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Zhang Q.,Yanshan University | Hao K.,Yanshan University | Li H.,Yanshan University | Li H.,National Engineering Research Center For Equipment And Technology Of Csr
Guangxue Xuebao/Acta Optica Sinica | Year: 2014

In the light of underwater binocular image matching cannot satisfy the epipolar constraint of air, and the mismatching rate of underwater image processed by the scale invariant feature transform (SIFT) algorithm is high, we put forward an underwater feature matching algorithm based on curve constraint. Binocular camera should be calibrated, and some relevant parameters are obtained, as well as the reference image and the image to be matched; the SIFT feature matching algorithm can help to match two images, at the same time, the feature points can be extracted from the reference image to deduce the corresponding curve on the image to be matched. The curve is used as a constraint to determine whether the corresponding feature is on it, thus mismatching points will be excluded to achieve a higher accuracy. The test results show that this algorithm is superior to SIFT algorithm and can help to exclude mismatching points effectively. The matching accuracy can increase by about 12%. The problem of SIFT algorithm's high rate of mismatching for underwater binocular stereo matching is solved.


Sun H.,Yanshan University | Yang J.-M.,Yanshan University | Liu X.,Shougang Jingtang United Iron and Steel Co. | Tang Y.-C.,Qinhuangdao Kai Hong Technology Co. | And 2 more authors.
Kongzhi yu Juece/Control and Decision | Year: 2016

In the evolutionary algorithm with constrained multi-objective problems, the selection strategy of constraineddominate proposed by Professor Deb is widely used. The excellent infeasible solution is equally important as the feasible solution in the constraint treatment method. The infeasible solution has a small chance of winning in the selection strategy of constrained-dominate. Therefore, a differential evolution algorithm based on the environment pareto dominated selection strategy in the constrained multi-objective optimization problem is proposed. Benchmark functions are simulated, and the results show that, compared with other algorithms, the proposed algorithm has better convergence and stability. © 2016, Editorial Office of Control and Decision. All right reserved.


Zhang Q.,Yanshan University | Liu T.,Yanshan University | Li H.,Yanshan University | Li H.,National Engineering Research Center For Equipment And Technology Of Csr | And 3 more authors.
Guangxue Xuebao/Acta Optica Sinica | Year: 2014

In terms of underwater binocular image matching cannot satisfy the epipolar constraint of air, and the large amount of calculation of underwater image processed by the normalized cross correlation (NCC) algorithm, an underwater region matching algorithm based on optimum searching area is presented. Binocular camera should be calibrated in order to obtain some relevant parameters, as well as reference image and image to be matched; the maximum deviate value from the line in the air can be calculated through the curve constraint and the optimum searching area is therefore decided. The NCC region matching algorithm can help to match two images, at the same time, instead of searching on the original epipolar line, an optimum searching area is proposed so that the searching is performed in this area with several lines to achieve the purpose of a higher accuracy. Meanwhile, the time spent on the matching is reduced by the application of box filter technology. The results of the test indicate this algorithm achieves the same matching accuracy compared with the scale-invariant feature transform (SIFT) feature matching algorithm and this can be used to perform dense disparity. Also the speed of matching is largely accelerated compared with the original NCC algorithm. Therefore, the region matching algorithm is successfully applied to underwater image matching.


Li Y.,Yanshan University | Li Y.,National Engineering Research Center For Equipment And Technology Of Csr | Li H.,Yanshan University | Li H.,National Engineering Research Center For Equipment And Technology Of Csr | And 3 more authors.
Guangxue Xuebao/Acta Optica Sinica | Year: 2014

Principal component analysis (PCA) can only keep the global structure, while neighborhood preserving embedding (NPE) preserves the similarity between neighbor data, but ignores the difference between them. Focusing on the problems mentioned above, a feature extraction method is proposed by fusing global and local various feature, and is applied to facial expression recognition. PCA is used to preserve global structure and a local diversity scatter and a local similarity scatter is defined by manifold learning algorithms, combining with local maximum scatter difference criterion, the proposed method can efficiently preserve the variety of local manifold. The low dimensional feature is extracted by combining the global feature with local various feature for expression classification. The experiments on JAFFE and Cohn-Kanade facial expression databases indicate that compared with PCA, locality preserving progection (LPP), NPE and other methods, this method not only improves the recognition rate efficiently, but also needs the least dimensions when achieves the highest recognition rate, which demonstrates that this method is superior to others in recognition rate.

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