2017年神经系统模式识别和计算机视觉的计算研讨会 2017 Workshop on Computational Aspects of Pattern Recognition and Computer Vision with Neural Systems

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活动简介

Computational requirements on information processing systems are nowadays enormous - not only huge amounts of data needs to be processed and classified but also the systems need to deal with massive data usually in the form of data streams and frequently real-time processing requirements. On the other hand, neural systems proved their great potential, especially in pattern recognition and computer vision. However, all of the above rely heavily on efficient algorithms and continuously improved implementations. Therefore computational aspects become a key issue in pattern recognition and computer vision.

In this workshop we wish to collect researchers and practitioners to share interesting research topics and ideas especially in the area of computational aspects of pattern recognition and computer vision processed on all types of neural systems, starting from algorithm design and up to implementations and applications, encountered in computer vision and pattern recognition computer vision for information mining, especially form from massive data streams and new neural architectures.

征稿信息

重要日期

2017-03-12

初稿截稿日期

2017-03-19

初稿录用日期

2017-03-26

终稿截稿日期

征稿范围

Parallel implementations of pattern recognition and computer vision neural systems
Deep learning – techniques and new achievements in computer vision with special stress on image enhancement for pattern recognition
Real-time neural systems, their implementation and application
Rapid neural system development – new directions and platforms
Graphic card (GPU) implementations of pattern recognition and computer vision systems
Hardware implementations (FPGA) of pattern recognition and computer vision systems
New algorithms for efficient computations on pattern recognition and computer vision neural systems
Tips and tricks in pattern recognition and computer vision algorithms
Industrial applications of pattern recognition and computer vision, especially with dedicated streaming data
Computational aspects in all kinds of massive and streaming data
Pattern recognition in computer vision, multimedia, and image processing
Multilinear and tensor approach to data representation and pattern recognition
Active learning for neural based pattern recognition and computer vision
Hyperspectral image processing
Pattern recognition in hyperspectral images
Visualisation and sonnification for high dimensional data