Open AccessProceedings Article
Fast image alignment using anytime algorithms
Rupert Brooks,Tal Arbel,Doina Precup +2 more
- 06 Jan 2007
- pp 2078-2083
TL;DR: This paper presents anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information, and achieves comparable quality and robustness with significantly less computation.
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Abstract: Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often guided by similarity measures 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 can use a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. This makes the problem of image alignment a natural application domain for deliberation control using anytime algorithms. However, almost no research has been done in this direction. 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.
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Citations
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- 26 Sep 2014
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Analysing solution quality of anytime Bag of Words object classification for a service robot
Alex Lotz,Andreas Steck,Christian Schlegel +2 more
- 23 Apr 2012
TL;DR: This paper revises some theoretical assumptions according to the needs in a tangible object classification scenario and shows an application-related mechanism that facilitates a service robot to not only be aware of but also to actively manage its resources.
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A Survey of Usage of Anytime Algorithm in Fault detection in Cloud Systems
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TL;DR: In this paper , the authors investigate the utilization of the anytime algorithm in cloud-based fault detection techniques, which are real-time decision-making algorithms that focus on time-deliberation.
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