Written by leading researchers, the 2nd Edition of the Dictionary of Computer Vision & Image Processing is a comprehensive and reliable resource which now provides explanations of over 3500 of the most commonly used terms across image processing, computer vision and related fields including machine vision. It offers clear and concise definitions with short examples or mathematical precision where necessary for clarity that ultimately makes it a very usable reference for new entrants to these fields at senior undergraduate and graduate level, through to early career researchers to help build up knowledge of key concepts. As the book is a useful source for recent terminology and concepts, experienced professionals will also find it a valuable resource for keeping up to date with the latest advances. New features of the 2nd Edition: Contains more than 1000 new terms, notably an increased focus on image processing and machine vision terms; Includes the addition of reference links across the majority of terms pointing readers to further information about the concept under discussion so that they can continue to expand their understanding; Now available as an eBook with enhanced content: approximately 50 videos to further illustrate specific terms; active cross-linking between terms so that readers can easily navigate from one related term to another and build up a full picture of the topic in question; and hyperlinked references to fully embed the text in the current literature.
Dictionary of Computer Vision and Image Processing
This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision.
With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, ...
Bamford, P., and Lovell, B., Unsupervised Cell Nucleus Segmentation with Active Contours, Signal Processing, 71, ... Caselles, V., Kimmel, R., and Sapiro, G., Geodesic Active Contours, International Journal of Computer Vision, 22(1), ...
This book is useful for students, researchers, scientists, and engineers interested in the research developments of this rapidly growing field.
This updated edition provides practical solutions so you can: Program state-of-the-art image-processing capabilities into software Find the steps for taking advantage of classifiers Apply 2D vision methods in content-based searches Perform ...
The image patches, Iph , from Ih are hereby represented as a sparse linear combination in a dictionary, DHR , trained from the HR image patches, sampled from training images. Mathematically it is stated as follows.
Sparse representation and dictionary learning techniques are successfully applied to various image processing and computer vision tasks like compression, denoising, super-resolution and classification etc. Mairal et al.
The learned 2D separable dictionary corresponding to the specific scan type is found to superiorly reconstruct the MR images respective to their under-sampled 2D slices. The paper is organized mainly into three sections.
Divided into five major parts, the book begins by introducing the concepts and definitions necessary to understand computer imaging.