PPT-Parallel Image Processing

Mohammadhossein Behgam Agenda Need for parallelism Challenges Image processing algorithms Data handling amp Load Balancing Communication cost amp performance What is the problem Image Processing applications can be very computationally demanding due to. Overview. Images. Pixel Filters. Neighborhood Filters. Dithering. Image as a Function. We can think of an . image . as a function, . f. , . f:. . R. 2. . . . R. f . (. x, y. ). . gives the . intensity. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1. STUDY AND IMPLEMENTATION OF POPULAR PARALLELING TECHNIQUES APPLIED TO HEVC. Unde. r the guidance of. Dr. K. R. . Rao. By:. Karthik Suresh . (1000880819). Overview. HEVC. Improvements . Need for parallel processing. Processing & Machine Vision. By:. Dr. Rajeev Srivastava. Associate Professor, CSE, IIT(BHU), Varanasi. Fundamentals of . Digital Image Processing. • Applications of image processing. • What's an image?. Thomas Sangild Sørensen. Course overview. Department of Computer Science. Introduction . to computer graphics and image processing (Q1). Data-parallel computing (Q1). Advanced image processing (Q2). Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Overview. What’s in the toolboxes. Learn basic techniques to use them. Using MATLAB’s built in help and demos. Setup. Log in to your machines. Start MATLAB. Open a web browser and type in:. http://tinyurl.com/uta-tut. Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Mid-point feedback. Are you learning from the papers we are reading?. Do you find class discussions helpful?. Does preparing for the class presentation help? . Eric Rees, Clemens Kaminski, Miklos Erdelyi, Dan Metcalf, Alex Knight. Laser Analytics Group. , . University of Cambridge . & . Biotechnology Group. , . National Physical Laboratory. Contents. Introduction. Inside a Processor. Chip in Package. Circuits. Primarily Crystalline Silicon. 1 mm – 25 mm on a side. 100 million to billions of transistors. current “feature size” . (process). ~ 14 nanometers. 1 INTRODUCTIONImage processing is the frequently used technique in vast areas like medical image astronomical data analysisand so on The collection of images is increased to petabytes in last few Mor . soffer. Instructors: prof. Naftali . Lazarovitch. , prof. . Ofer. . Hadar. Schedule. Introduction and motivation. Our method. Data collection . Data description. Proposed method. Results and conclusions. Recognition Vol.7, No. 2 (2014), pp. 3 89 - 3 9 8 http://dx.d oi.org/10.14257/ijsip.2014.7. 2 . 3 6 ISSN: 2005 - 4254 IJSIP Copyright ⓒ 201 4 SERSC Extraction of Optic Cup in Retinal Fundus image