Geometric invariance in computer vision
Geometric invariance in computer vision
International Journal of Computer Vision
Planar object recognition using projective shape representation
International Journal of Computer Vision
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Canonical Frames for Planar Object Recognition
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Shape matching of partially occluded curves invariant under projective transformation
Computer Vision and Image Understanding
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Novel concepts and challenges for the next generation of video surveillance systems
Machine Vision and Applications
Estimating correspondence between multiple cameras using joint invariants
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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An efficient method based on projective joint invariant signatures is presented for distributed matching of curves in a camera network. The fundamental projective joint invariants for curves in the real projective space are the volume cross-ratios. A curve in m-dimensional projective space is represented by a signature manifold comprising n-point projective joint invariants, where n is at least m + 2. The signature manifold can be used to establish equivalence of two curves in projective space. However, without correspondence between the two curves, matching signature manifolds is a computational challenge. In this paper we overcome this challenge by finding discriminative sections of signature manifolds consistently across varying viewpoints and scoring the similarity between these sections. This motivates a simple yet powerful method for distributed curve matching in a camera network. Experimental results with real data demonstrate the classification performance of the proposed algorithm with respect to the size of the sections of the invariant signature in various noisy conditions.