PPT-Object Recognition Part 2

Authors Kobus Barnard Pinar Duygulu Nado de Freitas and David Forsyth Slides by Rong Zhang CSE 595 Words and Pictures Presentation Day 22411. CSE P 576. Larry Zitnick (. larryz@microsoft.com. ). Nov 23rd, 2001. Copyright © 2001, 2003, Andrew W. Moore. Support Vector Machines . Modified from the slides by Dr. Andrew W. Moore. http://www.cs.cmu.edu/~awm/tutorials. Weiqiang. . Ren. , Chong Wang, . Yanhua. Cheng, . Kaiqi. . Huang, . Tieniu. . Tan. {. wqren,cwang,yhcheng,kqhuang,tnt. }@nlpr.ia.ac.cn. Task2 : Classification + Localization. Task 2b: . Classification + localization . Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Yu Chen. 1 . Tae-. Kyun. Kim. 2. Roberto Cipolla. 1.  . University of Cambridge, Cambridge, UK. 1. Imperial College, London, UK. 2.  . Problem Description. Task: To identify the phenotype class of deformable objects.. Zhiyong Yang. Brain and Behavior Discovery Institute. James and Jean Culver Vision . Discovery Institute. Department of Ophthalmology. Georgia Regents University. April. . 4, 2013. Outline. A model of pattern recognition . Object Localization. Goal: detect the location of an object within an image. Fully supervised:. Training data labeled with object category and ground truth bounding boxes. Weakly supervised:. Only object category is known, no location info. Recognition(. 细粒度分类. ) . 沈志强. Datasets. . -- Caltech-UCSD Bird-200-2011. Number of categories: 200. Number of images: 11,788. Annotations per image: 15 Part Locations, 1 Bounding Box. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. 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. Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with .