Learning Features by Contrasting Natural Images with Noise
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
IEEE Transactions on Image Processing
Wordica: Emergence of linguistic representations for words by independent component analysis
Natural Language Engineering
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ACM Transactions on Graphics (TOG)
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International Journal of Computer Vision
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AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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BI'12 Proceedings of the 2012 international conference on Brain Informatics
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Neural Processing Letters
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One of the most successful frameworks in computational neuroscience is modeling visual processing using the statistical structure of natural images. In this framework, the visual system of the brain constructs a model of the statistical regularities of the incoming visual data. This enables the visual system to perform efficient probabilistic inference. The same framework is also very useful in engineering applications such as image processing and computer vision. This book is the first comprehensive introduction to the multidisciplinary field of natural image statistics. The book starts with a review of background material in signal processing and neuroscience, which makes it accessible to a wide audience. The book then explains both the basic theory and the most recent advances in a coherent and user-friendly manner. This structure, together with the included exercises and computer assignments, also make it an excellent textbook. "Natural Image Statistics" is a timely and valuable resource for advanced students and researchers in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.