Technical Book: Fundamentals and Applications of Image Understanding and Pattern Recognition
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Technical Book: Fundamentals and Applications of Image Understanding and Pattern Recognition
<Author> Tadayoshi Shioyama, Emeritus Professor, Kyoto Institute of Technology (Doctor of Engineering) <Publication Date> February 22, 2010 <Format> B5 size, 160 pages planned <Price> 52,290 yen (including tax) *For detailed content, please download the catalog (brochure).
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<Fields of Application for the Theory and Methods in This Book> - Face image recognition - Biometric authentication - Automated visual inspection - Vehicle measurement - Pedestrian recognition - Front vehicle recognition - Lane recognition - General image processing ☆ Preprocessing for image understanding and recognition (filtering, transformation) ☆ Restoring 3D information from 2D information ☆ Reducing the dimensionality of the feature space without decreasing recognition accuracy ☆ Robust recognition against variations in the target ☆ Construction of discriminant functions (inter-class boundaries) ☆ Examples of innovative applied research (10 cases)
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Chapter 1: Basics of Image Understanding 1. Projection from 3D Space to Images 2. Camera Parameters 3. 3D Reconstruction through Triangulation 4. 3D Reconstruction through Monocular Vision Chapter 2: Basics of Pattern Recognition 1. Image Processing Techniques 2. Statistical Feature Extraction 3. Category Decision Methods 4. Projective Invariants 5. Affine Invariants 6. Learning Methods Chapter 3: Recent Trends in Image Understanding and Pattern Recognition 1. Robust Tracking using Bayesian Filters with Multiple Features 2. Tracking Multiple Targets using Spatiotemporal Relationships 3. Detection of Moving Object Regions using Geometric Constraints that Surpass Epipolar Constraints 4. Efficient Solutions to the Matting Problem 5. Recognition based on Discriminant Analysis using Canonical Correlation 6. Feature Space Analysis using Mean Shift - Applications to Smoothing that Preserves Discontinuities and Region Segmentation 7. Construction of Projective Invariants 8. Shape from Texture under Perspective (Central) Projection 9. Discriminative Common Vector Method that Overcomes the Weaknesses of PCA and LDA 10. Identification Method with Feature Component Selection using Bayes' Rule
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