PDF Image Statistics in Visual Computing

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Image Statistics in Visual Computing: 1st Edition (e-Book) - Routledge

Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. With numerous color figures throughout, Image Statistics in Visual Computing covers all aspects of natural image statistics, from data collection to analysis to applications in computer graphics, computational photography, image processing, and art. The authors keep the material accessible, providing mathematical definitions where appropriate to help readers understand the transforms that highlight statistical regularities present in images.

The book also describes patterns that arise once the images are transformed and gives examples of applications that have successfully used statistical regularities. Numerous references enable readers to easily look up more information about a specific concept or application. A supporting website also offers additional information, including descriptions of various image databases suitable for statistics. Collecting state-of-the-art, interdisciplinary knowledge in one source, this book explores the relation of natural image statistics to human vision and shows how natural image statistics can be applied to visual computing.

Image Statistics in Visual Computing

It encourages readers in both academic and industrial settings to develop novel insights and applications in all disciplines that relate to visual computing. It is presented in an accessible fashion full of color images. It contains more than reference entries.

Large-scale Image Classification: ImageNet and ObjectBank

So, it is a good starting point for all those who want to easily familiarize with the theory of the presented field. This book is good for computer scientists who want to start their research in digital imaging and for engineers who want to apply the described methods in practice.

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X is standing in front of Mt. Deep learning is all about how you make machines synthesize this hierarchical logic and also learn these representative features and kernels all by itself. It has been used to solve problems like handwritten character recognition, object and product recognition and localization, image captioning, generating synthetic images to self driving cars.

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