PPT-Lecture 6: Feature matching

CS5670 Computer Vision Noah Snavely Reading Szeliski 41 Announcements Project 1 artifact voting online shortly Project 2 to be released soon Quiz at the beginning of class today Local features main components. Quater wavelength transformer matching its advantages and limitations Single stub matching technique and its special features brPage 2br Module 2 Transmission Lines Lecture 15 Impedance Matching using Transmission Line Impedance Matching Impedance : Mapping Vehicles in Visual Domain and Electronic Domain. Dong Li, . Zhixue. Lu. , . Tarun. Bansal. , . Erik Schilling and . Prasun. . Sinha. Department of Computer Science and Engineering. The Ohio State University. 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 Processing. Pier Luigi Mazzeo. pierluigi.mazzeo@cnr.it. Image Rotation &. Object . Detection . Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. Features. Outline. Autonomous object . counting. Speeded Up Robust Features. Proposed Algorithm. Feature Grid Vector. Feature Grid . Cluster. Feature Vector Formation and Classification. Implementation with Graphical User Interface. 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. Microsoft Corporate. ganghua@microsoft.com. Online Contextual Face Recognition: . Towards Large Scale Photo Tagging for Sharing. Photo sharing has become a main online social activity. FaceBook. receives 850 million photo uploads/month. : Mapping Vehicles in Visual Domain and Electronic Domain. Dong Li, . Zhixue. Lu. , . Tarun. Bansal. , . Erik Schilling and . Prasun. . Sinha. Department of Computer Science and Engineering. The Ohio State University. 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.. 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 . 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. Participate on Piazza. https://. piazza.com. /buffalo/fall2022/cse331/. Read the syllabus CAREFULLY!. No graded material will be handed back until . you pass the syllabus quiz!. Please do keep on asking Qs!.