PPT-Wavelet Applications for Rendering

Speaker Yung Chum Lu Outline Introduction of Rendering Application I Application II Review Haar Something Interesting Rendering Story Board Create a 2D picture of a 3D world Ray Tracing. William Chen. Eco-informatics Summer Institute. 22 August 2013. 1. Goal. To create an informed set of wavelet data that may be quickly analyzed by scientists working on Fish-ELJ data.. We want to determine where fish like to reside near a log jam, but first we need to figure where the distribution of energy around a log jam. Wavelet analysis can help in this respect.. Michael Phipps. Vallary. . S.Bhopatkar. Discrete wavelet transform(DWT) is fast linear operation that operates . on a data vector whose length is an integer . power of . 2, transforming it into a numerically different vector of the same length. Denoising. Based on following articles:. Hyvarinen. at al, “Image . Denoising. by Sparse Code Shrinkage”. Sendur. and . Selesnick. , “. Bivariate. Shrinkage Functions for Wavelet-Based . Denoising. Corrinne Yu. Halo team Principal engine programmer. Corrinne.Yu@microsoft.com. Zen of multi core rendering. Take away. Compilation and survey of effective rendering techniques for current generation multi core console hardware . S. S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. Graphics. CMSC 435/634. 1. Graphics Areas. “. Core. ”. graphics areas. Modeling. Rendering. Animation. Other areas which draw on computer graphics. User Interaction. Virtual Reality. Visualization. (Section 13.10.6-13.10.8). Michael Phipps. Vallary. S. . Bhopatkar. The most useful thing about wavelet transform is that it can turned into sparse expansion i.e. it can be truncated. Truncated Wavelet Approximation. S. S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. Michael Phipps. Vallary. S. . Bhopatkar. The most useful thing about wavelet transform is that it can turned into sparse expansion i.e. it can be truncated. Truncated Wavelet Approximation. Arbitrary chosen . S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. Overview. This paper was published in the journal . Signal Processing: Image Communication, EURASIP, 2005 . . Goal of the method proposed. Goal. . . Early detection of fire before it spreads around.. Why?. . Presenter : Ke-Jie Liao. NTU,GICE,DISP Lab,MD531. An Introduction to Discrete Wavelet Transforms. 1. Introduction. Continuous Wavelet Transforms. Multiresolution Analysis Backgrounds. Image Pyramids. 1. Graphics Areas. “. Core. ”. graphics areas. Modeling. Rendering. Animation. Other areas which draw on computer graphics. User Interaction. Virtual Reality. Visualization. Image Processing. 3D Scanning. Chen Bar, . Ioannis. . Gkioulekas. and . Anat. Levin. Technion. , Israel. CMU, USA. Speckle image. Coherent scattering and memory effect . 2. Tissue. Microscope objective. Speckle Image. Scattering Layer.