PPT-Local features and image matching

Devi Parikh Disclaimer Many slides have been borrowed from Kristen Grauman who may have borrowed some of them from others Any time a slide did not already have a credit on it I have credited it to Kristen So there is a chance some of these credits are inaccurate. Yingen Xiong . and . Kari . Pulli. . Download our panorama software : . http://store.ovi.com/content/51491. . Outline. Introduction. What is the problem? Why do we need color correction?. Related work. Wireless Networks. Tuan Dao. , . Amit. K. Roy-. Chowdhury. , . Srikanth. V. Krishnamurthy . U.C. Riverside. Harsha. V. . Madhyastha. University of Michigan. Tom La . Porta. Penn State University. Premise. 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. Josef . Sivic. http://. www.di.ens.fr. /~josef. INRIA, . WILLOW, ENS/INRIA/CNRS UMR 8548. Laboratoire. . d’Informatique. , . Ecole. . Normale. . Supérieure. , Paris. With slides from: O. Chum, K. . Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. Oscar . Danielsson. (osda02@csc.kth.se). Stefan . Carlsson. (. stefanc@csc.kth.se. ). Josephine Sullivan (. sullivan@csc.kth.se. ). DICTA08. The Problem. Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features . Monday March . 7. Prof. Kristen . Grauman. UT-Austin. Midterm Wed.. Covers material up until 3/1. Solutions to practice exam handed out today. Bring a 8.5”x11” sheet of notes if you want. Review the outlines and notes on course website, accompanying reading in textbook. Network to Compare Image Patches. Jure . Zbontar. , Yann . LeCun. Background. Motivation. Problem Formulation. Methodology. Training Data. Suggested Net Architectures. Sequential Steps. Results. Conclusion. Principle Component Analysis. (PCA. ). . Jiali. . zhang. , . X. iaohong. . Liu . MS Statistics Student. SAN JOSE STATE UNIVERSITY . 12/10/2015. T. he . D. efinition of Image . 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.. Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. Find a bottle:. 4. Categories. Instances. Find these two objects. Can’t do. unless you do not . care about few errors…. Can nail it. Building a Panorama. M. Brown and D. G. Low. e. . Recognising Panorama. Computer Vision, FCUP, . 2018/19. Miguel Coimbra. Slides by Prof. Kristen . Grauman. Today. Local . invariant . features. Detection of interest points. (Harris corner detection). Scale invariant blob detection: . Palm Prints. Features of Palm or Palm print. Palm contains . Three flexion creases (principal lines : heart line, head line, life line) . Secondary creases (wrinkles). Ridges . Regions : Finger root (I), inside region (II) and outside region (III).