Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems - Hardcover

Über die Autorin bzw. den Autor

Kapil Joshi, PhD, is an assistant professor in the Computer Science & Engineering Department, Uttaranchal Institute of Technology in Dehradun, Uttarakhand, India. Joshi completed a PhD on the topic of Image Quality Enhancement using Fusion Techniques.' He has 8 years of academic experience in his areas of interest in Operating systems, Computer Networks, Web Technology, Data Structure, and Java. He has published various patents, research papers, and two books. In 2021, he was awarded the 'Best Young Researcher' Award in Global Education and Corporate Leadership received by Life Way Tech India Pvt. Ltd.
 

Shubham Mahajan is an assistant professor in the School of Engineering at Ajeekya D Y Patil University, Pune, Maharashtra, India. Mahajan completed a Ph.D. degree with Shri Mata Vaishno Devi University. His main areas of interest include image processing, video compression, and image segmentation. He received the 'Best Research Paper Award in 2019 from ICRIC.
 

Amit Kant Pandit works in the School of Electronics & Communications Engineering at the Shri Mata Vaishno Devi University in Katra, India. He completed a Ph.D. in 2009 from Shri Mata Vaishno Devi University. He is an author of 60 peer-reviewed journal publications. His research focuses on image processing, video compression, and nature-inspired computing methods.
 

Nitish Pathak works in the Department of Information Technology at Bhagwan Parshuram Institute of Technology in New Delhi, India. Pathak completed a Ph.D. in computer science and engineering. He has 17 years of engineering education experience and has been published in 80 international journals for international conferences, patents, and book chapters. Pathak was the recipient of the Directorate Award in 2008 and 2009. His research areas include intelligent computing techniques, empirical software engineering, and artificial intelligence.

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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.