Mr. Yalu Wu Profile

Yalu Wu

at Nanjing University of Science and Technology

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Proceedings Article | 14 December 2015 Paper

Infrared image recognition based on structure sparse and atomic sparse parallel

Yalu Wu, Ruilong Li, Yi Xu, Liping Wang

Proceedings Volume 9813, 98130J (2015) https://doi.org/10.1117/12.2203656

KEYWORDS: Associative arrays, Detection and tracking algorithms, Signal processing, Infrared imaging, Infrared radiation, Chemical species, Image classification, Computer simulations, Optimization (mathematics), Parallel computing

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Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method.

Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.

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