Computational Intelligence in Image and Video Processing (eBook, PDF)
Redaktion: D Patil, Mukesh; S Chaudhari, Sangita; K Birajdar, Gajanan
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Computational Intelligence in Image and Video Processing (eBook, PDF)
Redaktion: D Patil, Mukesh; S Chaudhari, Sangita; K Birajdar, Gajanan
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This book presents introduction and state-of-the-art 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.
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- Größe: 16.36MB
This book presents introduction and state-of-the-art 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.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 360
- Erscheinungstermin: 15. Februar 2023
- Englisch
- ISBN-13: 9781000832433
- Artikelnr.: 67306266
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
- Verlag: Taylor & Francis
- Seitenzahl: 360
- Erscheinungstermin: 15. Februar 2023
- Englisch
- ISBN-13: 9781000832433
- Artikelnr.: 67306266
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
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.
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.
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.
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.