PPT-Feature descriptors and matching

Basic correspondence Image patch as descriptor NCC as similarity Invariant to Photometric transformations Translation Rotation Scaling Find dominant orientation of the image patch This is given by . Categorization. . With. . Bags. of . Keypoints. Original . Authors. :. G.. . Csurka. , C.R. Dance, L. Fan, . J. . Willamowski. , C. Bray. ECCV Workshop on . Statistical. Learning in Computer – 2004. CSE P 576. Larry Zitnick (. larryz@microsoft.com. ). 20,000 images of Rome. =. ?. Large scale matching. How do we match millions or billions of images in under a second?. Is it even possible to store the information necessary?. . local. image . descriptors. . into. . compact. . codes. Authors. :. Hervé. . Jegou. Florent. . Perroonnin. Matthijs. . Douze. Jorge. . Sánchez. Patrick . Pérez. Cordelia. Schmidt. Presented. Image Processing . Pier Luigi . Mazzeo. pierluigi.mazzeo@. cnr.it. Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. 1: Read . Image. original. = . Segment Descriptor. Segments are areas of memory defined by a programmer and can be a code, data or stack segment.. In 80386 segments need not be all the same size and aligned. And segments need not be exactly 64 KB long, but we can define them to be any length from 1 byte to 4 GB.. Matthew Brown. University of British Columbia. (prev.) Microsoft Research. [ Collaborators: . †. Simon Winder, *Gang . Hua. , . †. Rick . Szeliski. . †. =MS Research, *=MS Live Labs]. Applications @MSFT. vision. Applications and Algorithms in CV. Tutorial . 9:. Descriptors. Visual Descriptors. Motivation::. Scene Classification. I. ntroduction. How to differentiate between scenes?. Applications and Algorithms in CV. Detection: . introduction. Approaches. Holistic detection: use local search window that meets . criterias. Part-based detection: pedestrian as a collection of parts (to be found!). Patch-based detection: local features matched against a (learned) codebook, then voting for final detection. 3 types of descriptors. :. SIFT / PCA-SIFT . (. Ke. , . Sukthankar. ). GLOH . (. Mikolajczyk. , . Schmid. ). DAISY . (. Tola. , et al, Winder, et al). Comparison of descriptors . (. Mikolajczyk. CS5670: Computer Vision. Noah Snavely. Reading. Szeliski: 4.1. Announcements. Project 1 Artifacts due tomorrow, Friday 2/17, at 11:59pm. Project 2 will be released next week. In-class quiz at the beginning of class Thursday. CS5670: Computer Vision. Noah Snavely. Reading. Szeliski: 4.1. Announcements. Project 1 artifact voting online shortly. Project 2 to be released soon. Quiz at the beginning of class today. Local features: main components. Samantha Horvath. Learning Based Methods in Vision. 2/14/2012. Introduction. Computer vision makes use of many “hand-crafted” descriptors.. These descriptors share many common components. This paper presents a modular framework for designing and optimizing new feature descriptors . Statement of the problem. Two sides of the market to be . matched.. Participants . on . both sides care about to whom they are matched.. M. oney can’t . be used to . determine . the assignment. .. Examples . the Classroom Environment . and Culture. . and . Professional . Collaboration . and . Communication . Dimensions of 5D . Norms for Learning. Talk. Listen. Share ideas. Respect opinions and ideas shared.