Lecture-1 - Computer Vision Story Mubarak Shah...

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Unformatted text preview: Computer Vision Story Mubarak Shah http://www.cs.ucf.edu/courses/cap6412/2003/Lecture-1.pdf Computer Vision • Computer Vision deals with recovery and use of information about objects present in a scene from images of the scene. Computer Vision • Computer Vision emerged from: – Image Processing – Pattern Recognition Computer Vision • Computer Vision started as an AI problem. AI • Artificial Intelligence is the study of mental faculties through the use of computational models. – Search – NLU – Speech Recognition – Games – Computer Vision – Expert Systems Image Understanding • To understand a single image of a scene, locate and identify objects, their structure, and spatial arrangements, and relationships with other objects. Different Levels • Low Level: Extraction of symbolic information • Intermediate Level • High Level: Interpretation High Level Vision • Image Understanding • Scene Interpretation • Line Drawings Interpretation of Line Drawing MIT Copy Demo What happened? • In order to do line interpretation, need to extract lines from images – Horn-Binford line finder – Solve low level problems before high level problems can be solved. Horn: Physics Based Vision • Optics • Reflectance • Illumination Marr Approach • Human vision system • Shape from X: Recover 3-D from 2-D • Quantitative vs Qualitative Shape from X • Shading • Stereo • Texture • Motion • Contours Shape from Texture Shape from Shading Shape from Stereo Marr’s Three Levels • Primal Sketch – Marr-Hildreth edge detector • 2.5 Sketch – Marr-Poggio stereo algorithm – Grimson’s stereo algorithm – Ullman’s structure from motion – Pentland, Witkin, Kass, – Terzopoulos: surface reconstruction • 3-D – Generalized Cylinders: Nishihara After 30 Years of Research • Stereo is almost a solved problem • Structure from motion is very hard • Shape from shading is not interesting/applicable • Range images did not help much • Not much progress in understanding/recognition/interpretation Motion-Based Recognition • A longer sequence leads to recognition of higher level motions, like walking or running,which consist of a complex and coordinated series of events that cannot be understood by looking at only a few frames. – 3-D is not necessary for recognition – Use motion directly for recognition vs • Recognition followed by reconstruction Video Understanding • Gestures • Activities • Facial expressions • Visual Speech • Applications – Video Surveillance and Monitoring – Perceptual User Interface – Model-based Video Compression – Augmented Reality and Video Games – Synthesis of Video Sequences Copy Demo Using A Video Sequence: [bread, lettuce, ham, bread] Making a Sandwich A picture is worth a thousand words. A word is worth a thousand pictures....
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This note was uploaded on 10/04/2011 for the course CAP 6411 taught by Professor Shah during the Spring '09 term at University of Central Florida.

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Lecture-1 - Computer Vision Story Mubarak Shah...

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