Bio-Inspired Computing for Image and Video Processing / D.P. Acharjya.
"In recent years bio-inspired computational theories and tools have developed to assist people in extracting knowledge from high dimensional data. These differ in how they take a more evolutionary approach to learning, as opposed to traditional artificial intelligence (AI) and what could be des...
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Full Text (via Taylor & Francis) |
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| Format: | eBook |
| Language: | English |
| Published: |
London :
Taylor and Francis,
2017.
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| Edition: | First edition. |
| Subjects: |
MARC
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| 520 | 2 | |a "In recent years bio-inspired computational theories and tools have developed to assist people in extracting knowledge from high dimensional data. These differ in how they take a more evolutionary approach to learning, as opposed to traditional artificial intelligence (AI) and what could be described as 'creationist' methods. Instead bio-inspired computing takes a bottom-up, de-centralized approach that often involves the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules, and of iteratively applying those rules. Bio-Inspired Computing for Image and Video Processing covers interesting and challenging new theories in image and video processing. It addresses the growing demand for image and video processing in diverse application areas, such as secured biomedical imaging, biometrics, remote sensing, texture understanding, pattern recognition, content-based image retrieval, and more. This book is perfect for students following this topic at both undergraduate and postgraduate level. It will also prove indispensable to researchers who have an interest in image processing using bio-inspired computing."--Provided by publisher. | |
| 505 | 0 | |a Part I Bio-Inspired Computing Models and Algorithms -- chapter 1 Genetic Algorithm and BFOA-Based Iris and Palmprint Multimodal Biometric Digital Watermarking Models -- chapter 2 Multilevel Thresholding for Image Segmentation Using Cricket Chirping Algorithm -- chapter 3 Algorithms for Drawing Graphics Primitives on a Honeycomb Model-Inspired Grid -- chapter 4 Electrical Impedance Tomography Using Evolutionary Computing: A Review -- part II Bio-Inspired Optimization Techniques -- chapter 5 An Optimized False Positive Free Video Watermarking System in Dual Transform Domain -- chapter 6 Bone Tissue Segmentation Using Spiral Optimization and Gaussian Thresholding -- chapter 7 Digital Image Segmentation Using Computational Intelligence Approaches -- chapter 8 Digital Color Image Watermarking Using DWT SVD Cuckoo Search Optimization -- chapter 9 Digital Image Watermarking Scheme in Transform Domain Using the Particle Swarm Optimization Technique -- part III Bio-Inspired Computing Applications to Image and Video Processing -- chapter 10 Evolutionary Algorithms for the Efficient Design of Multiplier-Less Image Filter -- chapter 11 Fusion of Texture and Shape-Based Statistical Features for MRI Image Retrieval System -- chapter 12 Singular Value Decomposition-Principal Component Analysis-Based Object Recognition Approach -- chapter 13 The KD-ORS Tree: An Efficient Indexing Technique for Content-Based Image Retrieval -- chapter 14 An Efficient Image Compression Algorithm Based on the Integration of a Histogram Indexed Dictionary and the Huffman Encoding for Medical Images. | |
| 650 | 0 | |a Natural computation. | |
| 650 | 0 | |a Image processing |x Mathematical models. | |
| 650 | 0 | |a Image analysis |x Mathematical models. | |
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| 650 | 7 | |a Image processing |x Mathematical models |2 fast | |
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| 700 | 1 | |a Santhi, V. | |
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