Sparse Coding and its Applications in Computer Vision

This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion.

The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications.

Contents:

• Introduction
• Theories of Sparse Coding
• Image Super-Resolution
• Image Deblurring
• Sensor Fusion
• Clustering
• Object Recognition
• Hyper-Spectral Image Modeling
• Conclusions

Readership: Graduate students, researchers and professionals in the field of machine perception, pattern recognition, image analysis, artificial intelligence, machine learning.
Key Features:

• Explanation of sparse coding from both theoretical and practical point of views
• A comprehensive review of the applications of sparse coding in both low-level and high-level vision problems
• Investigating future research directions of sparse coding by making connection with the current state-of-the-art feature learning models, including deep neural networks