The Hilbert Problems of Computer Vision Jitendra Malik University of California Berkeley Computer Vision Group.

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Transcript The Hilbert Problems of Computer Vision Jitendra Malik University of California Berkeley Computer Vision Group.

The Hilbert Problems of Computer Vision
Jitendra Malik
University of California
Berkeley
Computer Vision Group
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 …
University of California
Berkeley
Computer Vision Group
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
University of California
Berkeley
Computer Vision Group
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?
University of California
Berkeley
Computer Vision Group
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?
University of California
Berkeley
Computer Vision Group
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?
University of California
Berkeley
Computer Vision Group
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
University of California
Berkeley
Computer Vision Group