By Long Cheng, Qingshan Liu, Andrey Ronzhin
This e-book constitutes the refereed court cases of the thirteenth foreign 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 conscientiously reviewed and chosen from 104 submissions. The papers hide many issues of neural network-related examine together with sign and snapshot processing; dynamical behaviors of recurrent neural networks; clever keep an eye on; clustering, class, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.
Read Online or Download Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings (Lecture Notes in Computer Science) PDF
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Extra resources for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings (Lecture Notes in Computer Science)
Pattern Anal. Mach. Intell. 26, 1531–1536 (2004) 5. : Combining edge and texture information for real-time accurate 3d camera tracking. In: Third IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2004, pp. 48–56. IEEE (2004) 6. : A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986) 7. : Detecting object boundaries using low-, mid-, and high-level information. Comput. Vis. Image Underst. 114, 1055–1067 (2010) 8. : Supervised learning of edges and object boundaries.
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.
Wang Edge Detection System Figure 2 provides an overview of our edge detection system. Given an image, we can ﬁrst apply some preprocessing techniques  for noise removal. Then a convolutional neural network scans over the entire image, making edge prediction for every pixel based on the image patch centered on it. At last, nonmaximal suppression  or morphological operations can be further applied as a post-processing step to thin the output edge map so as to increase localization accuracy.