Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Elements of information theory
Elements of information theory
A survey of image registration techniques
ACM Computing Surveys (CSUR)
SIAM Journal on Scientific Computing
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational tradeoffs under bounded resources
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Tracking brain deformations in time sequences of 3D US images
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Scheduling of Image Processing Using Anytime Algorithm for Real-time System
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Guest Editorial: Similarity Matching in Computer Vision and Multimedia
Computer Vision and Image Understanding
Use of random time-intervals (RTIs) generation for biometric verification
Pattern Recognition
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Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.