PPT-Silhouette-based Object Phenotype Recognition using 3D Shap

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. e of Wheat Kernels Based on Fourier Describer Wei Xiao 1 , Qinghai Li 1,2 , Longzhe Quan 3 * 1 Wenzhou Vocational & Technical College . 325035 Wenzhou , P.R.China 2 Zhejiang industry&Trade Vo Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. Painting shadows and sunsets. What are silhouettes?. Silhouettes are objects that are fully black like a shadow.. When creating a silhouette, you only draw the outline of an object and then color it in black.. 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 . Presenter: Ilya Afanasyev. . . . Facoltà di Ingegneria. Trento, 2010. 14/12/2010. 1. /20. Superquadrics . (. SQ. ). A . superquadric surface can be defined by the 3D . vector. :. - are parameters of shape squareness;. Silhouette . Backlighting. when bright lights show behind a subject. it obscures subjects completely (similar to a silhouette). IT IS BAD. Backlighting. http://. www.youtube.com/watch?v. =. gxetoSmzVBM&safety_mode. and plant phenotypes. George . Gkoutos. Phenotype And Trait Ontology (PATO). The meaningful cross . species and across domain . translation of phenotype is essential .  . phenotype-driven gene function discovery and comparative pathobiology . “One of my earliest memories involves sitting on my dad’s lap in his studio in the garage of our house and watching him draw. I remember thinking: ‘I want to do that, too,’ and I pretty much decided then and there at age 2½ or 3 that I was an artist just like Dad.”. 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. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . 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. . Mukhopadhyay. Dept. of Computer Science and Engineering,. Indian Institute of Technology, . Kharagpur. 1. Collaborators. Dr. . Aditi. Roy. Prof. . Shamik. . Sural. 2. Motivation. Surveillance. works even at low resolution from a distance.. Overview. Sachin. . Ghanekar. Agenda. A brief Overview & History of Digital Audio. Basic Concepts of Object Audio & How it works. Signal Processing in Object Based Audio on Headphones.. Signal Processing in Object Based Audio on Immersive Speaker Layouts.