PPT-Introduction to object recognition

Slides adapted from FeiFei Li Rob Fergus Antonio Torralba and others Overview Basic recognition tasks A statistical learning approach Traditional or shallow recognition pipeline. Otherwise backtrack brPage 11br Interpretation Trees for FeatureBased Identificati on Verification of hypothetical interpretations Verification brPage 12br Interpretation Trees for FeatureBased Identificati on Partial misleading or spurious features 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. Automated Feature Extraction and Target Recognition. Speaker:. . Yi-Chun . Ke. Adviser:. . Bo-Chi Lai. outline. Introduction. Method. conclusion. Introduction. computational models of biological vision and learning. 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 . 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 . Introduction. History. Modern Applications. Case Study. Ethical Analysis. Overview. Voice recognition . Speech recognition . -. converts . spoken words to text. The term "voice recognition" is sometimes used to refer to recognition systems that must be trained to a particular . 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 .