The Hilbert Problems of Computer Vision

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Presentation transcript:

The Hilbert Problems of Computer Vision Jitendra Malik

Forty years of computer vision 1963-2003 1960s: Beginnings in artificial intelligence, image processing and pattern recognition 1970s: Foundational work on image formation: Horn, Koenderink, Longuet-Higgins … 1980s: Vision as applied mathematics: geometry, multi-scale analysis, control theory, optimization … 1990s: Geometric analysis largely completed Probabilistic/Learning approaches in full swing Successful applications in graphics, biometrics, HCI …

And now … Back to basics: the classic problem of understanding the scene from its image/s Central question: Interplay of bottom-up and top-down information

Early Vision What can we learn from image statistics that we didn't know already? How far can bottom-up image segmentation go? How do we make inferences from shading and texture patterns in natural images?

Static Scene Understanding What is the interaction between segmentation and recognition? What is the interaction between scenes, objects, and parts? What is the role of design vs. learning in recognition systems?

Dynamic Scene Understanding What is the role of high-level knowledge in long range motion correspondence? How do we find and track articulated structures? How do we represent "movemes" and actions?

From Images to Objects "I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of colour. Do I have "327"? No. I have sky, house, and trees." --Max Wertheimer