Feature Extraction and Image Processing for Computer Vision

With the proliferation of mobile and robotic applications, the efficiency of visual search and recognition engines - widely recognized as one of the key components in next-generation artificial intelligence systems - has received ever increasing attention. This book describes basic principles, theories, and techniques in designing visual search and recognition engines. The reader will learn how: Visual features are extracted and quantizedAn indexing system is builtFast comparisons are made to alternative schemesCutting-edge techniques like deep learning are integrated into the design of visual feature learning The book explains recent techniques to handle efficiency issues. From the feature end, it describes how hash functions are learnt to transfer high-dimensional feature descriptors into compact binary code. From the model end, it discusses recent advances in compressing and accelerating large deep learning models (like convolutional neural networks) to fit into mobile and robotic memory storage. Introduces the building blocks of visual search and recognition enginesDetailed reviews of visual feature hashingDetailed reviews of deep learning model compressionThe latest cutting edge research such as compact descriptors for visual search and visual feature hashing INDICE: 1. Techniques in visual descriptor extraction and representation2. Overview of visual search and recognition engines3. Feature quantization and indexing4. Building recognition models5. On compact feature learning6. On deep model compression7. Applications8. Trends and Future Research Challenges

• ISBN: 978-0-12-814976-8
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 632
• Fecha Publicación: 01/09/2019
• Nº Volúmenes: 1
• Idioma: Inglés