A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
Primal-dual methods for vertex and facet enumeration (preliminary version)
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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In this paper, we propose a general-purpose methodology for detecting multiple objects with known visual models from multiple views. The proposed method is based Monte-Carlo sampling and weighted mean-shift clustering, and can make use of any model-based likelihood (color, edges, etc.), with an arbitrary camera setup. In particular, we propose an algorithm for automatic computation of the feasible state-space volume, where the particle set is uniformly initialized. We demonstrate the effectiveness of the method through simulated and real-world application examples.