Computational Intelligence in Image and Video Processing

Computational Intelligence in Image and Video Processing presents introduction, state-of-the-art and adaptations of computational intelligence techniques and their usefulness in image and video enhancement, classification, retrieval, forensics and captioning. It covers an amalgamation of such techniques in diverse applications of image and video processing. Features: - A systematic overview of state-of-the-art technology in computational intelligence techniques for image and video processing - Advanced evolutionary and nature-inspired approaches to solve optimization problems in the image and video processing domain - Outcomes of recent research and some pointers to future advancements in image and video processing and intelligent solutions using computational intelligence techniques - Code snippets of the computational intelligence algorithm/techniques used in image and video processing This book is primarily aimed at advanced undergraduates, graduates and researchers in computer science and information technology. Engineers and industry professionals will also find this book useful.

Dr. Mukesh D Patil is the Principal of Ramrao Adik Institute of Technology, Navi Mumbai, India. He obtained his Master of Technology and a PhD from Systems and Control engineering, Indian Institute of Technology Bombay, Mumbai, India, in 2002 and 2013. His current research areas include robust control, fractional order control and signal processing. He has published over 45-refereed papers and several patents, most in the areas of fractional-order control and signal processing. He is a senior member of IEEE, Fellow of IETE and life member of ISTE. He has served on the program committees of various conferences/workshops and member of several prestigious professional bodies. Dr. Gajanan K Birajdar obtained his M. Tech. (Electronics and Telecommunication Engineering) from Dr. Babasaheb Ambedkar Technological University, Maharashtra, India, in 2004 and Ph. D. in blind image forensics from Nagpur University, India, in 2018. He is working in the Department of Electronics Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, University of Mumbai. He is a member of various professional bodies like ISTE, IETE, and IE(I). His current research interests are multimedia security and forensics. Dr. Sangita S Chaudhari obtained her Master of Engineering (Computer Engineering) from Mumbai University, Maharashtra, India, in 2008 and Ph. D. in GIS and Remote Sensing from Indian Institute of Technology Bombay, Mumbai, India in 2016. Currently, she is working as professor in Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai. She has published several papers in the International/National Journals/Conferences and book chapters. She is an IEEE senior member and active member of IEEE GRSS and IEEE Women in Engineering. Her research interests include Image processing, Information security, Geographical Information Systems, and Remote sensing.

1.Text Information Extraction from Digital Image Documents Using Optical Character Recognition. 2. Extracting the Pixel Edges on Leaves to Detect Type using Fuzzy Logic. 3. Water Surface Waste Object Detection and Classification. 4. A Novel Approach for Weakly Supervised Object Detection Using Deep Learning Technique. 5. Image Inpainting Using Deep Learning. 6. Watermarking in Frequency Domain Using Magic Transform. 7. An Efficient Lightweight LSB Steganography with Deep learning Steganalysis. 8. Rectum Cancer Magnetic Resonance Image Segmentation. 9. Detection of Tuberculosis in Microscopy Images using Mask Region Convolutional Neural Network. 10. Comparison of Deep Learning Methods for COVID-19 Detection Using Chest X-ray. 11. Video Segmentation and Compression. 12. A Novel DST-SBPMRM-Based Compressed Video Steganography Using Transform Coefficients of Motion Region. 13. Video Matting, Watermarking and Forensics. 14. Time Efficient Video Captioning Using GRU, Attention Mechanism and LSTM. 15. Nature-Inspired Computing for Feature Selection and Classification. 16. Optimized Modified K-Nearest Neighbor Classifier for Pattern Recognition. 17. Role of Multi-objective Optimization in Image Segmentation and Classification.