PPT-Feature descriptors and matching

Author : paige | Published Date : 2023-11-12

Basic correspondence Image patch as descriptor NCC as similarity Invariant to Photometric transformations Translation Rotation Scaling Find dominant orientation

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Feature descriptors and matching: Transcript

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 . 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?. 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. = . Image from http://graphics.cs.cmu.edu/courses/15-463/2010_fall/. Robust feature-based alignment. So far, we’ve assumed that we are given a set of “ground-truth” correspondences between the two images we want to align. Eric Brenner. Paul Carpenter. Daniel Ehrenberg. Aaron McCarty. Travis Raines. Advised by Jeff . Ondich. Defining the Problem. What is optical character recognition (OCR)?. Input: an image of some text. CS 6350 3 types of descriptors. :. SIFT / PCA-SIFT . (. Ke. , . Sukthankar. ). GLOH . (. Mikolajczyk. , . Schmid. ). DAISY . (. Tola. , et al, Winder, et al). Comparison of descriptors . (. Mikolajczyk. Image Processing. Pier Luigi Mazzeo. pierluigi.mazzeo@cnr.it. Image Rotation &. Object . Detection . Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. 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. Kenton McHenry, Ph.D.. Research Scientist. Raster Images. 0.92. 0.93. 0.94. 0.97. 0.62. 0.37. 0.85. 0.97. 0.93. 0.92. 0.99. 0.95. 0.89. 0.82. 0.89. 0.56. 0.31. 0.75. 0.92. 0.81. 0.95. 0.91. 0.89. 0.72. F. eature . T. ransform. David Lowe. Scale/rotation invariant. Currently best known feature descriptor. A. pplications. Object recognition, Robot localization. Example I: mosaicking. Using SIFT features we match the different images. ch. 7) &. Image Matching (. ch. 13). ch.. 7 and . ch.. 13 of . Machine Vision. by Wesley E. Snyder & . Hairong. Qi. Mathematical Morphology. The study of shape…. Using Set Theory. Most easily understood for binary images.. Computer Vision. Jia-Bin Huang, Virginia Tech. Many slides from D. Hoiem. Administrative Stuffs. HW 1 due 11:55 PM Sept 17. Submission through Canvas. HW 1 Competition: Edge Detection. Submission link. Li, Mark Drew. School of Computing Science, . Simon . Fraser University, . Vancouver. , B.C., Canada. {zza27, . li. , mark}@. cs.sfu.ca. Learning Image Similarities via Probabilistic Feature Matching.

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