Image Analysis and Recognition

1. Front Matter

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2. Image Segmentation

   1. Automatic Image Segmentation Using a Deformable Model Based on Charged Particles

      • Andrei C. Jalba, Michael H. F. Wilkinson, Jos B. T. M. Roerdink

      Pages 1-8

   2. Hierarchical Regions for Image Segmentation

      • Slawo Wesolkowski, Paul Fieguth

      Pages 9-16

   3. Efficiently Segmenting Images with Dominant Sets

      • Massimiliano Pavan, Marcello Pelillo

      Pages 17-24

   4. Color Image Segmentation Using Energy Minimization on a Quadtree Representation

      • Adolfo Martínez-Usó, Filiberto Pla, Pedro García-Sevilla

      Pages 25-32

   5. Segmentation Using Saturation Thresholding and Its Application in Content-Based Retrieval of Images

      • A. Vadivel, M. Mohan, Shamik Sural, A. K. Majumdar

      Pages 33-40

   6. A New Approach to Unsupervised Image Segmentation Based on Wavelet-Domain Hidden Markov Tree Models

      • Qiang Sun, Shuiping Gou, Licheng Jiao

      Pages 41-48

   7. Spatial Discriminant Function with Minimum Error Rate for Image Segmentation

      • EunSang Bak

      Pages 49-56

   8. Detecting Foreground Components in Grey Level Images for Shift Invariant and Topology Preserving Pyramids

      • Giuliana Ramella, Gabriella Sanniti di Baja

      Pages 57-64

   9. Pulling, Pushing, and Grouping for Image Segmentation

      • Guoping Qiu, Kin-Man Lam

      Pages 65-73

   10. Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator

       • Lei Wang, Hongbing Ji, Xinbo Gao

       Pages 74-81

   11. A Multistage Image Segmentation and Denoising Method – Based on the Mumford and Shah Variational Approach

       • Song Gao, Tien D. Bui

       Pages 82-89

   12. A Multiresolution Threshold Selection Method Based on Training

       • J. R. Martinez-de Dios, A. Ollero

       Pages 90-97

   13. Segmentation Based Environment Modeling Using a Single Image

       • Seung Taek Ryoo

       Pages 98-105

   14. Unsupervised Color-Texture Segmentation

       • Yuzhong Wang, Jie Yang, Yue Zhou

       Pages 106-113

3. Image Processing and Analysis

   1. Hierarchical MCMC Sampling

      • Paul Fieguth

      Pages 114-121

   2. Registration and Fusion of Blurred Images

      • Filip Sroubek, Jan Flusser

      Pages 122-129

   3. A New Numerical Scheme for Anisotropic Diffusion

      • Hongwen Yi, Peter H. Gregson

      Pages 130-138

   4. An Effective Detail Preserving Filter for Impulse Noise Removal

      • Naif Alajlan, Ed Jernigan

      Pages 139-146

   5. A Quantum-Inspired Genetic Algorithm for Multi-source Affine Image Registration

      • Hichem Talbi, Mohamed Batouche, Amer Draa

      Pages 147-154

ICIAR 2004, the International Conference on Image Analysis and Recognition, was the ?rst ICIAR conference, and was held in Porto, Portugal. ICIAR will be organized annually, and will alternate between Europe and North America. ICIAR 2005 will take place in Toronto, Ontario, Canada. The idea of o?ering these conferences came as a result of discussion between researchers in Portugal and Canada to encourage collaboration and exchange, mainly between these two countries, but also with the open participation of other countries, addressing recent advances in theory, methodology and applications. The response to the call for papers for ICIAR 2004 was very positive. From 316 full papers submitted, 210 were accepted (97 oral presentations, and 113 - sters). The review process was carried out by the Program Committee members and other reviewers; all are experts in various image analysis and recognition areas. Each paper was reviewed by at least two reviewing parties. The high q- lity of the papers in these proceedings is attributed ?rst to the authors, and second to the quality of the reviews provided by the experts. We would like to thank the authors for responding to our call, and we wholeheartedly thank the reviewers for their excellent work in such a short amount of time. We are espe- ally indebted to the Program Committee for their e?orts that allowed us to set up this publication. We were very pleased to be able to include in the conference, Prof. Murat KuntfromtheSwissFederalInstituteofTechnology,andProf. Mario ´ Figueiredo, oftheInstitutoSuperiorT´ ecnico,inPortugal.

• Hidden Markov Model
• Markov Model
• action recognition
• algorithms
• classification
• color analysis
• computer vision
• document processing
• image analysis
• image processing
• image reconstruction
• image segmentation
• pattern analysis
• pattern recognition
• video analysis

• FEUP - Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

  Aurélio Campilho

• Electrical and Computer Engineering Department, University of Waterloo,  

  Mohamed Kamel

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