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Conceptual and Experimental Vision. Introduction R.Bajcsy, S.Sastry and A.Yang Fall 2006. Introduction and plan for the course. We plan to follow the text : An Invitation to 3-D Vision by Yi Ma, Stefano Soatto,Jana Kosecka and S.S.Sastry.
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Conceptual and Experimental Vision Introduction R.Bajcsy, S.Sastry and A.Yang Fall 2006
Introduction and plan for the course • We plan to follow the text :An Invitation to 3-D Vision by Yi Ma, Stefano Soatto,Jana Kosecka and S.S.Sastry. • Plus some additional papers on real time, Active Vision. • Approximately every two weeks there will be a problem set and programming homework assignment • There will no midterm and final, but projects instead. Students are expected to participate in the class.
The proposed Syllabus • Week 1: Introduction • Week 2: Image formation : geometry, optics , Radiometry and error analysis • Week 3: Image primitives and correspondence • Week 4: Review of basic algebra and geometry • Week 5 :Epipolar geometry • Week 6: Camera calibration • Week 7: Structure from motion • Week 8: Optimization
Syllabus cont., • Week 9: Real Time Vision • Week 10: Visual feedback • Week 11: Active Vision • Week 12: Introduction to GPCA: Iterative methods • Week 13: Introduction to GPCA:Algebraic Methods • Week 14: Estimation and Segmentation of Hybrid Models, and Applications • Week 15:Projects
Our expectation • Through this course, students should acquire the ability to study computer vision through rigorous mathematical frameworks. • By the end of the course, students should be familiar with the history of computer vision, the start-of-the-art performance of current vision systems, and important open problems in the literature. • Experimentally, students should be able to setup a stereo camera system, evaluate its characteristics, calibrate it, and reconstruct motions of single and multiple objects.
What is Vision? • From the 3-D world to 2-D images: image formation (physics). • Domain of artistic reproduction (synthesis): painting, graphics. • From 2-D images to the 3-D world: image analysis and reconstruction (mathematical modeling, inference). • Domain of vision: biological (eye and brain) computational
What we will cover • Geometry • Stereo and 3D reconstruction • Matching and Registration • Segmentation • Real time considerations • Visual feedback and control • Error analysis of the sensor system
What we will not cover • Recognition • Learning • Tracking and video analysis • Low level analysis an graphics and Image Synthesis
Illusions for Prof. Ramachandran • http://psy.ucsd.edu/chip/video/Mot_Capt_LQ.rm