Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems (eBook)

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.

Applications highlighted in the book include:

• diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;
• computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;
• methods capable of retrieving photometric and geometric transformed images;
• concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;
• machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;
• a comprehensive study of content-based image-retrieval techniques for feature extraction;
• machine learning approaches to understanding angiogenesis;
• handwritten image enhancement based on neutroscopic-fuzzy.

Audience

The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.