Advances in Neural Networks – ISNN 2016: 13th International by Long Cheng, Qingshan Liu, Andrey Ronzhin

By Long Cheng, Qingshan Liu, Andrey Ronzhin

This e-book constitutes the refereed court cases of the thirteenth overseas Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers offered during this quantity have been rigorously reviewed and chosen from 104 submissions. The papers conceal many subject matters of neural network-related study together with sign and snapshot processing; dynamical behaviors of recurrent neural networks; clever keep an eye on; clustering, category, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.

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Additional info for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings

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Mach. Intell. 33, 898–916 (2011) 2. : Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 36–51 (2008) 20 R. Wang 3. : A contour-based moving object detection and tracking. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance 2005, pp. 271–276. IEEE (2005) 4. : Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal.

Evnevich, and Dmitriy K. su Abstract. The paper deals with parallel large aerospace images processing. We considered a simple multi-alternative discrete accumulation method for reliable distinction of satellite imagery and implemented a parallel classification system to increase the algorithm efficiency. The process of development of the distinction algorithm and system architecture was described. The system prototype was successfully tested. The experiments allowed to draw conclusion about the system performance and to estimate the effect of using the parallel architecture.

This implies preliminary computation of the virtual brightness value (which is a substitute for the black color brightness on a grayscale image) assuring faultless distinction of the samples. Thus, the optimization of the training set is carried out. The procedure described above is equivalent to counting the number of matching brightness of black while superimposing the etalon figures on the current one and assigning a number to it according to the majority of coincidences. Here 7 images (channels) are used: RED + GREEN + BLUE (« gray » channel), separated RED, GREEN and BLUE channels and the half-sums of RED + GREEN, RED + BLUE and GREEN + BLUE.

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