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ImageNet
ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use.
MIRU2014 tutorial deeplearning
2. Deep Learningã«ã¤ã„㦠様々ãªãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã§ãƒˆãƒƒãƒ—レベルã®æ€§èƒ½ 音声èªè˜(2011) 多層(7ã¤ï¼‰çµåˆï¼Žäº‹å‰å¦ç¿’ã‚ã‚Š F. Seide, G. Li and D. Yu, “Conversational Speech Transcription Using Context-Dependent Deep Neural Networks.â€, INTERSPEECH2011. 一般物体èªè˜(2012) 多層ã®CNNã§å¾“æ¥æ€§èƒ½ã‚’大ãã上回る A. Krizhevsky, I. Sutskever and G. E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." NIPS. Vol. 1. No. 2. 2012. 2 3. ãªãœDeep LearningãŒæ³¨ç›®ã•ã‚Œã¦ã‚‹ï¼Ÿ 色々
CV Datasets on the web
If you have additions or changes, send an e-mail (remove the "nospam"). This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. Participate in Reproducib
80 Million Tiny Images
June 29th, 2020 It has been brought to our attention [1] that the Tiny Images dataset contains some derogatory terms as categories and offensive images. This was a consequence of the automated data collection procedure that relied on nouns from WordNet. We are greatly concerned by this and apologize to those who may have been affected. The dataset is too large (80 million images) and the images ar
Computer vision image datasets
Datasets MIT CSAIL LabelMe, open annotation tool related tech report PASCAL Visual Object Classes challenges (2005-2007) Wordnet Caltech101 Caltech256 TREC Video Retrieval Evaluation Oxford buildings dataset Photo-tourism patches UIUC Car detection dataset CMU Face databases Animals on the Web data ETH-80 Graz 02 MIT Objects and Scenes NYU NORB dataset Columbia COIL Oxford flowers dataset SFU act
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ImageOptim — better Save for Web
ImageOptim makes images load faster Removes bloated metadata. Saves disk space & bandwidth by compressing images without losing quality. Faster web pages and apps Reduces image file sizes — so they take up less disk space and downÂload faster — by applying advanced compression that preserves quality. Image files scrubbed clean Removes invisible junk: private EXIF metaÂdata from digital cameras, em
pngquant — lossy PNG compressor
pngquant is a command-line utility and a library for lossy compression of PNG images. The conversion reduces file sizes significantly (often as much as 70%) and preserves full alpha transparency. Generated images are compatible with all web browsers and operating systems. Features High-quality palette generation using a combination of vector quantization algorithms. Unique adaptive dithering algor
GIFアニメ生æˆã«ImageMagickã¯ã‚ªãƒ¯ã‚³ãƒ³ã€æƒ…å¼·ã¯é«˜é€ŸãªGraphicsMagickを使ㆠ- 海峡
大ã—ãŸè©±ã§ã¯ãªã„ã‘ã‚Œã©ã€GIFアニメをコマンドラインã‹ã‚‰ä½œã‚‹ãªã‚‰GraphicsMagickãŒä¾¿åˆ©ãªã®ã§å…±æœ‰ã—ã¾ã™ã€‚ MP4ã®å‹•ç”»ãƒ•ã‚¡ã‚¤ãƒ«ã‹ã‚‰GIFアニメを作ã‚ã†ã¨ã™ã‚‹ã¨ã€ã¾ãšffmpegã§å‹•ç”»ã‚’ç”»åƒã«åˆ†å‰²ã—ã¦ãã‚Œã‹ã‚‰imagemagickã§GIFã«çµåˆã™ã‚‹ã¨ã„ã†æ–¹æ³•ãŒã‚ã‚Šã¾ã™ã€‚ % ffmpeg -i hoge.mp4 -r 2 %04d.png % convert *.png hoge.gif複数枚ã®ç”»åƒã‚’imagemagickã§GIFã«çµåˆã™ã‚‹ã®ãŒå®Ÿã¯çµæ§‹æ™‚é–“ãŒã‹ã‹ã‚‹ãŸã‚ç…©ã‚ã—ã„ã“ã¨ãŒå¤šã„ã§ã™ã€‚å ´åˆã«ã‚ˆã£ã¦ã¯ã€æ•°å秒ã‹ã‹ã£ãŸã‚Šã™ã‚‹ã€‚ ImageMagickã®fork版ã§ã‚ã‚‹GraphicsMagickを使ã†ã¨é«˜é€Ÿã«GIFアニメを生æˆã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ GraphicsMagickã®ã»ã†ãŒ3~4å€é€Ÿã„ GraphicsMagickを使ãˆã°ImageMagickよりも3~4å€é«˜é€Ÿã«GI
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libjpeg-turbo | Main / libjpeg-turbo
libjpeg-turbo Home About libjpeg-turbo Professional Services Sponsors SIMD Coverage of the libjpeg Algorithms "libjpeg-turbo" != "TurboJPEG" Mailing Lists and Discussion Forums Downloads Digital Signatures (Code Signing Policy) Official Binaries: Supported Platforms and Other Notes YUM and APT Repositories Documentation Reports libjpeg-turbo Performance Study A Study on the Usefulness of DCT Scali
本当ã¯é€Ÿã„ImageMagick: サムãƒã‚¤ãƒ«ç”»åƒç”Ÿæˆã‚’10å€é€Ÿãã™ã‚‹æ–¹æ³• - 昼メシ物語
一般的㫠ImageMagick ã®ã‚µãƒ ãƒã‚¤ãƒ«ç”»åƒç”Ÿæˆã¯é…ã„ã¨ã•ã‚Œã¦ãŠã‚Šã€ãƒ‘フォーマンスãŒæ±‚ã‚られるシーンã§ã¯ Imlib2 ãªã©ã®ã‚ˆã‚Šé«˜é€Ÿãªç”»åƒå‡¦ç†ãƒ©ã‚¤ãƒ–ラリãŒä½¿ã‚れるã“ã¨ãŒå¤šã„ã§ã™ã€‚ Imlib2 ã®é«˜é€Ÿã•ã«ã¤ã„ã¦ã¯ã€ä»¥å‰ã€ŒImlib2ã§ImageMagickより3å€é«˜é€Ÿã‹ã¤ç¾Žã—ã„サムãƒã‚¤ãƒ«ç”»åƒã®ç”Ÿæˆ - 床ã®ãƒˆãƒ«ã‚¹ãƒˆã‚¤ã€ã‚²ã‚¤ã¨ã™ã‚‹ã¨ã®ã“ã¨ã€ã¨ã„ã†è¨˜äº‹ã§ç´¹ä»‹ã—ã¾ã—ãŸã€‚ã“ã®è¨˜äº‹ã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã«ãŠã„ã¦ã€Imlib2 ã«ã‚ˆã‚‹ã‚µãƒ ãƒã‚¤ãƒ«ç”»åƒã®ç”Ÿæˆã¯ã€ ImageMagick ã®3å€ç¨‹é«˜é€Ÿã§ã—ãŸã€‚ ã—ã‹ã—〠ImageMagick 㯠Imlib2 より画質ãŒã‚ˆãã€é«˜æ©Ÿèƒ½ã§ä½¿ã„ã‚„ã™ãã€ä»Šã‚‚é »ç¹ã«ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã•ã‚Œã¦ãŠã‚Šã€ã¨ã¦ã‚‚よã出æ¥ã¦ã„ã¾ã™ã€‚ãã®ç‚¹ Imlib2 ã¯ã€2004å¹´ã‹ã‚‰ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã•ã‚Œã¦ãŠã‚‰ãšã€ã‚»ã‚ュリティホールãŒè¦‹ã¤ã‹ã£ã¦ã‚‚ã€å„LinuxディストリビューションãŒãã‚Œãžã‚Œãƒ‘ッãƒã‚’当ã¦
Imlib2ã§ImageMagickより3å€é«˜é€Ÿã‹ã¤ç¾Žã—ã„サムãƒã‚¤ãƒ«ç”»åƒã®ç”Ÿæˆ - 昼メシ物語
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20110904cvsaisentan(shirasy) 3 4_3
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HIPI - Hadoop Image Processing Interface :: Introduction
HIPI is an image processing library designed to be used with the Apache Hadoop MapReduce parallel programming framework. HIPI facilitates efficient and high-throughput image processing with MapReduce style parallel programs typically executed on a cluster. It provides a solution for how to store a large collection of images on the Hadoop Distributed File System (HDFS) and make them available for e
livedoor Techブãƒã‚° : 本当ã¯ã‚‚ã£ã¨é€Ÿã„Imlib2: Imlib2ã§ã‚‚ImageMagickã¨åŒã˜ä»•çµ„ã¿ã§ã‚µãƒ ãƒã‚¤ãƒ«ç”»åƒç”Ÿæˆã‚’速ãã™ã‚‹æ–¹æ³•
ã“ã‚“ã«ã¡ã¯ï¼ã“ã‚“ã«ã¡ã¯ï¼ 開発部ã®ã‚„ã¾ã—ーã§ã™ã€‚ 今回ã¯SmallLightã®ä¸ã§ã‚„ã£ã¦ã„ã‚‹ç´°ã‹ã„ã“ã¨ã«ã¤ã„ã¦ã§ã™ã€‚ SmallLightã¨ã¯ SmallLightã¨ã¯ã€2010年末ã«livedoor labs EDGEã«ã¦ãƒªãƒªãƒ¼ã‚¹ã—ãŸç”»åƒã‚µãƒ ãƒã‚¤ãƒ«ç”Ÿæˆç”¨Apacheモジュールã§ã™ã€‚ JPEGç”»åƒã®èªã¿è¾¼ã¿å‡¦ç†ã®æœ€é©åŒ– JPEGç”»åƒã¯ã€ãã®åœ§ç¸®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã®ç‰¹æ€§ã§èªã¿è¾¼ã¿æ™‚ã«1/2ã€1/4ã€1/8ã«ãƒ€ã‚¦ãƒ³ã‚¹ã‚±ãƒ¼ãƒªãƒ³ã‚°ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚libjpegã§ã¯ç”»åƒèªã¿è¾¼ã¿æ™‚ã«jpeg_decompress_structæ§‹é€ ä½“ã®scale_denomã«ãƒ€ã‚¦ãƒ³ã‚¹ã‚±ãƒ¼ãƒªãƒ³ã‚°æŒ‡æ•°ã‚’指定ã—ã¾ã™ã€‚ SmallLightã§ã¯ã“れをJPEGヒントオプションã¨ã—ã¦å®Ÿè£…ã—ã¦ã„ã¾ã™ã€‚パターン文å—列㫠jpeghint=y を付与ã™ã‚‹ã“ã¨ã§æœ‰åŠ¹ã«ãªã‚Šã¾ã™ã€‚（SmallLightã® README ã®100行目） JPEG
グリーã®å¤§è¦æ¨¡åˆ†æ•£ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸æˆ¦ç•¥(nanofs) | GREE Engineering
ã¯ã˜ã‚ã« ã¯ã˜ã‚ã¾ã—ã¦ã€ã‚°ãƒªãƒ¼æ ªå¼ä¼šç¤¾ã§ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã‚’ã—ã¦ãŠã‚Šã¾ã™kgwsã¨ç”³ã—ã¾ã™ã€‚今回ã¯ã€ã‚°ãƒªãƒ¼å†…ã§å†™çœŸãƒ‡ãƒ¼ã‚¿ã®ä¿å˜ã‚’è¡Œã£ã¦ã„る分散ストレージ(nanofs)を紹介ã•ã›ã¦ã„ãŸã ã‘ã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ 背景 弊社ã§é‹å–¶ã•ã›ã¦ã„ãŸã ã„ã¦ã„ã‚‹ "GREE" ã§ã¯ãƒ¦ãƒ¼ã‚¶ã®å†™çœŸã‚„動画データをä¿å˜ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚1億ユーザを目指ã™ã‚°ãƒªãƒ¼ã¯ã€ãƒ¦ãƒ¼ã‚¶ã®å¢—åŠ ã¨ã¨ã‚‚ã«å†™çœŸã‚„動画データã¯ä¸Šé™ãªã—ã«å¢—åŠ ã—ã¦ã„ãã¾ã™ã€‚ã¾ãŸãƒ¦ãƒ¼ã‚¶ã®çš†æ§˜ã®å¤§åˆ‡ãªãƒ‡ãƒ¼ã‚¿ã‚’失ã†ã“ã¨ã¯è¨±ã•ã‚Œã¾ã›ã‚“ã—ã€ã‚µãƒ¼ãƒ“スをæ¢ã‚ã‚‹ã“ã¨ã‚‚許ã•ã‚Œã¾ã›ã‚“。ãã‚“ãªçŠ¶æ³ã®ä¸ã€æ§˜ã€…ãªæŠ€è¡“や仕組ã¿ã‚’使ã„サービスをé‹å–¶ã—ã¦ã¾ã„ã‚Šã¾ã—ãŸã€‚ グリーã®ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã®æ´å²ã¯å¤§ãã分ã‘ã¦3世代ãŒã‚ã‚Šã¾ã—ãŸã€‚ 第一世代 第一世代ã§ã¯ã‚¢ãƒ—リケーションサーãƒã‹ã‚‰NFSサーãƒã‚’マウントã—ç”»åƒãƒ‡ãƒ¼ã‚¿ã‚’ä¿å˜ã—ã¦ãŠã‚Šã¾ã—ãŸã€‚ç°¡å˜ã«å°Žå…¥ã§ãã‚‹ã“ã¨ã¨é«˜ä¾¡ãªã‚µãƒ¼ãƒã‚’使用ã™ã‚Œã°ä¿¡é ¼æ€§ã‚„安定性もä¿ãŸã‚Œã‚‹
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Mobile Image Processing at Velocity SC, 2015
_level0 | Kayac Interactive Designer's Blog
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