Computational Intelligence in Image Processing

1. Front Matter

   Pages i-xii

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2. Image Preprocessing Algorithms

   1. Front Matter

      Pages 1-1

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   2. Improved Digital Image Enhancement Filters Based on Type-2 Neuro-Fuzzy Techniques

      • Mehmet Emin Yüksel, Alper Baştürk

      Pages 3-20

   3. Locally-Equalized Image Contrast Enhancement Using PSO-Tuned Sectorized Equalization

      • N. M. Kwok, D. Wang, Q. P. Ha, G. Fang, S. Y. Chen

      Pages 21-36

   4. Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding

      • Ilhem Boussaïd, Amitava Chatterjee, Patrick Siarry, Mohamed Ahmed-Nacer

      Pages 37-69

   5. A Genetic Programming Approach for Image Segmentation

      • Hugo Alberto Perlin, Heitor Silvério Lopes

      Pages 71-90

3. Image Compression Algorithms

   1. Front Matter

      Pages 91-91

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   2. Fuzzy Clustering-Based Vector Quantization for Image Compression

      • George E. Tsekouras, Dimitrios M. Tsolakis

      Pages 93-105

   3. Layers Image Compression and Reconstruction by Fuzzy Transforms

      • Ferdinando Di Martino, Salvatore Sessa

      Pages 107-130

   4. Modified Bacterial Foraging Optimization Technique for Vector Quantization-Based Image Compression

      • Nandita Sanyal, Amitava Chatterjee, Sugata Munshi

      Pages 131-152

4. Image Analysis Algorithms

   1. Front Matter

      Pages 153-153

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   2. A Fuzzy Condition-Sensitive Hierarchical Algorithm for Approximate Template Matching in Dynamic Image Sequence

      • Rajshree Mandal, Anisha Halder, Amit Konar, Atulya K Nagar

      Pages 155-172

   3. Digital Watermarking Strings with Images Compressed by Fuzzy Relation Equations

      • Ferdinando Di Martino, Salvatore Sessa

      Pages 173-186

   4. Study on Human Brain Registration Process Using Mutual Information and Evolutionary Algorithms

      • Mahua Bhattacharya, Arpita Das

      Pages 187-199

   5. Use of Stochastic Optimization Algorithms in Image Retrieval Problems

      • Mattia Broilo, Francesco G. B. De Natale

      Pages 201-215

   6. A Cluster-Based Boosting Strategy for Red Eye Removal

      • Sebastiano Battiato, Giovanni Maria Farinella, Daniele Ravì, Mirko Guarnera, Giuseppe Messina

      Pages 217-249

5. Image Inferencing Algorithms

   1. Front Matter

      Pages 251-251

      PDF

   2. Classifying Pathological Prostate Images by Fractal Analysis

      • Po-Whei Huang, Cheng-Hsiung Lee, Phen-Lan Lin

      Pages 253-263

   3. Multiobjective PSO for Hyperspectral Image Clustering

      • Farid Melgani, Edoardo Pasolli

      Pages 265-280

   4. A Computational Intelligence Approach to Emotion Recognition from the Lip-Contour of a Subject

      • Anisha Halder, Srishti Shaw, Kanika Orea, Pavel Bhowmik, Aruna Chakraborty, Amit Konar

      Pages 281-298

Computational intelligence based techniques have firmly established themselves as viable, alternate, mathematical tools for more than a decade. They have been extensively employed in many systems and application domains, among these signal processing, automatic control, industrial and consumer electronics, robotics, finance, manufacturing systems, electric power systems, and power electronics. Image processing is also an extremely potent area which has attracted the atten­tion of many researchers who are interested in the development of new computational intelligence-based techniques and their suitable applications, in both research prob­lems and in real-world problems.

Part I of the book discusses several image preprocessing algorithms; Part II broadly covers image compression algorithms; Part III demonstrates how computational intelligence-based techniques can be effectively utilized for image analysis purposes; and Part IV shows how pattern recognition, classification and clustering-based techniques can be developed for the purpose of image inferencing. The book offers a unified view of the modern computational intelligence tech­niques required to solve real-world problems and it is suitable as a reference for engineers, researchers and graduate students.

• computational intelligence
• digital images
• digital watermarking
• evolutionary algorithms
• evolutionary computing
• fuzzy transforms
• genetic programming
• image analysis
• image compression
• image inferencing
• image processing
• image retrieval
• neurofuzzy techniques
• optimization

• , Electrical Engineering Department, Jadavpur University, Kolkata, India

  Amitava Chatterjee

• , LISSI, University of Paris-Est Créteil, Créteil, France

  Patrick Siarry

Amitava Chatterjee is an Associate Professor in the Dept. of Electrical Engineering of Jadavpur University in Kolkata, India. He teaches electrical engineering and his research interests include fuzzy control, image processing, robotics, signal processing and stochastic optimization. He has visited overseas on prestigious scholarships and fellowships and has given invited lectures in India, Japan and France. He presently serves as an Associate Editor of the IEEE Transactions on Vehicular Technology. Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi). He has considerable teaching and research experience in the areas of signal processing, control, artificial intelligence, operations research, and optimization. He presently serves as an Associate Editor of the journal Information Sciences and the journal Engineering Applications of Artificial Intelligence.

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