. Note that unsafeWindow is a top-level var in the content script, not a sub-object of anything.
Javascript regex search/replace is skipping over matches
Your explanation is great, and I understand now. The problem is that JS doesn't support zero-width lookbehind assertions, and \b is too broad, I really need either whitespace or parens to match before/after the number.
@ChocoDeveloper at that point you might as well run everything off of root. It can be extremely convenient to have passwordless sudo, but the security implications are too great for me to consider doing this on any server in a production environment.
My condolences, thanks for the update. I've successfully learned to live without this feature. What a nightmare.
How do the various ANSI CL implementations differ?
or use the rlwrap utility when starting any lisp from the command line. It makes the REPL much more friendly.
Can't agree more for CCL on Windows (x86 OR x86_64). Another nice thing about it is that it runs really well on linux, mac, BSD, and solaris too, so you can literally program once, run just about everywhere. I tend to develop in windows and push my code out to linux servers. Never have a problem doing this with CCL.

value: This website is a question and answer website for professional and enthusiast programmers. It is the flagship site of the Stack Exchange Network, created in 2008 by Jeff Atwood and Joel Spolsky.


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Find out if your remodeling dreams fit your budget reality.Try the Real Cost Finder 34,335 candle niche Home Design Photos
SaveEmail	Tweak Your Space15 Reviews	Dining Room (After)
Ideabooks119	Questions1	Please where did you buy these candle metal shelves that are sticking in the stone wall??? Thanks so much!pieces and had them made by a local metal-smith to hold store bought votive candles.Candle shelves???...Candles on wall...wall candles...Sauna candle idea...Candle shelvesCandles, love candles — peacemom52 Embed
See My Projects SaveEmail	Robin Rigby Fisher CMKBD/CAPS19 Reviews	French Inspired Design
Ideabooks19,631	Questions12	Where can I get the candle stands near this fireplace?katie do u like candle sticks...The candles...Candle holdersfireplace — jerce Embed
Ideabooks6,498	Questions5	I would put lot of candle in my house. Someshape of the bed. ceiling light. candles to the sideas cosy as any other rich colour palette. The cluster of chunky church candles and pretty chandelier enhance the captivating mood. Snuggle up to more sheepskinCandles candles chandlers!!!!...the candle arrangementLuxury — Tiffany Pazol Embed
Ideabooks681	Questions0	Hurricane candle holders with black dirt? White candlewall design with candles...Hurricane with beans/candles...Candle containers...Glass with candlesChair — bkraft526 Embed
Ideabooks4,574	Questions9	Candles, plant, light...Candles in bathroomNO BOYS ALLOWED — Menke Inc Embed
Ideabooks2,757	Questions2	enchanting dining spot. Place battery-powered candles inside and you won’t have to remember to blow out candles or deal with wax dripping on your table.Great idea to use battery candles in lanternsHang lanterns with battery powered candles insideHang lanterns with battery powered candleshang lanterns preferably with battery operated candles under the umbrella tableenchanting dining spot. Place battery-powered candles inside and you won’t have to remember to blow out candles or deal with wax dripping on your table.nice start for this year. I especially like the lighting — concettawade Embed
Ideabooks2,440	Questions1	Like the look/ long single candlesBell jars with moss and candles, silver pumpkins, leaf arragementCandle terrariums...Lighted candles...Natural plants candlesLove this chandelier — kwb1999 Embed
Ideabooks63,803	Questions24	Are those candles or lights on the mantle above the fireplace?Candles on mantle - String of electric lights?Candles on Fireplace...Candles and artThe stone fireplace is amazing but I would just stick to white plastered wall over panels. — Paul Kirby-Smith Embed

value: This website is an American website, online community and software for architecture; interior design and decorating; landscape design and home improvement. It was founded in 2009 and is based in Palo Alto, California.


text:
 Apsara Guest House Kampot City Kampot Cambodja - Ofertes d'hotels amb els MILLORS DESCOMPTES EN LÍNIA
Kampot Pensions
Veure tots els 156 allotjaments a Kampot.
1 Ousaphea Village, Kampong Kandal, Kampot Centre, Kampot, Cambodja - Molt bona ubicació - Veure al mapa
Té alguna pregunta per a Apsara Guest House Kampot City?
Excel·lent 8,0 (92 comentaris)
Excel·lent 8,0 (73 comentaris)
Notable 7,8 (188 comentaris)
Més informació sobre Apsara Guest House Kampot City
Situat a Kampot Centre, Apsara Guest House Kampot City és el lloc perfecte per gaudir de Kampot i els seus voltants. Tota l'emoció del centre de la ciutat és només a 5.9 km. Amb la seva bona localització, l'hotel ofereix fàcil accès a les destinacions obligades de la ciutat.
Amb una oferta de serveis hotelers superior i ampla gama d'entreteniment, Apsara Guest House Kampot City és compromet a que la seva estada sigui el més còmoda possible. Les carecterístiques més important de l'hotel inclouen Wi-Fi gratuït en totes les habitacions, servei de compra d'entrades, recepció 24 hores, magatzem d'equipatge, Zones públiques amb Wi-Fi.
Apsara Guest House Kampot City té 20 habitacions, totes amoblades amb gust per tal d'oferir comforts com armari, tovalloles, penjador de roba, televisió de pantalla plana, mirall. A més, l'oci que l'hotel ofereix assegurarà que tengui molt a fer durant el dia. Descobreixi una encantadora mescla de servei profesional i ampla gama de característiques a Apsara Guest House Kampot City.
Estació d'autobusos de Kampot
Estació ferroviària de Kampot
Comentaris d'Agoda (33)Comentaris de Booking.com (326)
Tots els tipus de viatgers (33)Parelles (14)Viatgers en solitari (15)Famílies amb infants petits (1)Famílies amb infants grans (1)Grups (2)
Tini, Àustria
Be careful! That hotel does not take Bookings from Agoda! ”
Violent and genuinely evil owner! He refuses to speak and contact Agoda. Never ever had such an experience before. Worst place in Cambodia. It was a big disappointment after booking and he refused to give us a room and went very rude. I returned after a few days to ask him to just let Agoda know and to speak to Agoda so I can get my money back. He went very violent and grabbed me and threatened me to call the police.
Spend the extra and get a hotel or stay home. ”
I had no choice but to book in here last Minute as there were no hotels available at a reasonable cost. The lad behind the desk was useful and there was no problem with the language I was shown up 3 flights of stairs because there is no elevator, this almost finished me off as I have bad knees, never mind I thought, soldier on. The room was ok except for no air con, fridge, kettle, water pressure and a ceiling fan that would stop in a single position, it here than when it was off, meaning 5 second of cool air ever 30 seconds. The worst part was the ants, I woke after a fairly broken sleep to find my legs had been eaten by ants, I actually had to brush them off of me. Needless to say I slept fully clothed on my second night. Maybe backpackers are hardier than me, maybe they care less, don't know. All I know is that this place is a nightmare for anyone who doesn't like camping, if you like to sleep, stay cool, have hot or cold drinks, doesn't want to be eaten by ants don't come here
I liked this hotel. The staff was very kind and accommodating. No elevator, so the upper floors are a bit of a walk, but nice big rooms and good A/C.
Good sized room, very clean. Safe area within walking distance to downtown area . Would highly recommend.
we really enjoyed Apsara.it was excellently located near the river on a quiet street.The room was spacious,clean and comfortable.All round very good value for money.i would definitely return there. The one downside was the internet provision.in the 2 days i was there i could only connect for 2 brief periods.this was extremely frustrating for someone like myself who needs to maintain outside contacts.this needs to be addressed.

value: This website is a Singaporean online travel agency and metasearch engine for hotels, vacation rentals, flights, and airport transfer. It is owned by Agoda Company Pte.


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sctx comments on Did I dream of having a lucid dream?jump to contentmy subredditsannouncementsArtAskRedditaskscienceawwblogbookscreepydataisbeautifulDIYDocumentariesEarthPornexplainlikeimfiveFitnessfoodfunnyFuturologygadgetsgamingGetMotivatedgifshistoryIAmAInternetIsBeautifulJokesLifeProTipslistentothismildlyinterestingmoviesMusicnewsnosleepnottheonionOldSchoolCoolpersonalfinancephilosophyphotoshopbattlespicsscienceShowerthoughtsspacesportstelevisiontifutodayilearnedTwoXChromosomesUpliftingNewsvideosworldnewsWritingPromptsedit subscriptionsfront-all-random | books-philosophy-television-Showerthoughts-Art-gadgets-gifs-Futurology-science-tifu-worldnews-aww-funny-AskReddit-DIY-GetMotivated-gaming-mildlyinteresting-Fitness-UpliftingNews-Music-photoshopbattles-nosleep-askscience-WritingPrompts-nottheonion-movies-TwoXChromosomes-creepy-dataisbeautiful-todayilearned-explainlikeimfive-sports-Documentaries-videos-listentothis-LifeProTips-pics-InternetIsBeautiful-food-IAmA-personalfinance-news-history-space-EarthPorn-Jokes-OldSchoolCoolmore » LucidDreamingcommentsrelatedwant to join? sign in or create an account in seconds|Englishlimit my search to /r/LucidDreaminguse the following search parameters to narrow your results:subreddit:subredditfind submissions in "subreddit"author:usernamefind submissions by "username"site:example.comfind submissions from "example.com"url:textsearch for "text" in urlselftext:textsearch for "text" in self post contentsself:yes (or self:no)include (or exclude) self postsnsfw:yes (or nsfw:no)include (or exclude) results marked as NSFWe.g. subreddit:aww site:imgur.com dogsee the search faq for details.advanced search: by author, subreddit...this post was submitted on 22 Apr 20121 point (60% upvoted)shortlink: remember mereset passwordloginSubmit a new text postLucidDreamingsubscribeunsubscribe133,786 readers~75 users here nowAll about Lucid Dreams. How to, sǝɔuǝıɹǝdxǝ, etc.
created by OsakaWilson(?|?) :(a community for 5 yearsmessage the moderatorsMODERATORSOsakaWilson(?|?) :(TheonHad few LDsAutoModeratorBlazingFireStormFlying - Chronicle styleTheLucidSageEven day dreaming about lucid dreamingabout moderation team »012Did I dream of having a lucid dream? (self.LucidDreaming)submitted 2 years ago * by [deleted]2 commentssharecancelsorry, this has been archived and can no longer be voted onloading...sorted by: besttopnewhotcontroversialoldrandomq&ayou are viewing a single comment's thread.view the rest of the comments →[–]sctxNatural Lucid Dreamer 1 point2 points3 points 2 years ago (0 children)sorry, this has been archived and can no longer be voted onIt sounds like you are becoming over-excited in your dreams and waking yourself up. The more your focus on doing things like flying/changing the environment the higher chance you have of waking yourself up. When you realize you are dreaming try to relax and slowly breathe in your surroundings. Although flying around for four or five minutes sounds like a pretty successful LD to me!
permalinkembedaboutblogaboutteamsource codeadvertisejobshelpsite rulesFAQwikireddiquettetransparencycontact usapps & toolsAlien Blue iOS appreddit AMA appmobile sitebuttons<3reddit goldreddit storeredditgiftsreddit.tvradio redditUse of this site constitutes acceptance of our User Agreement and Privacy Policy. © 2015 reddit inc. All rights reserved.REDDIT and the ALIEN Logo are registered trademarks of reddit inc.create a new accountusername:password:verify password:email: (optional)remember mecreate accountsign inusername:password:remember mereset passwordsign inWe care about your privacy, and we never spam. By creating an account, you agree to reddit's User Agreement and Privacy Policy. We're proud of them, and you should read them.English [en]العربية [ar]Беларуская мова [be]български език [bg]Bosanski [bs]català [ca]česky [cs]dansk [da]Deutsch [de]Ελληνικά [el]English (Australia) [en-au]English (Canadian) [en-ca]English (Great Britain) [en-gb]English [en-us]Esperanto [eo]español [es]español [es-ar]eesti keel [et]Euskara [eu]فارسی [fa]suomi [fi]français [fr]Gàidhlig [gd]עברית [he]मानक हिन्दी [hi]hrvatski [hr]Magyar [hu]Հայերեն լեզու [hy]Bahasa Indonesia [id]íslenska [is] (*)italiano (Italy) [it]日本語 [ja]ಕನ್ನಡ [kn_IN]한국어 [ko]Latin [la]1337 [leet]LOL [lol]lietuvių kalba [lt]latviešu valoda [lv]Nederlands [nl]Nynorsk [nn]Norsk [no]Arrrrrrrr! [pir]polski [pl]português [pt] (*)português [pt-pt]português brasileiro [pt_BR]română [ro]русский [ru]slovenčina [sk]slovenščina [sl]српски језик [sr]Srpski [sr-la]Svenska [sv]தமிழ் [ta]ภาษาไทย [th]Türkçe [tr]українська мова [uk]Tiếng Việt [vi]中文 [zh] (*) incomplete volunteer to translateπ Rendered by PID 17665 on app-05 at 2015-03-30 16:35:57.791522+00:00 running f30c8bd country code: US.

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text:
Patent US5774230 - Color image processing apparatus for color-correcting input image data using ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA color image processing apparatus including a read device for reading an original image, a color correcting device for color-correcting input image data using a color-correcting neural network, and an output device for outputting color-corrected image data. The neural network with teacher data, which...http://www.google.com/patents/US5774230?utm_source=gb-gplus-sharePatent US5774230 - Color image processing apparatus for color-correcting input image data using neural networkAdvanced Patent SearchPublication numberUS5774230 APublication typeGrantApplication numberUS 08/652,790Publication dateJun 30, 1998Filing dateMay 23, 1996Priority dateJun 21, 1995Fee statusPaidPublication number08652790, 652790, US 5774230 A, US 5774230A, US-A-5774230, US5774230 A, US5774230AInventorsMakio GotoOriginal AssigneeSharp Kabushiki KaishaExport CitationBiBTeX, EndNote, RefManPatent Citations (7), Referenced by (27), Classifications (12), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetColor image processing apparatus for color-correcting input image data using neural networkUS 5774230 AAbstract A color image processing apparatus including a read device for reading an original image, a color correcting device for color-correcting input image data using a color-correcting neural network, and an output device for outputting color-corrected image data. The neural network with teacher data, which are in effect learning color data reflecting the visual characteristics of human eyes, using learning data produced by reading a copy image produced based on the learning color data. Accordingly, the neural network can correct colors accurately in a manner to fully reflect the visual characteristics of human eyes, thereby making the color difference between the original image and a copy of the same less noticeable.
What is claimed is: 1. A color image processing apparatus including:read means for reading an original image; color correcting means for color-correcting input image data using a color-correcting neural network; and output means for outputting color-corrected image data; said color-correcting neural network learning using teacher data and learning data, said teacher data being learning color data produced by adding data of a first color and/or deleting data of a second color to/from data of master color samples, said first color being a color to which human eyes are highly sensitive, said second color being a color to which human eyes are less sensitive, said learning data being image data produced by said read means as said read means reads an image produced by said output means based on said learning color data. 2. The color image processing apparatus as defined in claim 1, wherein said color correcting means includes a plurality of color-correcting neural networks for individual color characteristics of said input image data, so colors of the input image data are corrected using a neural network for a specific color characteristic in response to a color characteristic of the input image data.
3. The color image processing apparatus as defined in claim 1, wherein said color correcting means includes a color-correcting neural network which learns using teacher data and learning data,said teacher data being latest color-corrected image data, said learning data being input image data produced by said read means as said read means reads an image produced by said output means based on said latest color-corrected image data. 4. The color image processing apparatus as defined in claim 2, wherein said color correcting means includes a color-correcting neural network which learns using teacher data and learning data,said teacher data being latest color-corrected image data, said learning data being input image data produced by said read means as said read means reads an image produced by said output means based on said latest color-corrected image data. 5. The color image processing apparatus as defined in claim 1 further including a memory unit for storing the master color samples data and said learning color data used as said teacher data, said memory unit being connected to said color correcting means.
6. The color image processing apparatus as defined in claim 1, wherein said color correcting means converts the input image data in the form of a RGB signal into one of a CMY signal and a CMYK signal.
7. The color image processing apparatus as defined in claim 1, wherein said color correcting means includes a portrait neural network and a landscape neural network in said plurality of color-correcting neural networks.
8. The color image processing apparatus as defined in claim 7 further including an image selecting device for judging whether an original image is a portrait or a landscape, and said image selecting device for selecting one of said portrait neural network and landscape neural network based on said judgement.
9. The color image processing apparatus as defined in claim 1, wherein said color correcting means includes a general neural network and a user's neural network in said plurality of color-correcting neural networks.
10. The color image processing apparatus as defined in claim 9 further including a learning device for supplying the learning data and the teacher data to said user's neural network.
FIELD OF THE INVENTION The present invention relates to a color image processing apparatus, such as a color copying machine and a color printing machine, furnished with a color correcting function.
BACKGROUND OF THE INVENTION A color image processing apparatus, such as a color copying machine and a color printing machine, is known as an image input/output apparatus which reads a color original image by a color scanner or the like serving as read means and outputs a copy image by a color printer or the like serving as output means.
However, a copy image produced by the above color image processing apparatus hardly renders exactly the same colors as those of the original image even when input image data are supplied to the output means intact.
To eliminate this problem, there has been proposed a method of color-correcting the input image data using a neural network in such a manner to approximate the colors of a copy image to those of the original image. An example of such a method is disclosed in Japanese Laid-open Patent Application No. 2-241271 (1990). In a color correcting device in the cited application, a limited number of color samples, together with a color separation signal of the color samples and a color separation signal of color-corrected color samples, are supplied to a neural network. Then, the neural network learns color correcting parameters adaptively and optimizes the same, thereby enhancing accuracy in color correction.
Another example is disclosed in Japanese Laid-open Patent Application No. 5-153381 (1993). In a color image processing apparatus in the cited application, a neural network circuit converts a color separation signal of light reflected from or passing through an original image into an optimal density signal through learning and corrects colors accurately.
In general, a color scanner or the like serving as read means scans a color original image and sends a color separation signal of R(Red), G(Green), and B(Blue) to the neural network. Then, the neural network converts the same into a density signal of C(Cyan), M(Magenta), and Y(Yellow) and supplies the same to a printer or the like serving as output means. Hereinafter, the firstly- and secondly-mentioned signals are referred to as a RGB signal and a CMY signal, respectively.
This means each RGB value must have a CMY value as a color-corrected value in response to an input of the same. A combination of the RGB value and CMY value (which is referred to as a RGB-CMY combination, hereinafter) is computed in the overall color space, for example, in the following manner: a color scanner or the like serving as read means optically scans an image with reference to a sample of 729 colors in CMY color representation, nine steps from each.
The neural network eventually learns optimal RGB-CMY combinations in the overall color space using the learning data, in other words, the neural network learns to minimize an error between a CMY value in the combination and a corresponding CMY value in the teacher data. By so doing, the neural network corrects colors in such a manner to approximate colors of a copy image to those of the original image as close as possible.
Incidentally, the sense of sight largely depends on color brightness, that is to say, human eyes are highly sensitive to low saturation colors, such as a flesh color and sky blue, while they are less sensitive to high saturation colors, such as yellow. Therefore, some colors may not be corrected as effective as was expected even if an error between the CMY value in the teacher data and the CMY value in the combination is small in numerical value.
Thus, if the neural network corrects every color in the overall color space in the above manner, there is a small error of the same numerical value between each CMY value in the combination and a corresponding CMY value in the teacher data; however, the color difference between an original image and a copy thereof remains noticeable in some low saturation colors, such as a flesh color and sky blue, or colors to which human eyes are highly sensitive.
SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide a color image processing apparatus capable of producing a copy image having less noticeable color difference from an original image by converting input image data of the original image into output image data in a manner fully reflecting the visual characteristics of human eyes.
To fulfill the above object, a color processing apparatus of the present invention includes a read device for reading an original image, a color correcting device for color-correcting input image data using a color-correcting neural network, and an output device for outputting color-corrected image data. The color processing apparatus is characterized in that (1) the neural network is constructed in a manner to learn with teacher data and learning data, and (2) the teacher data are learning color data produced by adding data of colors, to which human eyes are highly sensitive, to data of master color samples and/or deleting data of colors, to which human eyes are less sensitive, from the master color samples data. The learning data are produced by reading the image of a copy image produced based on the above learning color data.
According to the above structure, the color-correcting neural network learns using the learning color data as the teacher data with the learning data. The learning color data are produced by adding data of colors, to which human eyes are highly sensitive, such as a flesh color and sky blue, to the master color samples data and/or deleting data of colors, to which human eyes are less sensitive, such as yellow, from the master color samples data; the learning data are produced by the read device as it reads a copy-image which the output device produced based on the learning color data. Accordingly, the neural network such that corrects colors in a manner to fully reflect the visual characteristics of human eyes can be constructed.
The color-correcting neural network thus constructed corrects colors in accordance with the visual characteristics of human eyes, thereby making the resulting copy image more realistic. Hence, the present invention can provide a color image processing apparatus capable of producing a copy image on which color difference from the original image is less noticeable.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic block diagram showing an example color image processing apparatus in accordance with the present invention;
FIG. 2 is a flowchart detailing a construction procedure of a neural network equipped in a color correcting device of the color image processing apparatus of FIG. 1;
FIG. 3 is a schematic view showing an example structure of the neural network of FIG. 2;
FIG. 4 is a schematic block diagram showing another example color image processing apparatus in accordance with the present invention;
FIG. 5 is a flowchart detailing a construction procedure of a neural network equipped in a color correcting device of the color image processing apparatus of FIG. 4; and
FIG. 6 is a schematic block diagram showing still another example color image processing apparatus in accordance with the present invention.
DESCRIPTION OF THE EMBODIMENTS (First Embodiment) Referring to FIGS. 1 through 3, the following description will describe an example embodiment of the present invention.
As shown in FIG. 1, an example color image processing apparatus of the present invention includes an input device 1 for inputting image information of an original image, a color correcting device 2 for correcting color data of the input image information, and an output device 3 for producing a copy image by outputting image information containing the color-corrected color data.
To be more specific, the input device 1 includes a color scanner as read means, which optically scans a color original image and produces a color separation signal of R(Red), G(Green), and B(Blue) (referred to as RGB signal, hereinafter). The color scanner used herein is capable of producing a RGB signal with 256 levels each.
The output device 3 includes a color printer as output means and produces a copy image based on a density signal of C(Cyan), M(Magenta), and Y(Yellow) (referred to as CMY signal, hereinafter). The color printer used herein is capable of printing an image in CMY color representation with 256 levels each.
The color correcting device 2 is equipped with a color-correcting neural network. Although a detailed description will be given below, it is this neural network that receives a RGB signal of an original image and converts the same into a color-corrected CMY signal of a copy image in such a manner to approximate the colors of the copy image to those of the original image.
A memory unit 4, which serves as storage means, is connected to the color correcting device 2. The memory unit 4 stores color data with which the neural network operates or learns, such as master color samples data and teacher data.
Note that, when the neural network equipped in the color correcting device 2 converts a RGB signal into a CMY signal (through computation) to match the colors of a copy image with those of an original image, the neural network must minimize an error between an output CMY value and a corresponding CMY value in the teacher data. Minimizing an error for each color demands an adequate color samples for computation, or master color samples. Herein, the master color samples are selected at regular intervals in the overall color space of the CMY color representation. For example, assume that each of CMY colors includes nine groups each having 32 steps and 729 combinations are made by selecting one group from each of CMY colors, then some master color samples are selected from these 729 combinations in such a manner to place the same at regular intervals in the overall color space of the CMY color representation. Data of the master color samples thus selected are stored in the memory unit 4.
The neural network learns using the master color samples data as the teacher data, and eventually reduces errors between the output CMY values in all the combinations in the overall color space and corresponding CMY values in the teacher data to one small numerical value.
Incidentally, the sense of sight largely depends on color brightness, that is to say, human eyes are highly sensitive to low saturation colors, such as a flesh color and sky blue, while they are less sensitive to high saturation colors, such as yellow. Therefore, correction of some colors may not be as effective as was expected even if an error between the CMY value in the teacher data and the CMY value in the combination is small in numerical value.
The color image processing apparatus of the present embodiment solves this problem by: 1) producing learning color samples, with which colors can be corrected in a manner to fully reflect the visual characteristics of human eyes, by selectively adding/deleting desired color data to/from the master color samples data; 2) constructing a color-correcting neural network such that uses the above learning color samples as the teacher data.
Next, the construction of the above color-correcting neural network will be described with reference to the flowchart in FIG. 2.
To begin with, the master color samples data pre-stored in the memory unit 4 are extracted (S1).
Then, whether any low saturation color, such as a flesh color and sky blue, to which human eyes are highly sensitive, should be added to the master color samples or not is determined (S2).
When it is determined to add a low saturation color, for example, a flesh color, data of a color sample of the same is added to the master color samples data as target color data (S3). When a flesh color is preferably converted in a more accurate manner, a few to tens of color samples of the same are added to the master color samples data. In practice, the scanner reads the flesh color sample and the RGB-CMY combination thereof is added to the master color samples data. When it is determined not to add any low saturation color in S2, the flow skips to S4.
Then, whether any high saturation color, such as yellow, to which human eyes are less sensitive, should be deleted from the master color samples or not is determined (S4). When it is determined to delete a high saturation color, for example, yellow, data of a color sample of the same are deleted from the master color samples data as unwanted color data (S5). In practice, the RGB-CMY combination of the unwanted color is deleted from the master color samples data. If it is determined not to delete any high saturated color in S4, the flow skips to S6. The color samples data obtained in S1 through S5 are defined as the teacher data with which the neural network learns.
Then, the color samples data obtained in S1 through S5 are supplied to the output device 3, which prints out the same as a learning color sample (S6).
Next, the color scanner of the input device 1 reads (scans) an image of the learning color sample and separates the same into a RGB signal (S7). Accordingly, the neural network starts to learn using the RGB signals produced in S7 as learning (input) data with the teacher data obtained in S1 through S5 (S8). The color-correcting neural network is constructed in the above manner.
The color-correcting neural network thus constructed comprises, as shown in FIG. 3, an input layer 11 for inputting a RGB value, an output layer 13 for outputting a CMY value, and an intermediate layer 12 placed between the input layer 11 and output layer 13; each layer is linked to another by linking weights 14. To be more specific, when the learning (input) data RGB are supplied to the input layer 11, the linking weights 14 are changed in such a manner that the output layer 13 outputs a value approximate to the CMY value in the teacher data. This is how the color-correcting neural network learns. Eventually, the neural network becomes an optimal one such that minimizes an average error between the output value in response to the-learning (input) data RGB and the CMY value in the teacher data.
As has been explained, the color correcting device 2 in the color image processing apparatus of the present embodiment is equipped with the neural network that has learned with the teacher data, which are in effect learning color samples fully reflecting color correction in accordance with the visual characteristics of human eyes, and it is this neural network that converts a RGB signal (color separation signal) of the original image from the color scanner of the input device 1 into a CMY signal (density signal) for the color printer of the output device 3.
Accordingly, the neural network corrects colors in a manner to fully reflect the visual characteristics of human eyes. As a result, not only colors are corrected in a more accurate manner, but also color difference between the original image and a copy image becomes less noticeable.
As has been explained, the learning color sample, which the color-correcting neural network uses as the teacher data, comprises color samples produced by adding data of colors to which human eyes are highly sensitive and/or deleting data of colors to which human eyes are less sensitive to/from the master color samples data. Thus, the neural network uses the learning color sample that best reflects color correction in accordance with the visual characteristics of human eyes
In other words, the learning color sample additionally includes data of low saturation colors, such as a flesh color and sky blue, to which human eyes are highly sensitive, while omitting data of high saturation colors, such as yellow, to which human eyes are less sensitive. This is the reason why the learning color sample fully reflects the color correction in accordance with the visual characteristics of human eyes.
As a result, the neural network corrects colors in a manner to fully reflect the visual characteristics. Thus, not only colors are corrected in a more accurate manner, but also the color difference between the original image and a copy image becomes less noticeable.
Since a CMY color representation printer is used herein, the output layer 13 of the neural network of FIG. 3 has three units. If a CMYK color representation printer is used instead, the number of the units of the output layer 13 is increased to four and so is the number of the units of the other layers to corresponding numbers. However, such a modified neural network can also realize substantially the same effect explained above.
(Second Example) Referring to FIGS. 4 and 5, the following description will describe another example embodiment of the present invention. Hereinafter, like components are labeled with like reference numerals with respect to the first embodiment, and the description of these components is not repeated for the explanation's convenience.
In the first embodiment, the accuracy in color correction is upgraded by the color correcting device 2; however, the color-correcting neural network constructed therein is intended for color correction based on one specific color characteristic. Thus, if some original images having their respective color characteristics are copied, one can be copied in a satisfactory manner while the others are not. To be more specific, both a portrait and a landscape are drawn mostly in low saturated colors. However, each has their respective color characteristics: the former mainly uses a flesh color while the latter a sky blue. In short, if there is only one neural network, satisfactory color correction is limited to an original image having the same color characteristic to that of the neural network. To solve this problem, a color image processing unit of the present embodiment, as shown in FIG. 4, includes a color correcting device 22 instead of the color correcting device 2 of the first embodiment in FIG. 1.
The color correcting device 22 includes a plurality of color-correcting neural networks for individual color characteristics of input image data including a portrait and a landscape: a default neural network (1), a portrait neural work (2), a landscape neural network (3), . . . , a network (n).
Hence, a color-corrected density signal from an adequate color-correcting neural network in the color correcting device 22 is inputted into the output device 3.
In addition, an image selecting device 21 is provided between the input device 1 and color correcting device 22, so that the color-correcting neural networks are automatically switched depending on the kind of the input image data from the input device 1.
More precisely, the image selecting device 21 automatically judges the kind of an original image, such as a portrait and a landscape, based on the characteristics of the input RGB signal reflecting a specific characteristic of the original image, and outputs the judgment to the color correcting device 22. Alternatively, the user may judge the kind of an original image and manually input the judgement data to the color correcting device 22 using a manipulation panel or the like.
Next, the construction of the color-correcting neural network will be described with reference to the flowchart in FIG. 5. Note that the portrait neural network will be explained as a example.
To begin with, master color samples data pre-stored in the memory unit 4 are extracted (S11).
Then, tens of samples of a typical flesh color, are added to the extracted master color samples data (S12). At the same time, whether there are any duplicate data in the additional flesh color data and master color samples data or not, or the additional flesh color data include any similar color in the master color samples data is judged (S13). The duplicate data and similar color data are collectively referred to as duplicate data, hereinafter.
If there exist the duplicate data in S13, the duplicate data are deleted from the additional flesh color data (S14). Otherwise, the flow skips to S15. The duplicate data are deleted in the same manner as S5 in the first embodiment. The color samples data obtained in S11 through S14 are defined as the teacher data with which the neural network learns.
Then, the color samples data obtained in S11 through S14 are supplied to the output device 3, which prints out the same as a learning color sample (S15).
Next, the color scanner of the input device 1 reads an image on the learning color sample and separates the same into a RGB signal (color separation signal) (S16). Accordingly, the neural network learns using the RGB signal as the learning (input) data with the teacher data obtained in S11 through S14 (S17). The portrait neural network is constructed in the manner above.
The color-correcting neural networks other than the portrait neural network, such as the landscape neural network, can be constructed in the same manner as detailed in S11 through S17.
The portrait neural network is of the same structure as its counterpart in the first embodiment of FIG. 3, and learns in the same manner. That is to say, when the learning (input) data RGB are supplied to the input layer 11, the linking weights 14 are changed in such a manner that the output layer 13 outputs a value approximate to the CMY value in the teacher data.
As has been explained, the color image processing apparatus of the present embodiment includes various type of color-correcting neural networks for color-correcting a portrait, a landscape, etc. having their respective color characteristics. The color-correcting neural networks are switched in response to the characteristics of the input image data, so that colors are corrected in such a manner that a copy image renders the color characteristics of the input image data as truly as possible.
Utilizing a plurality of color-correcting neural networks for individual color characteristics can further upgrade the accuracy in color correction while making the color difference between the original image and a copy image less noticeable.
(Third Embodiment) Referring to FIG. 6, the following description will describe still another example embodiment of the present invention.
Herein, colors can be corrected as desired by the user. To be more specific, a color image processing apparatus of the present embodiment, as shown in FIG. 6, includes a color correcting device 32 instead of the color correcting device 22 of the second embodiment in FIG. 4.
The color correcting device 32 is equipped with a user's neural network in addition to those equipped in the color correcting device 22. A learning device 33 is connected to the user's neural network to supply learning data and teacher data.
The learning device 33 produces the learning data and teacher data, which are supplied to the user's neural network, using density data outputted from the color correcting device 32 and input image data read by the input device 1 from a copy image outputted from the output device 3 based on the density data.
The user's neural network includes linking weights of the same type used in its counterpart in the first embodiment, and is capable of correcting colors in the overall color space in a satisfactory manner. The linking weights are changed depending on the learning data supplied from the learning device 33.
The above structure enables the user's neural network to correct colors in accordance with the user's choice and circumstances.
The following description will describe the construction of the user's neural network.
When a latest copy image from the color image processing apparatus of FIG. 6 after the color correction in accordance with the color characteristic of the original image is still unsatisfactory to the user, the user starts the optical scanning of the latest copy image by the input unit 1. At this point, a representative value of the density values used when producing the latest copy image, and a color separation value obtained through reading the latest copy image by the input device 1 are inputted into the learning device 33.
Then, the learning device 33 updates the learning data held therein since the initialization by adding the above representative value of the color separation signal. The learning device 33 also updates the teacher data held therein since the initialization by adding the above representative value of the density values.
The updated learning data and teacher data are inputted into the color correcting device 32 from the learning device 33, upon which the user's neural network starts to learn, in other words, the user's neural network is constructed.
Once the user's neural network is constructed in this manner, colors are always corrected by the user's neural network afterwards.
As has been explained, the color image processing apparatus of the present embodiment includes the user's neural network in the color correcting device 32 that learns with the teacher data, which are in effect the color samples data produced using the color-corrected image data and input image data read from a copy image produced based on the above color-corrected image data. This means that the user can arbitrary change the linking weights of the neural network depending on a subject image in such a manner to minimize a color correction error. In other words, colors can be corrected in accordance with the user's choice and circumstances.
A first color processing apparatus of the present invention includes read means for reading an original image, color correcting means for color-correcting input image data using a color-correcting neural network, and output means for outputting color-corrected image data. The color-correcting neural network is constructed in a manner to learn using teacher data and learning data. The teacher data are learning color data produced by adding data of colors, to which human eyes are highly sensitive, to data of master color samples and/or deleting data of colors, to which human eyes are less sensitive, from the master color samples data. The learning data are produced by reading the image of a copy image produced based on the above learning color data.
According to the above structure, the color-correcting neural network learns using the learning color data as the teacher data with the learning data. The learning color data are produced by adding data of colors, to which human eyes are highly sensitive, such as a flesh color and sky blue, to the master color samples data and/or deleting data-of colors, to which human eyes are less sensitive, such as yellow, from the master color samples data. The learning data are produced by the read means as it reads a copy image which the output means produced based on the learning color data. Accordingly, the neural network such that corrects colors in a manner to fully reflect the visual characteristics of human eyes can be constructed.
The color-correcting neural network thus constructed corrects colors in accordance with the visual characteristics of human eyes, thereby making the resulting copy image more realistic. Hence, the first color image processing apparatus of the present invention can produce a copy image on which color difference from the original image is less noticeable.
A second color image processing apparatus is of the same structure as the first color image processing apparatus except that the color correcting means includes a plurality of color-correcting neural networks for individual color characteristics of input image data, and thus, characterized in that colors in the input image data are corrected depending on its color characteristic using an adequate color-correcting neural network.
To be more specific, the color correcting means includes a plurality of neural networks for individual color characteristics, such as a portrait neural network and a landscape neural network. Thus, colors are corrected in response to the color characteristics of the input image data by switching the neural networks in an adequate manner. As a result, the output image data reflect the color characteristics of the input image data as truly as possible. This enhances the accuracy in color correcting further, and therefore, the color difference between the original image and a copy of the same becomes less noticeable.
A third color image processing apparatus of the present invention is of the same structure as its counterpart in the first or second embodiment except that the color correcting means further includes another type of color-correcting neural network. Unlike the neural network(s) in the first or second color image processing apparatus, the neural network of the third color image processing apparatus uses color-corrected image data as the teacher data and input image data read form the copy image produced based on the color-corrected image data as learning data.
Accordingly, the color correcting means includes a color-correcting neural network that learns using color-corrected image data as the teacher data with the input image data read from the copy image produced based on the post-correction image data as the learning data. This structure enables the user to change the linking weights of the neural network arbitrary depending on a subject image to minimize a color correction error. In short, the user can correct colors in accordance with his choice or circumstances.
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Co., Ltd.Method of correcting setup parameter decision characteristics and automatic setup apparatus using a neural networkUS5546195 *Aug 11, 1994Aug 13, 1996Toyo Ink Manufacturing Co., Ltd.Apparatus for reproducing color imagesUS5636290 *Jun 20, 1996Jun 3, 1997Fuji Xerox Co., Ltd.Color image processingJPH02241271A * Title not availableJPH05153381A * Title not available* Cited by examinerReferenced byCiting PatentFiling datePublication dateApplicantTitleUS6043894 *Jul 10, 1998Mar 28, 2000Gretamacbeth LlcMethod for maintaining uniformity among color measuring instrumentsUS6130756 *Mar 12, 1998Oct 10, 2000Eastman Kodak CoMethod for characterizing a response function of an outputUS6559944Jan 18, 2000May 6, 2003Gretagmacbeth, LlcMethod for maintaining uniformity among color measuring instrumentsUS6577761Oct 26, 1999Jun 10, 2003Toshiba Tec CorporationImage processor and image processing systemUS6714924Feb 7, 2001Mar 30, 2004Basf CorporationComputer-implemented neural network color matching formulation systemUS6804390Feb 7, 2001Oct 12, 2004Basf CorporationComputer-implemented neural network color matching formulation applicationsUS6892194 *Jun 5, 2001May 10, 2005Basf CorporationSystem and method for organizing color values using an artificial intelligence based cluster modelUS6895113Apr 5, 2001May 17, 2005Lightsurf, Inc.Method and apparatus for distributing color corrected images across a network using distributed cachingUS6963668Mar 22, 2001Nov 8, 2005Lightsurf Technologies, Inc.Method and system for fast image correctionUS6973211Jul 26, 2002Dec 6, 2005Basf CorporationColor management and solution distribution system and methodUS6993512Jun 5, 2001Jan 31, 2006Basf CorporationSystem and method for converting a color formula using an artificial intelligence based conversion modelUS6999615Jun 5, 2001Feb 14, 2006Basf CorporationColor management and solution distribution system and methodUS7034960Aug 16, 2001Apr 25, 2006Sun Chemical CorporationSystem and method for managing electronic transmission of color dataUS7092008 *Jun 23, 2000Aug 15, 2006Lightsurf, Inc.Remote color characterization for delivery of high fidelity imagesUS7177466 *Nov 12, 1999Feb 13, 2007Lightsurf Technologies, Inc.System and method for providing high fidelity color imagesUS7202976Feb 9, 2006Apr 10, 2007Sun Chemical CorporationSystem and method for managing electronic transmission of color dataUS7268918Jun 1, 2004Sep 11, 2007Sun Chemical CorporationSystem and method for controlling metamerismUS7339595Mar 7, 2001Mar 4, 2008Lightsurf Technologies, Inc.Method and system for improved internet colorUS7417764May 20, 2004Aug 26, 2008Sun Chemical CorporationSystem and method for disseminating color ink and colorant formulasUS7457458 *Nov 27, 2000Nov 25, 2008Inb Vision Ag.Method and apparatus for defining and correcting image dataUS7536231 *Apr 28, 2005May 19, 2009Basf CorporationMethod for determining acceptability of proposed color solution using an artificial intelligence modelUS7839415Mar 3, 2008Nov 23, 2010Verisign, Inc.Method and system for improved internet colorUS8208170 *Oct 10, 2008Jun 26, 2012Xerox CorporationSystem and method for printing target colors with process colors utilizing parallel feedforward neural networksUS8233189Mar 4, 2008Jul 31, 2012Sun Chemical CorporationSystem and method for disseminating color ink and colorant formulasUS8345060Nov 22, 2010Jan 1, 2013Verisign, Inc.Method and system for improved internet colorUS20100091305 *Oct 10, 2008Apr 15, 2010Xerox CorporationSystem and method for printing target colors with process colors utilizing parallel feedforward neural networksEP1297314A1 *May 11, 2001Apr 2, 2003AG Research LimitedA method of processing data from a spectrophotometer* Cited by examinerClassifications U.S. Classification382/162, 706/25, 358/523, 358/518, 382/167International ClassificationH04N1/46, G06N3/00, G06F15/18, G06T11/60, H04N1/60Cooperative ClassificationH04N1/603European ClassificationH04N1/60FLegal EventsDateCodeEventDescriptionDec 2, 2009FPAYFee paymentYear of fee payment: 12Dec 2, 2005FPAYFee paymentYear of fee payment: 8Sep 27, 2001FPAYFee paymentYear of fee payment: 4Mar 17, 1997ASAssignmentOwner name: SHARP KABUSHIKI KAISHA, JAPANFree format text: A CORRECTIVE ASSIGNMENT TO CORRECT THE ADDRESS OF THE RECEIVING PARTY, PREVIOUSLY RECORDED AT REEL 8018, FRAME 0913;ASSIGNOR:GOTO, MAKIO;REEL/FRAME:008414/0309Effective date: 19960517May 23, 1996ASAssignmentOwner name: SHARP KABUSHIKI KAISHA, JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GOTO, MAKIO;REEL/FRAME:008018/0913Effective date: 19960517RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google

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OstkUser610177 Nov 18, 2016
Sale $3,173.97

value: This website is an American internet retailer selling primarily furniture headquartered in Midvale, Utah, near Salt Lake City.


text:
Ballad of Yachiyo Performance | Philip Kan Gotanda | Audible.com
SAMPLE PLAY AUDIO SAMPLE http%3A%2F%2Fsamples.audible.com%2Fpf%2Flatw%2F000030%2Fpf_latw_000030_sample.mp3+flashcontent0YJTTRM9ZBR117Q21S5M0
Length: 1 hr and 27 mins Performance Release Date:05-01-01 Publisher:
This tale of illicit passion, set among the lush cane fields of the Hawaiian islands in 1919, was inspired by a true incident in the playwright's family. Beautiful 16-year-old Yachiyo reluctantly leaves her impoverished parents to live with sophisticated relatives. Yachiyo's mother hopes she will learn the Japanese tea ceremony and other refinements that will help attract a wealthy husband. But as Yachiyo acquires civilized graces from her hostess, Okusan, she also struggles with a growing passion for Okusan's handsome, dissolute husband. Performed by a full cast starring Sab Shimono, June Angela, Greg Watanabe, Takayo Fischer, Dian Kobayashi, Lane Nishikawa, and Annie Lee.

value: This website is an American online audiobook and podcast service that allows users to purchase and stream audiobooks and other forms of spoken word content. This content can be purchased individually or under a subscription model where the user receives "credits" that can be redeemed for content monthly and receive access to a curated on-demand library of content.


text:
The Andalusian epic : Islamic Spain \ (DVD video, 2004) [WorldCat.org]
http://www.worldcat.org/oclc/58993909
Title: The Andalusian epic : Islamic Spain \
OCLC:58993909
The Andalusian epic : Islamic Spain \
This program addresses the expansion of the Arab empire into Spain, where Muslims ruled with tolerance for more than seven centuries. The introduction and consolidation of Islamic power in Spain, the creation of the Umayyad emirate by the sole survivor of the Umayyad dynasty, the rise of Cordoba as a cultural rival of Abbasid Baghdad, and the gradual ebb of Arab rule on the Iberian Peninsula are all discussed. Special attention is given to the prosperous reign of Abdel Rahman III and the flowering of a Muslim culture that respectfully welcomed the contributions of Christians and Jews alike.
58993909
Narration in English. Some commentary in French with English subtitles. Credits in French.
Original music, Bernhard Elsner; photography, Lionel San-Kenguistel.
a co-production of FIT Production, La Cinquième, and Canal Sur Televisión ; un film conc̦u et écrit par Mahmoud Hussein ; réalisé par Philippe Calderon.
Add tags for "The Andalusian epic : Islamic Spain \".
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The Andalusian epic : Islamic Spain \/Mahmoud Hussein; Philippe Calderon; FIT Production (Firm); Cinquième (Firm); Canal Sur Televisión S.A.; Films for the Humanities (Firm);; Princeton, N.J. : Films for the Humanities & Sciences, ©2004.

value: This website is a union catalog that itemizes the collections of 15,637 libraries in 107 countries that participate in the OCLC global cooperative. It is operated by OCLC, Inc.


text: − −== Sources of Information for This Collection == − −<!--bibdescbegin-->Italy. Salerno civil registration offices. Civil Registration, 1809-1865. State Archive of Salerno (Archivio di Stato di Salerno), Salerno, Italy.<!--bibdescend--> − −The suggested format for citing FamilySearch Historical Collections is found in the following article: [[Help:How to Create Source Citations for Famiy Search Historical Records Collections|Help:How to Create Source Citations for FamilySearch Historical Records Collections.]] Revision as of 19:59, 30 August 2012

value: This website is a nonprofit organization and website offering genealogical records, education, and software. It is operated by The Church of Jesus Christ of Latter-day Saints , and is closely connected with the church's Family History Department.


text:
Robin Miller answers user mail - Racing - ESPN
Robin Miller answers user mail
Updated: January 12, 2004, 5:51 PM ET
Check out what questions RPM.ESPN.com users had for Robin Miller -- and Robin's answers:
Why doesn't IRL release attendance figures for their races? I'm particularly interested to know how the Nazareth race on Aug. 24 compared to the past spring race (2002).Greg O'Brien
Pottstown, Pa.Robin Miller: The tracks announce the attendance, not the IRL in keeping with IMS tradition. The crowd last Sunday was estimated at 15,000 and it was pathetic, especially considering what a gorgeous ...Read full story
Robin Miller answers user mail, June 26
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RPM user comment central

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text:
Kyo Gyo Shin Sho - AbeBooks
Title: kyo gyo shin sho
Ajatasatru: The Story of Who We Are : A New Century of Sutra from Shinrans Kyo-Gyo-Shin-Sho
Published by Buddhist Study Center Pr (1988)
ISBN 10: 0938474073 ISBN 13: 9780938474074
About this Item: Buddhist Study Center Pr, 1988. Paperback. Condition: Good. Good. Used, may have wear and markings but is still in solid reading condition. Pasadena's finest new and used bookstore. Seller Inventory # mon0000384245
About this Item: Buddhist Study Center Pr, 1988. Paperback. Condition: Used: Good. Seller Inventory # SONG0938474073

value: This website is an e-commerce global online marketplace with seven websites that offer books, fine art, and collectables from sellers in over 50 countries. Launched in 1996, it specialises in used, rare and out-of-print books.


text:
The influence of the Jatakas on art and literature (Libro, 2000) [WorldCat.org]
Titolo: The influence of the Jatakas on art and literature
Editore: Delhi : New Delhi : B.R. Pub. Corp. ; Distributed by BRPC (India) Ltd., 2000.
Aggiungi etichette: per "The influence of the Jatakas on art and literature".
Buddhist Art(26 documenti)
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