Image Processing Contrast Enhancement and Filtering ADAPT_HIST_EQUAL- Performs adaptive histogram equalization BUTTERWORTH- Returns the absolute value of the low-pass Butterworth kernel. BYTSCL- Scales all values of an array into range of bytes. CANNY- Implements the Canny edge-detection algorithm. CONVOL- Convolves two vectors or arrays. DIGITAL_FILTER- Calculates coefficients of a non-recursive, digital filter. FFT- Returns the Fast Fourier Transform of an array. HILBERT- Constructs a Hilbert transform. HIST_EQUAL- Histogram-equalizes an image. IR_FILTER - Performs the infinite or finite impulse response filter on data. LEEFILT- Performs the Lee filter algorithm on an image array. MEDIAN- Returns the median value of Array or applies a median filter. ROBERTS- Returns an approximation of Roberts edge enhancement. SMOOTH- Smooths with a boxcar average. SOBEL- Returns an approximation of Sobel edge enhancement. UNSHARP_MASK - Performs an unsharp-mask sharpening filter on a two-dimensional array or a truecolor image. See Also- Wavelet Toolkit Feature Extraction/Image Segmentation CONTOUR- Draws a contour plot. DEFROI- Defines an irregular region of interest of an image. HISTOGRAM- Computes the density function of an array. HOUGH- Returns the Hough transform of a two-dimensional image. IMAGE_STATISTICS- Computes sample statistics for a given array of values. ISOCONTOUR- Interprets the contouring algorithm found in the IDLgrContour object. ISOSURFACE- Returns topologically consistent triangles by using oriented tetrahedral decomposition. LABEL_REGION- Labels regions (blobs) of a bi-level image. MAX- Returns the value of the largest element of Array. MEDIAN- Returns the median value of Array or applies a median filter. MIN- Returns the value of the smallest element of an array. PROFILES- Interactively examines image profiles. RADON- Returns the Radon transform of a two-dimensional image. REGION_GROW- Perform region growing. SEARCH2D- Finds objects or regions of similar data within a 2D array. THIN- Returns the skeleton of a bi-level image. UNIQ- Returns subscripts of the unique elements in an array. WATERSHED- Applies the morphological watershed operator to a grayscale image. WHERE- Returns subscripts of nonzero array elements. Image Display DISSOLVE- Provides a digital dissolve effect for images. IDLgrImage- Creates an image object that represents a mapping from a 2D array of data values to a 2D array of pixel colors. IDLgrPalette- Represents a color lookup table that maps indices to red, green, and blue values. IIMAGE- Creates an iTool and associated user interface (UI) configured to display and manipulate image data. RDPIX- Interactively displays image pixel values. SLIDE_IMAGE- Creates a scrolling graphics window for examining large images. TV- Displays an image. To scale and display the image, use TVSCL. TVCRS- Manipulates the image display cursor. TVLCT- Loads display color tables. TVSCL- Scales and displays an image. XOBJVIEW- Displays object viewer widget. XOBJVIEW_ROTATE- Programmatically rotate the object currently displayed in XOBJVIEW. XOBJVIEW_WRITE_IMAGE- Write the object currently displayed in XOBJVIEW to an image file. ZOOM- Zooms portions of the display. ZOOM_24- Zooms portions of true-color (24-bit) display. Image Geometry Transformations CONGRID- Resamples an image to any dimensions. EXPAND- Shrinks/expands image using bilinear interpolation. EXTRAC- Returns sub-matrix of input array. Array operators (e.g., * and :) should usually be used instead. INTERPOLATE- Returns an array of interpolates. INVERT- Computes the inverse of a square array. POLY_2D- Performs polynomial warping of images. POLYWARP- Performs polynomial spatial warping. REBIN- Resizes a vector or array by integer multiples. REFORM- Changes array dimensions without changing the total number of elements. REVERSE- Reverses the order of one dimension of an array. ROT- Rotates an image by any amount. ROTATE- Rotates/transposes an array in multiples of 90 degrees. SHIFT- Shifts elements of vectors or arrays by a specified number of elements. TRANSPOSE- Transposes an array. WARP_TRI- Warps an image using control points. Morphological Image Operators DILATE- Implements morphologic dilation operator on binary and grayscale images. ERODE- Implements the erosion operator on binary and grayscale images and vectors. LABEL_REGION- Labels regions (blobs) of a bi-level image. MORPH_CLOSE- Applies closing operator to binary or grayscale image. MORPH_DISTANCE- Estimates N-dimensional distance maps, which contain for each foreground pixel the distance to the nearest background pixel, using a given norm. MORPH_GRADIENT- Applies the morphological gradient operator to a grayscale image. MORPH_HITORMISS- Applies the hit-or-miss operator to a binary image. MORPH_OPEN- Applies the opening operator to a binary or grayscale image. MORPH_THIN- Performs a thinning operation on binary images. MORPH_TOPHAT- Applies top-hat operator to a grayscale image. WATERSHED- Applies the morphological watershed operator to a grayscale image. Regions of Interest CW_DEFROI- Creates compound widget used to define region of interest. DEFROI- Defines an irregular region of interest of an image. DRAW_ROI- Draws region or group of regions to current Direct Graphics device. IDLanROI- Represents a region of interest used for analysis. IDLanROIGroup- Analytical representation of a group of regions of interest. IDLgrROI- Object graphics representation of a region of interest. IDLgrROIGroup- Object Graphics representation of a group of regions of interest. LABEL_REGION- Labels regions (blobs) of a bi-level image. REGION_GROW- Grows an initial region to include all areas that match specified constraints. XROI- Utility for defining regions of interest, and obtaining geometry and statistical data about these ROIs.
感谢水木上同领域的同学分享，有了他的整理，让我很方便的获得了CV方面相关的经典论文，我也顺便整理一下，把pdf中的文字贴到网页上，方便其它人更直观的获取所要内容~~~ 资料的下载链接为： http://iask.sina.com.cn/u/2252291285/ish?folderid=775855 以下为该同学的整理的综述： “ 前言：最近由于工作的关系，接触到了很多篇以前都没有听说过的经典文章，在感叹这些文章伟大的同时，也顿感自己视野的狭小。 想在网上找找计算机视觉界的经典文章汇总，一直没有找到。失望之余，我决定自己总结一篇，希望对 CV 领域的童鞋们有所帮助。由于自己的视野比较狭窄，肯定也有很多疏漏，权当抛砖引玉了，如果你觉得哪篇文章是非常经典的，也可以把相关信息连带你的昵称发给我，我好补上。我的信箱 xdyang.ustc@gmail.com 文章主要来源：PAMI, IJCV, TIP, CVIU, PR, IVC, CVGIU, CVPR, ICCV, ECCV, NIPS, SIGGRAPH, BMVC等 主要参考网站: Google scholar, citeseer, cvpapers, opencv 中英文官方网站 主要参考书籍： 数字图像处理 第三版 冈萨雷斯等 图像处理，分析和机器视觉 第三版 Sonka等（非常非常好的一本书） 学习OpenCV 计算机视觉：算法与应用 文章按时间排序，排名不分先后，^_^。每一行最后一栏是我自己加的注释，如果不喜欢可以无视之，如果有不对的地方还请告诉我，免得继续出丑。 给出的文章有些是从google scholar或者citeseer上拷贝下来的，所以有链接。所有的文章在网上都很容易找到。有空的时候我会把它们全部整理出来，逐步上传到ishare.iask.sina.com 由于整理的很仓促，时间也很短，还有很多不完善的地方。我会不断改进，并不时上传新版本。 上传地址为 http://iask.sina.com.cn/u/2252291285/ish?folderid=775855 最后更新：2012/3/14 1990 年之前 Peter Burt,EdwardAdelson The Laplacian Pyramid as A Compact Image Code 虽说这个Laplacian Pyramid是有冗 余的，但使用起来非常简单方便， 对理解小波变换也非常有帮助。这 位Adelson是W.T.Freeman的老板， 都是大牛. J Canny A Computational Approach to Edge Detection 经典不需要解释。在 Sonka的书里 面对这个算法也有比较详细的描 述。 S Mallat. A theory for multiresolution signal decomposition: The wavelet representation Mallat的代表作 M Kass, AWitkin, D Terzopoulos. Snakes: active contour models Deformable model的开山鼻祖。 RMHARALICK Textural Features for ImageClassification 这三篇都是关于纹理特征的，虽然过去这么多年了，现在在检索和识别中依然很有用。 RMHARALICK Statistical and structural approaches Tamura等 Texture features corresponding tovisual perception A PDempster, NM Laird, D BRubin. 1977 Maximum likelihood from incompletedata via the EM algorithm EM 算法在计算机视觉中有着非常重要的作用 L Rabiner.1989 A Tutorial on Hidden Markov Modelsand Selected Applications in SpeechRecognition HMM 同样是计算机视觉必须掌握的一项工具 B D Lucas, TKanade An iterative image registrationtechnique with an application to stereo- vision Lucas 光流法 J R Quinlan Induction of decision trees 偏模式识别和机器学习一点 1990 年 P Perona, JMalik.PAMI Scale-space and edge detectionusing anisotropic diffusion 关于 scale space 最早的一篇论文之一，引用率很高 T Lindeberg Scale-space for discrete signals. Lindeberg 关于 scale space 比较早的一篇，后续还有好几篇 anzad, A.;Hong, Y.H. Invariant image recognition byZernike moments Zernike moment,做过模式识别或者检索的应该都知道这个东东 1991 年 W Freeman, EAdelson. The design and use of steerablefilters Freeman最早的一篇力作，也是我读的第一篇学术论文。现在Freeman在 MIT 风生水起，早已是IEEE Fellow了 Michael J.Swain , DanaH. Ballard Color Indexing. google scholar 上引用将近五千次 MA TurkCVPR Face recognition using eigenfaces 1992 年 L G Brown. A survey of image registrationtechniques. 比较早的一篇关于配准的综述了 1993 年 S G Mallat, ZZhang. Matching pursuits withtime-frequency dictionaries Mallat另一篇关于小波的文章，不研究小波的可以无视之 L Vincent. Morphological grayscalereconstruction in image analysis:Applications and efficient algorithms DPHuttenlocher Comparing images using theHausdorff distance Google scolar 上引用2200多次 1994 年 J Shi, C Tomasi. Good feature to track. Tomasi这个名字还会出现好几次，真的很牛 Linderberg Scale-space theory in computervision J L Barron, DJ Fleet, S S Beauchemin. Performance of optical flowtechniques. 1995 年 R Malladi, JSethian, BVemuri. Shape Modeling with FrontPropagation: A Level Set Approach Level set的经典文章 TF COOTES Active Shape Models-Their Trainingand Application ASM MA Stricker Similarity of color images 颜色检索相关 C Cortes, VVapnik. Support-vector networks. SVM 在计算机视觉中也有着非常重要的地位 1996 年 T MCINERNEY. Deformable models in medicalimage analysis: A survey 活动模型的一篇较早的综述 Tai Sing Lee Image Representation Using 2DGabor Wavelets Google引用也有近千次 Amir Said, A.Pearlman A New, Fast, and Efficient ImageCodec Based on Set Partitioning inHierarchical Tree SPIHT。图像压缩领域与 EBCOT齐名的经典算法。 L P Kaelbling, ML Littman, A WMoore. Reinforcement learning: A survey 机器学习里面的一篇综述，引用率比较高，就列在这了。 B. S.Manjunath and W.Y. Ma Texture features for browsing andretrieval of image data 检索的文章比较多，其实它们的应用不仅仅是检索。只要是需要提取特征的地方，检索里面的方法都可以用到 comparing images using colorcoherence vectors 检索中的CCV方法 Image retrieval using color andshape 关于形状特征后面有一篇综述 1997 年 V Caselles, RKimmel, GSapiro. Geodesic active contours 活动轮廓模型的一个小分支 R E Schapire, YFreund, PBartlett, W SLee. Boosting the Margin: A NewExplanation for the Effectiveness ofVoting Methods. Schapire 和 Freund 发 明 了Adaboost，给计算机视觉带来了不少经典算法 F Maes, DVandermeulen,G Marchal, PSuetens. Multimodality image registration bymaximization of mutual information 互信息量配准 E Osuna, RFreund, FGirosi. Training support vector machines: Anapplication to face detection. SVM在人脸检测中的应用。不过人脸检测最经典的方法应 该是Viola-Jones J Huang, SKumar, MMitra, W-J Zhu,R Zabih. Image indexing using colorcorrelogram Color correlogram，检索中的又一个颜色特征。和前面的 CCV 以及颜色矩特征基本上覆盖了所有的颜色特征。 Y Freund, RSchapire. A decisiontheoretic generalization ofon-line learning and an application toboosting. Adaboost的经典文章 1998 年 1998 年是图像处理和计算机视觉经典文章井喷的一年。大概从这一年开始，开始有了新的趋势。由于竞争的加剧，一些好的算法都先发在会议上了，先占个坑，等过一两年之后再扩展到会议上。 T Lindeberg Feature detection with automaticscale selection Linderberg的 scale space到此为止基本结束了。在一些边缘提取，道路或者血管检测中，scale space 确实是一种很不错的工具 C J C Burges. A tutorial on support vector machinesfor pattern recognition. 使用 svm的话，这篇文章应该是必读的了。比 95 年那篇原始文章引用率还高 M Isard, ABlake. CONDENSATION – Conditional TrackingDensity Propagation for Visual Tracking中的经典文章了 L Page, S Brin,R Motwani, T Winograd The PageRank citation ranking:bringing order to the web 这篇文章应该不属于 CV 的范畴，鉴于作者的大名鼎鼎，暂且列在这 C Tomasi, RManduchi. Bilateral filtering for gray and colorimages. 做过图像滤波平滑去噪或者 HDR的应该都知道Bilateral filter。原理非常非常简单，简单到一个公式就可以概括这篇文章，简单到实在无法扩充到期刊。这也是 Tomasi 第二次出现了。一直很纳闷，这个很直观的思想在这之前怎么就从来没人提呢。 C Xu, J LPrince. Snakes, shapes and gradient vectorflow. 终于碰到中国人写的文章了，很荣幸还是校友。GVF是 snake和levelset领域的重要分支和方法 Wim Sweldens. The lifting scheme: A construction ofsecond generation wavelets. 第二代小波。真正让小波有了实用价值，在 JPEG2000 中就采用的提升小波。个人更喜欢的是下一篇，简单易懂，字体也大 DaubechiesWim Sweldens Factoring wavelet transforms intolifting steps 另一位作者也很牛，小波十讲的作者 H A Rowley, SBaluja, TKanade. Neural Network-based FaceDetection. 做人脸的应该是必看的了。不做人脸的话应该可以不用看吧 J B A Maintz,M A Viergever. A survey of medical imageregistration. 关于图像配准的另一篇综述 T F Cootes, GJ Edwards, CJ Taylor. Active Appearance Models AAM 1999 年 D Lowe. Object Recognition from LocalScale-invariant Features 大名鼎鼎的SIFT，后面有一篇IJCV上的 Journal版本，更全面一点。 R E Schapire. A brief Introduction to Boosting 还是 boosting D M Gavrila. The visual analysis of humanmovements: a survey 综述文章的引用一般都比较高 Y Rui, T SHuang, S FChange. Image retrieval: current techniques,promising directions, and openissues TSHuang小组对检索的一个总结 J K Aggarwal, QCai. Human motion analysis: a review 人体运动分析的一个综述 2000 年 世纪之交，各种综述都出来了 J Shi, J Malik. Normalized Cuts and ImageSegmentation NCuts的引用率相当高，Jianbo Shi也因为这篇文章成为计算机视觉界引用率最高的作者之一 Z Zhang. A Flexible New Technique forCamera Calibration 张正友的关于摄像机标定的经典短文 A K Jain, R P WDuin, J C Mao. Statistical pattern recognition: areview. 统计模式识别综述，这一年 pami上两篇很有名的综述之一。 在这里推荐 Web 写的 Statistical PatternRecognition第三版，相当不错，网上有电子版。 C Stauffe Learning Patterns of Activity UsingReal-Time Tracking 搜 TLD 的时候发现这篇文章引用率也很高，两千多次。还没来得及读。 D Taubman. High performance Scalable ImageCompression With EBCOT EBCOT，JPEG2000 中的算法 A W MSmeulders, MWorring, SSantini, AGupta, R Jain. Content-based image retrieval at theend of the early years 在世纪之交对图像检索的一篇很权威的综述。感觉在这之后检索的研究也没那么热了。不过在工业界热度依旧，各大网上购物平台，比如淘宝， 亚马逊，京东等都在做这方面的研发，衣服检索是一个很不错的应用点。 M Pantic, L J MRothkrantz. Automatic analysis of facialexpressions: the state of the art. N Paragios, RDeriche. Geodesic active contours and levelsets for the detection and tracking ofmoving objects 使用 level set做跟踪 Y Rubner, CTomasi, LGuibas. TThe earth mover’s distance as ametric for image retrieval. EMD算法。Tomasi再次出现 PicToSeek Combining Color andShape Invariant Features for ImageRetrieval 依然是检索特征 2001 年 Paul Viola,Michael JJones. Robust real-time object detection 这是一篇很牛的文章，在人脸检测上几乎成了标准。比较坑爹的是，号称发在IJCV2001 上，但怎么找也找不到。应该是 IJCV2004年的那篇“Robust real-time face detection”吧。他们在这一年另一篇比较出名的文章是在CVPR上的“Rapid ObjectDetection using a Boosted Cascadeof Simple Features”这篇才是04年那篇著名文章的会议版。 Y Boykov,Kolmogorov. An experimental comparison ofmin-cut/max-flow algorithms forenergy minimization in vision. 俄罗斯人在 graph cut 领域开始发力了 T Moeslund, EGranum. A Survey of Computer Vision BasedHuman Motion Capture 人体运动综述 T F Chan, LVese. Active contours without edges. Snake 和 level set领域的经典文章 A M Martinez,A C Kak. PCA versus LDA PCA 也是计算机视觉中非掌握不可的工具。LDA在模式识别中有很重要的地位 BS Manjunath Color and texture descriptors 颜色和纹理的描述子，在识别中很有用 2002 年 D Comaniciu, PMeer. Mean shift: A robust approachtoward feature space analysis. Mean shift的经典文章。前两天发现 Comaniciu 已经是 IEEE Fellow了 Ming-HusanYang, David JKriegman,NarendraAhuja. Detecting Faces in Images: ASurvey. 人脸检测综述，引用率想不高都难 R Hsu, MAbdel-Mottaleb. Face Detection in Color Images. 依然是人脸检测，名字都起得这么霸气 J-L Starck, E JCandès, D LDonoho. The curvelet transform for imagedenoising. Geometrical wavelet 中的一篇代表 作 。 其 他 的 如 ridgelet,contourlet, bandelet 等在这里就不赘述了。研究这方面的很容易找到这方面的经典文献。个人以为不研究这方面的看了后对自己的研究也不会有多大启发。曾经以为这个方向会很火，到最后还是没火起来。 我觉得原因可能是现在存储和传输能力的大大提高，使得对压缩的需求没有那么大了，这方面的研究自然就停滞了，就如同JPEG2000没有成气候 Shape matching and objectrecognition using shape contexts Shape context。用形状匹配达到目标识别目的。这方面最经典的文章了。随后后续也有一些这方面的文章，但基本都是很小的改进或者应用。作者提供了原码，可以在 matlab上运行看看效果。 N Paragios, RDeriche. Geodesic active regions and levelset methods for supervised texturesegmentation Statistical Color Models withApplication to Skin Detection A tutorial on particle filters for onlinenonlinear non-Gaussian Bayesiantracking particle filter 的一个综述 2003 年 W Zhao, RChellappa, PJ Phillips, ARosenfeld. Face recognition: A literature survey. 人脸检测的综述 J Sivic, AZisserman. Video Google: A text retrievalapproach to object matching invideos. 好像是Visual words的起源文章。引用率很高，先列出来再看。 D Comaniciu,V Ramesch,P Meer. Kernel-Based Object Tracking. 基于核的跟踪。 B Zitová, JFlusser. Image registration methods: Asurvey. 又一篇图像配准的综述。做图像配准的比较有福气，综述很多 KMikolajczyk,C Schmid. A performance evaluation of localdescriptors. 比较各种描述子的，包括SIFT M JWainwright,M I Jordan. Graphical models, exponentialfamilies, and variational inference. 乔丹的名气太大，不露露脸说不过去 J Portilla, VStrela, MWainwright, ESimoncelli. Image denoising using scalemixtures of gaussians in the waveletdomain. 图像去噪，小波变换，混合高斯 Robert E.Schapire The Boosting Approach to MachineLearning An Overview boosting作者自己写的综述，自然值得一看。 2004 年 Lucas-Kanade 20 Years On AUnifying Framework 引用文章摘要的第一句话Since the Lucas-Kanade algorithmwas proposed in 1981 imagealignment has become one of themostwidely used techniques in computervision. Applications range fromoptical flow and tracking to layeredmotion, mosaic construction, andface coding. D G Lowe. Distinctive image features fromscale-invariant keypoints. SIFT，不解释 Chih-ChungChang,Chih-Jen Lin. LIBSVM: A library for support vectormachines 我实在怀疑引用这篇文章的人是否都看过这篇文章。貌似不看这篇文章也可以使用 LIBSVM Z Wang, A CBovik, H RSheikh, E PSimoncelli. Image quality assessment: Fromerror visibility to structural similarity 图像质量评价，最近 Bovik 还有一篇类似的文章也刊登在 TIP上 Y Ke, RSukthankar. Pca-sift: a more distinctiverepresentation for local imagedescriptors SIFT 的变形 Review of shape representation anddescription techniques Efficient Graph-Based ImageSegmentation 2005 年 N Dalal, BTriggs. Histograms of oriented gradients forhuman detection. HOG 虽然很新，但很经典 A C Berg, T LBerg, J Malik. Shape matching and objectrecognition using low distortioncorrespondences. 还是 shape matching S Roth, MBlack. Fields of experts: A framework forlearning image priors. 这篇应该要归结到图像统计特性的范畴吧 Z Tu, X Chen,A L Yuille, S CZhu. Image parsing: Unifyingsegmentation, detection, andrecognition. Geodesic active regions and level setmethods for motion estimation andtracking Chunming Li,ChenyangXu,ChangfengGui, and Martin D. Fox Level Set Evolution WithoutRe-initialization: A New VariationalFormulation 这篇文章解决了level set中需要不停的重初始化的问题。在 2010 年的 TIP上有一篇 Journal版本Distance Regularized Level SetEvolution and its Application toImage Segmentation A Performance Evaluation of LocalDescriptors 前面那篇是会议的，这篇是 PAMI上的。比较各种描述子的，包括SIFT 2006 年 D Donoho. Compressed sensing. CS 压缩感知 最近很火的一个名词 Greg Welch,Gary Bishop. An introduction to the Kalman Filter. kalman滤波 S Lazebnik, CSchmid, JPonce. Beyond bags of features: spatialpyramid matching for recognizingnatural scene categories. Visual words Xiaojin Zhu. Semi-supervised learning literaturesurvey. A Yilmaz, OJaved, MShah. Object Tracking: A survey. tracking的一篇综述 Image Alignment and Stitching: ATutorial 2007 年 A Review of Statistical Approaches toLevel Set Segmentation: IntegratingColor, Texture, Motion and Shape The Appearance of Human Skin:A Survey Local Invariant Feature Detectors: ASurvey 2008 年 H Bay, A Ess,T Tuytelaars,L V Gool. SURF: Speeded Up Robust Features. K E A van deSande, TGevers, C GM Snoek. Evaluation of Color Descriptors forObject and Scene Recognition M Yang A Survey of Shape FeatureExtraction Techniques 虽然这篇文章的引用率目前来看并不高,但个人认为这是一篇在shape feature方面很不错的文章 P.Felzenszwalb,D. McAllester,D. Ramanan A Discriminatively Trained,Multiscale, Deformable Part Model 2008 年的 CVPR，到现在引用已有四百多次，潜力巨大。rosepink提供 2009 年 J Wright, A YYang, AGanesh, S SSastry, Ma. Robust Face Recognition via SparseRepresentation. B Settles. Active learning literature survey 2010 年 2011 年 Hough Forests for Object Detection,Tracking, and Action Recognition Robust Principal ComponentAnalysis? Candes 和 UIUC 的Ma Yi等人 2012 年 Zdenek Kalal,KrystianMikolajczyk,and Jiri Matas, Tracking-Learning-Detection PAMI上的，虽然还没有正式发表，但肯定会火。在作者的主页上有几篇相关的会议文章， demo和code。用到了 Lucas-Kanade方法 （完）“
作图像处理方面的研究工作，最重要的两个问题：其一是要把握住国际上最前沿的内容；其二是所作工作要具备很高的实用背景。解决第一个问题的办法就是找出这个方向公认最牛的几个超级大拿(看看他们都在作什么)和最权威的出版物(阅读上面最新的文献)，解决第二个问题的办法是你最好能够找到一个实际应用的项目，边做边写文章。 做好这几点的途径之一就是充分利用网络资源，特别是权威网站和大拿们的个人主页。下面是我收集的一些资源,希望对大家有用. 研究群体 大拿主页 前沿期刊 GPL软件资源 搜索引擎 一、研究群体 http://www-2.cs.cmu.edu/~cil/vision.html 这是卡奈基梅隆大学的计算机视觉研究组的主页，上面提供很全的资料，从发表文章的下载到演示程序、测试图像、常用链接、相关软硬件，甚至还有一个搜索引擎。 http://www.cmis.csiro.au/IAP/zimage.htm 这是一个侧重图像分析的站点，一般。但是提供一个Image Analysis环境---ZIMAGE and SZIMAGE。 http://www.via.cornell.edu/ 康奈尔大学的计算机视觉和图像分析研究组，好像是电子和计算机工程系的。侧重医学方面的研究，但是在上面有相当不错资源，关键是它正在建设中，能够跟踪一些信息。 http://www2.parc.com/istl/groups/did/didoverview.shtml 有一个很有意思的项目：DID(文档图像解码)。 http://www-cs-students.stanford.edu/ 斯坦福大学计算机系主页，自己找吧:( http://www.fmrib.ox.ac.uk/analysis/ 主要研究：Brain Extraction Tool,Nonlinear noise reduction,Linear Image Registration, Automated Segmentation,Structural brain change analysis,motion correction,etc. http://www.cse.msu.edu/prip/ 这是密歇根州立大学计算机和电子工程系的模式识别--图像处理研究组，它的FTP上有许多的文章(NEW)。 http://pandora.inf.uni-jena.de/p/e/index.html 德国的一个数字图像处理研究小组，在其上面能找到一些不错的链接资源。 http://www-staff.it.uts.edu.au/~sean/CVCC.dir/home.html CVIP(used to be CVCC for Computer Vision and Cluster Computing) is a research group focusing on cluster-based computer vision within the Spiral Architecture. http://cfia.gmu.edu/ The mission of the Center for Image Analysis is to foster multi-disciplinary research in image, multimedia and related technologies by establishing links between academic institutes, industry and government agencies, and to transfer key technologies to help industry build next generation commercial and military imaging and multimedia systems. http://peipa.essex.ac.uk/info/groups.html 可以通过它来搜索全世界各地的知名的计算机视觉研究组(CV Groups)，极力推荐。 二、图像处理GPL库 http://www.ph.tn.tudelft.nl/~klamer/cppima.html Cppima 是一个图像处理的C++函数库。这里有一个较全面介绍它的库函数的文档，当然你也可以下载压缩的GZIP包，里面包含TexInfo格式的文档。 http://iraf.noao.edu/ Welcome to the IRAF Homepage! IRAF is the Image Reduction and Analysis Facility, a general purpose software system for the reduction and analysis of astronomical data. http://entropy.brni-jhu.org/tnimage.html 一个非常不错的Unix系统的图像处理工具，看看它的截图。你可以在此基础上构建自己的专用图像处理工具包。 http://sourceforge.net/projects/ 这是GPL软件集散地，到这里找你想要得到的IP库吧。 三、搜索资源 当然这里基本的搜索引擎还是必须要依靠的，比如Google等，可以到我常用的链接看看。下面的链接可能会节省你一些时间： http://sal.kachinatech.com/ http://cheminfo.pku.edu.cn/mirrors/SAL/index.shtml 四、大拿网页 http://www.ai.mit.edu/people/wtf/ 这位可是MIT人工智能实验室的BILL FREEMAN。大名鼎鼎！专长是：理解--贝叶斯模型。 http://www.merl.com/people/brand/ MERL(Mitsubishi Electric Research Laboratory)中的擅长“Style Machine”高手。 http://research.microsoft.com/~ablake/ CV界极有声望的A.Blake 1977年毕业于剑桥大学三一学院并或数学与电子科学学士学位。之后在MIT，Edinburgh，Oxford先后组建过研究小组并成为Oxford的教授，直到1999年进入微软剑桥研究中心。主要工作领域是计算机视觉。 http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/har/Web/home.html 这位牛人好像正在学习汉语，并且搜集了诸如“两只老虎(Two Tigers)”的歌曲，嘿嘿:) 他的主页上面还有几个牛：Shumeet Baluja, Takeo Kanade。他们的Face Detection作的绝对是世界一流。他毕业于卡奈基梅隆大学的计算机科学系，兴趣是计算机视觉。 http://www.ifp.uiuc.edu/yrui_ifp_home/html/huang_frame.html 这位老牛在1963年就获得了MIT的博士学位！他领导的Image Lab比较出名的是指纹识别。 -------------------------------------------------------------------------------- 下面这些是我搜集的牛群(大部分是如日中天的Ph.D们)，可以学习的是他们的Study Ways! Finn Lindgren(Sweden):Statistical image analysis http://www.maths.lth.se/matstat/staff/finn/ Pavel Paclik(Prague):statistical pattern recognition http://www.ph.tn.tudelft.nl/~pavel/ Dr. Mark Burge:machine learning and graph theory http://cs.armstrong.edu/burge/ yalin Wang:Document Image Analysis http://students.washington.edu/~ylwang/ Geir Storvik: Image analysis http://www.math.uio.no/~geirs/ Heidorn http://alexia.lis.uiuc.edu/~heidorn/ Joakim Lindblad:Digital Image Cytometry http://www.cb.uu.se/~joakim/index_eng.html S.Lavirotte: http://www-sop.inria.fr/cafe/Stephane.Lavirotte/ Sporring: scale-space techniques http://www.lab3d.odont.ku.dk/~sporring/ Mark Jenkinson:Reduction of MR Artefacts http://www.fmrib.ox.ac.uk/~mark/ Justin K. Romberg:digital signal processing http://www-dsp.rice.edu/~jrom/ Fauqueur:Image retrieval by regions of interest http://www-rocq.inria.fr/~fauqueur/ James J. Nolan:Computer Vision http://cs.gmu.edu/~jnolan/ Daniel X. Pape:Information http://www.bucho.org/~dpape/ Drew Pilant:remote sensing technology http://www.geo.mtu.edu/~anpilant/index.html 五、前沿期刊(TOP10) 这里的期刊大部分都可以通过上面的大拿们的主页间接找到，在这列出主要是为了节省直接想找期刊投稿的兄弟的时间:) IEEE Trans. On PAMI http://www.computer.org/tpami/index.htm IEEE Transactionson Image Processing http://www.ieee.org/organizations/pubs/transactions/tip.htm Pattern Recognition http://www.elsevier.com/locate/issn/00313203 Pattern Recognition Letters http://www.elsevier.com/locate/issn/01678655 神经网络 Neural Networks Tutorial Review http://hem.hj.se/~de96klda/NeuralNetworks.htm ftp://ftp.sas.com/pub/neural/FAQ.html Image Compression with Neural Networks http://www.comp.glam.ac.uk/digimaging/neural.htm Backpropagator's Review http://www.dontveter.com/bpr/bpr.html Bibliographies on Neural Networks http://liinwww.ira.uka.de/bibliography/Neural/ Intelligent Motion Control with an Artificial Cerebellum http://www.q12.org/phd.html Kernel Machines http://www.kernel-machines.org/ Some Neural Networks Research Organizations http://www.ieee.org/nnc/ http://www.inns.org/ Neural Network Modeling in Vision Research http://www.rybak-et-al.net/nisms.html Neural Networks and Machine Learning http://learning.cs.toronto.edu/ Neural Application Software http://attrasoft.com Neural Network Toolbox for MATLAB http://www.mathworks.com/products/neuralnet/ Netlab Software http://www.ncrg.aston.ac.uk/netlab/ Kunama Systems Limited http://www.kunama.co.uk/ Computer Vision Computer Vision Homepage, Carnegie Mellon University www.cs.cmu.edu/~cil/vision.html Annotated Computer Vision Bibliography http://iris.usc.edu/Vision-Notes/bibliography/contents.html http://iris.usc.edu/Vision-Notes/rosenfeld/contents.html Lawrence Berkeley National Lab Computer Vision and Robotics Applications http://www-itg.lbl.gov/ITG.hm.pg.docs/VISIon/vision.html CVonline by University of Edinburgh The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision, www.dai.ed.ac.uk/CVonline Computer Vision Handbook, www.cs.hmc.edu/~fleck/computer-vision-handbook Vision Systems Courseware www.cs.cf.ac.uk/Dave/Vision_lecture/Vision_lecture_caller.html Research Activities in Computer Vision http://www-syntim.inria.fr/syntim/analyse/index-eng.html Vision Systems Acronyms www.vision-systems-design.com/vsd/archive/acronyms.html Dictionary of Terms in Human and Animal Vision http://cns-web.bu.edu/pub/laliden/WWW/Visionary/Visionary.html Metrology based on Computer Vision www.cranfield.ac.uk/sme/amac/research/metrology/metrology.html Digital Photography Digital Photography, Scanning, and Image Processing www.dbusch.com/scanners/scanners.html Educational Resources, Universities Center for Image Processing in Education www.cipe.com Library of Congress Call Numbers Related to Imaging Science by Rochester Institute of Technology http://wally2.rit.edu/pubs/guides/imagingcall.html Mathematical Experiences through Image Processing, University of Washington www.cs.washington.edu/research/metip/metip.html Vismod Tech Reports and Publications, MIT http://vismod.www.media.mit.edu/cgi-bin/tr_pagemaker Vision Lab PhD dissertation list, University of Antwerp http://wcc.ruca.ua.ac.be/~visielab/theses.html INRIA (France) Research Projects: Human-Computer Interaction, Image Processing, Data Management, Knowledge Systems www.inria.fr/Themes/Theme3-eng.html Image Processing Resources http://eleceng.ukc.ac.uk/~rls3/Contents.htm Publications of Carsten Steger http://www9.informatik.tu-muenchen.de/people/steger/publications.html FAQs comp.dsp FAQ www.bdti.com/faq/dsp_faq.htm Robotics FAQ www.frc.ri.cmu.edu/robotics-faq Where's the sci.image.processing FAQ? www.cc.iastate.edu/olc_answers/packages/graphics/sci.image.processing.faq.html comp.graphics.algorithms FAQ, Section 3, 2D Image/Pixel Computations www.exaflop.org/docs/cgafaq Astronomical Image Processing System FAQ www.cv.nrao.edu/aips/aips_faq.html
作图像处理方面的研究工作，最重要的两个问题：其一是要把握住国际上最前沿的内容；其二是所作工作要具备很高的实用背景。解决第一个问题的办法就是找出这个方向公认最牛的几个超级大拿(看看他们都在作什么)和最权威的出版物(阅读上面最新的文献)，解决第二个问题的办法是你最好能够找到一个实际应用的项目，边做边写文章。 做好这几点的途径之一就是充分利用网络资源，特别是权威网站和大拿们的个人主页。下面是我收集的一些资源,希望对大家有用. 研究群体 大拿主页 前沿期刊 GPL软件资源 搜索引擎 一、研究群体 http://www-2.cs.cmu.edu/~cil/vision.html 这是卡奈基梅隆大学的计算机视觉研究组的主页，上面提供很全的资料，从发表文章的下载到演示程序、测试图像、常用链接、相关软硬件，甚至还有一个搜索引擎。 http://www.cmis.csiro.au/IAP/zimage.htm 这是一个侧重图像分析的站点，一般。但是提供一个Image Analysis环境---ZIMAGE and SZIMAGE。 http://www.via.cornell.edu/ 康奈尔大学的计算机视觉和图像分析研究组，好像是电子和计算机工程系的。侧重医学方面的研究，但是在上面有相当不错资源，关键是它正在建设中，能够跟踪一些信息。 http://www2.parc.com/istl/groups/did/didoverview.shtml 有一个很有意思的项目：DID(文档图像解码)。 http://www-cs-students.stanford.edu/ 斯坦福大学计算机系主页，自己找吧:( http://www.fmrib.ox.ac.uk/analysis/ 主要研究：Brain Extraction Tool,Nonlinear noise reduction,Linear Image Registration, Automated Segmentation,Structural brain change analysis,motion correction,etc. http://www.cse.msu.edu/prip/ 这是密歇根州立大学计算机和电子工程系的模式识别--图像处理研究组，它的FTP上有许多的文章(NEW)。 http://pandora.inf.uni-jena.de/p/e/index.html 德国的一个数字图像处理研究小组，在其上面能找到一些不错的链接资源。 http://www-staff.it.uts.edu.au/~sean/CVCC.dir/home.html CVIP(used to be CVCC for Computer Vision and Cluster Computing) is a research group focusing on cluster-based computer vision within the Spiral Architecture. http://cfia.gmu.edu/ The mission of the Center for Image Analysis is to foster multi-disciplinary research in image, multimedia and related technologies by establishing links between academic institutes, industry and government agencies, and to transfer key technologies to help industry build next generation commercial and military imaging and multimedia systems. http://peipa.essex.ac.uk/info/groups.html 可以通过它来搜索全世界各地的知名的计算机视觉研究组(CV Groups)，极力推荐。 二、图像处理GPL库 http://www.ph.tn.tudelft.nl/~klamer/cppima.html Cppima 是一个图像处理的C++函数库。这里有一个较全面介绍它的库函数的文档，当然你也可以下载压缩的GZIP包，里面包含TexInfo格式的文档。 http://iraf.noao.edu/ Welcome to the IRAF Homepage! IRAF is the Image Reduction and Analysis Facility, a general purpose software system for the reduction and analysis of astronomical data. http://entropy.brni-jhu.org/tnimage.html 一个非常不错的Unix系统的图像处理工具，看看它的截图。你可以在此基础上构建自己的专用图像处理工具包。 http://sourceforge.net/projects/ 这是GPL软件集散地，到这里找你想要得到的IP库吧。 三、搜索资源 当然这里基本的搜索引擎还是必须要依靠的，比如Google等，可以到我常用的链接看看。下面的链接可能会节省你一些时间： http://sal.kachinatech.com/ http://cheminfo.pku.edu.cn/mirrors/SAL/index.shtml 四、大拿网页 http://www.ai.mit.edu/people/wtf/ 这位可是MIT人工智能实验室的BILL FREEMAN。大名鼎鼎！专长是：理解--贝叶斯模型。 http://www.merl.com/people/brand/ MERL(Mitsubishi Electric Research Laboratory)中的擅长“Style Machine”高手。 http://research.microsoft.com/~ablake/ CV界极有声望的A.Blake 1977年毕业于剑桥大学三一学院并或数学与电子科学学士学位。之后在MIT，Edinburgh，Oxford先后组建过研究小组并成为Oxford的教授，直到1999年进入微软剑桥研究中心。主要工作领域是计算机视觉。 http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/har/Web/home.html 这位牛人好像正在学习汉语，并且搜集了诸如“两只老虎(Two Tigers)”的歌曲，嘿嘿:) 他的主页上面还有几个牛：Shumeet Baluja, Takeo Kanade。他们的Face Detection作的绝对是世界一流。他毕业于卡奈基梅隆大学的计算机科学系，兴趣是计算机视觉。 http://www.ifp.uiuc.edu/yrui_ifp_home/html/huang_frame.html 这位老牛在1963年就获得了MIT的博士学位！他领导的Image Lab比较出名的是指纹识别。 -------------------------------------------------------------------------------- 下面这些是我搜集的牛群(大部分是如日中天的Ph.D们)，可以学习的是他们的Study Ways! Finn Lindgren(Sweden):Statistical image analysis http://www.maths.lth.se/matstat/staff/finn/ Pavel Paclik(Prague):statistical pattern recognition http://www.ph.tn.tudelft.nl/~pavel/ Dr. Mark Burge:machine learning and graph theory http://cs.armstrong.edu/burge/ yalin Wang:Document Image Analysis http://students.washington.edu/~ylwang/ Geir Storvik: Image analysis http://www.math.uio.no/~geirs/ Heidorn http://alexia.lis.uiuc.edu/~heidorn/ Joakim Lindblad:Digital Image Cytometry http://www.cb.uu.se/~joakim/index_eng.html S.Lavirotte: http://www-sop.inria.fr/cafe/Stephane.Lavirotte/ Sporring: scale-space techniques http://www.lab3d.odont.ku.dk/~sporring/ Mark Jenkinson:Reduction of MR Artefacts http://www.fmrib.ox.ac.uk/~mark/ Justin K. Romberg:digital signal processing http://www-dsp.rice.edu/~jrom/ Fauqueur:Image retrieval by regions of interest http://www-rocq.inria.fr/~fauqueur/ James J. Nolan:Computer Vision http://cs.gmu.edu/~jnolan/ Daniel X. Pape:Information http://www.bucho.org/~dpape/ Drew Pilant:remote sensing technology http://www.geo.mtu.edu/~anpilant/index.html 五、前沿期刊(TOP10) 这里的期刊大部分都可以通过上面的大拿们的主页间接找到，在这列出主要是为了节省直接想找期刊投稿的兄弟的时间:) IEEE Trans. On PAMI http://www.computer.org/tpami/index.htm IEEE Transactionson Image Processing http://www.ieee.org/organizations/pubs/transactions/tip.htm Pattern Recognition http://www.elsevier.com/locate/issn/00313203 Pattern Recognition Letters http://www.elsevier.com/locate/issn/01678655 神经网络 Neural Networks Tutorial Review http://hem.hj.se/~de96klda/NeuralNetworks.htm ftp://ftp.sas.com/pub/neural/FAQ.html Image Compression with Neural Networks http://www.comp.glam.ac.uk/digimaging/neural.htm Backpropagator's Review http://www.dontveter.com/bpr/bpr.html Bibliographies on Neural Networks http://liinwww.ira.uka.de/bibliography/Neural/ Intelligent Motion Control with an Artificial Cerebellum http://www.q12.org/phd.html Kernel Machines http://www.kernel-machines.org/ Some Neural Networks Research Organizations http://www.ieee.org/nnc/ http://www.inns.org/ Neural Network Modeling in Vision Research http://www.rybak-et-al.net/nisms.html Neural Networks and Machine Learning http://learning.cs.toronto.edu/ Neural Application Software http://attrasoft.com Neural Network Toolbox for MATLAB http://www.mathworks.com/products/neuralnet/ Netlab Software http://www.ncrg.aston.ac.uk/netlab/ Kunama Systems Limited http://www.kunama.co.uk/ Computer Vision Computer Vision Homepage, Carnegie Mellon University www.cs.cmu.edu/~cil/vision.html Annotated Computer Vision Bibliography http://iris.usc.edu/Vision-Notes/bibliography/contents.html http://iris.usc.edu/Vision-Notes/rosenfeld/contents.html Lawrence Berkeley National Lab Computer Vision and Robotics Applications http://www-itg.lbl.gov/ITG.hm.pg.docs/VISIon/vision.html CVonline by University of Edinburgh The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision, www.dai.ed.ac.uk/CVonline Computer Vision Handbook, www.cs.hmc.edu/~fleck/computer-vision-handbook Vision Systems Courseware www.cs.cf.ac.uk/Dave/Vision_lecture/Vision_lecture_caller.html Research Activities in Computer Vision http://www-syntim.inria.fr/syntim/analyse/index-eng.html Vision Systems Acronyms www.vision-systems-design.com/vsd/archive/acronyms.html Dictionary of Terms in Human and Animal Vision http://cns-web.bu.edu/pub/laliden/WWW/Visionary/Visionary.html Metrology based on Computer Vision www.cranfield.ac.uk/sme/amac/research/metrology/metrology.html Digital Photography Digital Photography, Scanning, and Image Processing www.dbusch.com/scanners/scanners.html Educational Resources, Universities Center for Image Processing in Education www.cipe.com Library of Congress Call Numbers Related to Imaging Science by Rochester Institute of Technology http://wally2.rit.edu/pubs/guides/imagingcall.html Mathematical Experiences through Image Processing, University of Washington www.cs.washington.edu/research/metip/metip.html Vismod Tech Reports and Publications, MIT http://vismod.www.media.mit.edu/cgi-bin/tr_pagemaker Vision Lab PhD dissertation list, University of Antwerp http://wcc.ruca.ua.ac.be/~visielab/theses.html INRIA (France) Research Projects: Human-Computer Interaction, Image Processing, Data Management, Knowledge Systems www.inria.fr/Themes/Theme3-eng.html Image Processing Resources http://eleceng.ukc.ac.uk/~rls3/Contents.htm Publications of Carsten Steger http://www9.informatik.tu-muenchen.de/people/steger/publications.html FAQs comp.dsp FAQ www.bdti.com/faq/dsp_faq.htm Robotics FAQ www.frc.ri.cmu.edu/robotics-faq Where's the sci.image.processing FAQ? www.cc.iastate.edu/olc_answers/packages/graphics/sci.image.processing.faq.html comp.graphics.algorithms FAQ, Section 3, 2D Image/Pixel Computations www.exaflop.org/docs/cgafaq Astronomical Image Processing System FAQ www.cv.nrao.edu/aips/aips_faq.html
GuiLin|China MIPPR | Nav: Registration | Conference | Travel/General | Accepted/Rejected +Abstracts Submission +Paper Submission +Accepted/Rejected +Conferences +Travel/General +Committees +Investment Cooperation +Contact In 2009,the sixth International Symposium on Multispectral Image Processing and Pattern Recognition was held in Yichang, China.This symposium was a great success. Scientists, engineers, and graduate students from more than 20 countries presented over 280 talks conveying research results in image processing and pattern recognition. 697 papers were selected for publication in SPIE proccedings. After that,the seventh SPIE International Symposium on Multispectral Image Processing and Pattern Recogniton(MIPPR'2011) will be held on November 4，2011 in the historical city of Guilin,China. Guilin is in the north of Guangxi.The population in the city of Guilin is more than six hundred thousand. There are famous and beautiful sceneries in Guilin,such as Li River, Elephant Trunk Hill and so on. The conference will bring together scientists,professoes,engineers and graduate students in this field and provide a forum for presentation,exchange and discussion of recent advances in theory,techniques,algorithms and applications in Multisepctral Image Processing and Pattern Recognition.In order to better inform the participants of the latest developments in the different branches of multispectral image processing and pattern recognition,apart from general papers to be presented at the forthcoming conference,we shall organize several special plenary sections for top quality invited papers. Sponsored by National Key Laboratory of Science and Technology on Multi-spectral Information Processing (China) Huazhong University of Science and Technology (China) SPIE- The international society advancing light-based research (USA) Guilin University of Electronic Technology (China) Symposium Honorary Chair: Bo Zhang, Tsinghua University (China) Symposium Chair: M.V.Srinivasan, University of Queensland (Australia) Deren Li,Wuhan University (China) Porgram Committee Chairs: Bir Bhanu, The University of California at Riverside (USA) Supported by: National Natural Sci. Foundation of China Education Ministry of China Proceedings Publisher: SPIE an international society advancing an interdisciplinary approach to the science and application of light.(USA) Organizing Committee Chair: Jianguo Liu,Huazhong University of Science and Technology (China) Co-Chairs: Jinxue Wang, SPIE (USA) General Secretary: Faxiong Zhang ,Huazhong University of Science and Technology (China) Asociate General Secretary: Wenwen Gu, Huazhong University of Science and Technology (China) what's new? | submit site | search | about us | gbook | Style Like Exp... ImageProcessing Art. IPRAI 2011 All Right Reserved. Designed and Programed by Chengzhao 会议网址： http://iprai.hust.edu.cn/mippr/default.html
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