By Long Cheng, Qingshan Liu, Andrey Ronzhin
This booklet constitutes the refereed complaints of the thirteenth foreign Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers awarded during this quantity have been rigorously reviewed and chosen from 104 submissions. The papers hide many issues of neural network-related learn together with sign and photo processing; dynamical behaviors of recurrent neural networks; clever regulate; clustering, class, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.
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Additional resources for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings
In this work, we propose a deep learning method to solve the edge detection problem in image processing area. Existing methods usually rely heavily on computing multiple image features, which makes the whole system complex and computationally expensive. We train Convolutional Neural Networks (CNN) that can make predictions for edges directly from image patches. By adopting such networks, our system is free from additional feature extraction procedures, simple and eﬃcient without losing its detection performance.
In this paper, a spectral-spatial classiﬁcation method for hyperspectral image based on spatial ﬁltering and feature extraction is proposed. To extract the spatial information that contain spatially homogeneous property and distinct boundary, the original hyperspectral image is processed by an improved bilateral ﬁlter ﬁrstly. And then the proposed feature extraction algorithm called locality preserving discriminant analysis, which can explore the manifold structure and intrinsic characteristics of the hyperspectral dataset, is used to reduce the dimensionality of both the spectral and spatial features.
Thus, we do not suggest performing ICA on DW directly when it is noisy. html. Acknowledgments. Authors thank the support from National Natural Science Foundation of China (Grant Nos. 81471742 & 81461130018). Cong thanks Dr. Nicole Landi in Haskins Laboratories in Yale University for providing their ERP data. References 1. : An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge (2005) 2. : Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure.