Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
The combinatorics of local constraints in model-based recognition and localization from sparse data
Journal of the ACM (JACM)
Sorting Jordan sequences in linear time using level-linked search trees
Information and Control
Determining the shape of a convex n-sided polygon by using 2n+k tactile probes
Information Processing Letters
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms in combinatorial geometry
Algorithms in combinatorial geometry
Simplified linear-time Jordan sorting and polygon clipping
Information Processing Letters
An optimal algorithm for intersecting line segments in the plane
Journal of the ACM (JACM)
Model-based probing strategies for convex polygons
Computational Geometry: Theory and Applications
ACM Computing Surveys (CSUR)
Almost optimal set covers in finite VC-dimension: (preliminary version)
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric decision trees for optical character recognition (extended abstract)
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
Surface approximation and geometric partitions
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Theoretical Computer Science - Special issue: Algorithmic learning theory
Adaptive submodularity: theory and applications in active learning and stochastic optimization
Journal of Artificial Intelligence Research
Efficient active learning of halfspaces: an aggressive approach
The Journal of Machine Learning Research
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A fundamental problem in model-based computer vision is that ofidentifying which of a given set of geometric models is present in animage. Considering a “probe” to be an oracle that tells uswhether or not a model is present at a given point, we study the problemof computing efficient strategies (“decision trees”) forprobing an image, with the goal to minimize the number of probesnecessary (in the worst case) to determine which single model ispresent. We show that a lgk height binary decision tree always exists fork polygonal models (in fixedposition), provided (1) they are non-degenerate (do not shareboundaries) and (2) they share a common point of intersection. Further,we give an efficient algorithm for constructing such decision treeswhen the models are given as a set of polygons in the plane. We showthat constructing a minimum height tree is NP-complete if either of thetwo assumptions is omitted. We provide an efficient greedy heuristicstrategy and show that, in the general case, it yields a decision treewhose height is at most lgn times that of an optimal tree. Finally,we discuss some restricted cases whose special structure allows forimproved results.