1. 1 Computer Vision Research  Huttenlocher, Zabih –Recognition, stereopsis, restoration, learning  Strong algorithmic focus –Combinatorial optimization –Geometric algorithms  Application areas –Techniques we developed have Played important role at Xerox and Microsoft Resulted in successful startups –Medical imaging Zabih joint with Radiology department in NYC

2. 2 Markov Random Fields  Many computer vision problems can be formalized using Markov random fields –Set of sites and neighborhood system –Estimate label for each site accounting for Goodness of fit of label to observed data at site Consistency of label with neighbors  MRF’s are undirected graphical models –Probabilistic relational models (directed)  Until recently a formalism used in computer vision, but not very practical

3. 3 Example MRF Problems  Stereopsis –For an image pair, estimate depth at each pixel Sites are pixels, neighbors are 4-connected grid, labels are depths  Object recognition –For an image, estimate location of a multi-part flexible object Sites are parts, neighbors are connected parts, labels are locations

4. 4 MRF Algorithms  Underlying graph G=(S,N) –For tree-structure can solve exactly using variant of Viterbi recurrence But impractical for large label set –For two labels, can solve exactly using min-cut –For three or more labels and grid-graph problem is NP hard  Recent algorithmic progress –For grid graphs, good approximation methods –For low tree-width graphs, exact methods even for large label sets

5. 5 Alpha Expansion Technique [BVZ99]  Use min-cut to efficiently solve a special two label problem –Labels “stay the same” or “replace with ”  Iterate over possible values of  –Each rules out exponentially many labelings Red expansion move from x Input labeling x

6. 6 Graph Cuts for MRF’s on Grid  Best stereo algorithms use alpha expansion technique –Middlebury stereo benchmark  Beyond computer vision: many image compositing, restoration, editing tasks –E.g., SIGGRAPH, Microsoft Ground TruthCorrelationAlpha Expansion

7. 7 Tree-Like MRF’s  Object recognition –Nodes are parts, labels are locations  Small graph, not at all grid-like –Many labels (millions or more)  Viterbi algorithm for trees –Still not practical because O(m 2 n) for n parts and m locations per part –Fast min convolution techniques make finding best labeling O(mn)  More generally for fan-like graphs

8. 8 Fan Structured Models [CFH05]  K-fan, let R  S be a set of reference parts –And R’=S-R be the remaining parts –Complete graph on R and complete bipartite graph on R,R’  Parts local image patches –Probability of (oriented) edge at each pixel

9. 9 Models (Weakly Supervised) Car (Rear) 1-fan Motorbike 2-fan Face 1-fan Training examples only labeled as positive/negative

10. 10 Recognition Results  High detection accuracy –Motorbikes 98.6%, Faces 98.2%, Cars 94.4%, Planes 95.0%  Fast running time –Approx. 2 sec. per image, 2 fans  Exact (global) method for computing highest probability configuration of parts for given image –No approximations or local search techniques  Single overall optimization problem –Does not depend on “feature detection”