PPT-Shape matching and object recognition using shape contexts

Serge Bolongie Jitendra Malik Jan Puzicha Presenter Neha Raste 1 Outline Introduction Background Algorithm Explanation Results and Discussion 2 Introduction Shape Context . We present two algorithms for rapid shape retrieval representative shape contexts performing comparisons based on a small number of shape contexts and shapemes using vector quantization in the space of shape contexts to obtain prototypical shape p Neural mechanisms of object recognition Logothetis et al. RESEARCH PAPER 553simulations showing that a simple artificial networkcan, in principle, recognize three-dimensional objects byinterpolating b Object Segmentation. Jaechul Kim and Kristen . Grauman. University of Texas at Austin. Problem statement. Category-independent object segmentation: . Generate object segments in the image . regardless of their categories.. 1. Make a class abstract so instances of it cannot be created.. Make a method abstract so it must be overridden.. An interface is like an abstract class whose methods are all abstract and whose fields are all public constants. This allows multiple inheritance without ambiguity. An interface has a different syntax and a different way of using it.. Unit 1 Psychology – . Visual Perception. What are perceptual constancies?. In vision, perceptual constancy refers to the tendency to perceive an object as remaining stable and unchanging (‘constant’) despite any changes that may occur to the image cast on the retina. . Martin G. ö. rg and Jianjun Zhao. Computer Science Department, Shanghai Jiao Tong University. Outline. Motivation and . Background. Difference Analysis Algorithm. Evaluation of . Quality and Feasibilit. 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 . from Fourier to Wavelets. Ming . Zhong. 2012.9. Overview (1). Harmonic analysis basics. Represent signals as the linear combination of basic overlapping, wave-like functions. Natural domain (space/time). David Meredith. dave@create.aau.dk. Source. Chapter 22 of. Shiffman. , D. (2008). . Learning Processing. . Morgan Kaufmann, Burlington, MA.. Introduction. Remember objects and classes?. An object contains data in variables and has its own functions, called . towards functionality. Ruizhen. Hu. 1. . . Oliver van Kaick. 2. Youyi. Zheng. 3. . . Manolis. Savva. 4. 2. . Carleton University. 1 . Shenzhen University. 3. . ShanghaiTech. 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. A . shape is an element of art. Specifically, . it . is an . enclosed space. , the boundaries of . which . are defined by other elements of . art. Shape. Types of Shape. Geometric: Shapes that have specific rules. LO: Use the features of Shape Poetry.. Success Criteria:. Know that a shape poem describes an object, person or animal and the words of the poem form the shape of the object, person or animal being described..