Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Probabilistic Modeling and Recognition of 3-D Objects
International Journal of Computer Vision
3D object recognition: Representation and matching
Statistics and Computing
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Estimation of Building Shape Using MCMC
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic model-based 3D object recognition by combining feature matching with tracking
Machine Vision and Applications
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Saliency-based automatic target detection in forward looking infrared images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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A robust and effective feature map integration method is presented for infrared (IR) target recognition. Noise in an IR image makes a target recognition system unstable in pose estimation and shape matching. A cooperative feature map binding under computational Gestalt theory shows robust shape matching properties in noisy conditions. The pose of a 3D target is estimated using a Markov Chain Monte Carlo (MCMC) method, a statistical global optimization tool where noise-robust shape matching is used. In addition, bottom-up information accelerates the recognition of 3D targets by providing initial values to the MCMC scheme. Experimental results show that cooperative feature map binding by analyzing spatial relationships has a crucial role in robust shape matching, which is statistically optimized using the MCMC framework.