Front Cover -- Feature Extraction & Image Processing for Computer Vision -- Copyright page -- Contents -- Preface -- What is new in the third edition? -- Why did we write this book? -- The book and its support -- In gratitude -- Final message -- About the authors -- 1 Introduction -- 1.1 Overview -- 1.2 Human and computer vision -- 1.3 The human vision system -- 1.3.1 The eye -- 1.3.2 The neural system -- 1.3.3 Processing -- 1.4 Computer vision systems -- 1.4.1 Cameras -- 1.4.2 Computer interfaces -- 1.4.3 Processing an image -- 1.5 Mathematical systems -- 1.5.1 Mathematical tools -- 1.5.2 Hello Matlab, hello images! -- 1.5.3 Hello Mathcad! -- 1.6 Associated literature -- 1.6.1 Journals, magazines, and conferences -- 1.6.2 Textbooks -- 1.6.3 The Web -- 1.7 Conclusions -- 1.8 References -- 2 Images, sampling, and frequency domain processing -- 2.1 Overview -- 2.2 Image formation -- 2.3 The Fourier transform -- 2.4 The sampling criterion -- 2.5 The discrete Fourier transform -- 2.5.1 1D transform -- 2.5.2 2D transform -- 2.6 Other properties of the Fourier transform -- 2.6.1 Shift invariance -- 2.6.2 Rotation -- 2.6.3 Frequency scaling -- 2.6.4 Superposition (linearity) -- 2.7 Transforms other than Fourier -- 2.7.1 Discrete cosine transform -- 2.7.2 Discrete Hartley transform -- 2.7.3 Introductory wavelets -- 2.7.3.1 Gabor wavelet -- 2.7.3.2 Haar wavelet -- 2.7.4 Other transforms -- 2.8 Applications using frequency domain properties -- 2.9 Further reading -- 2.10 References -- 3 Basic image processing operations -- 3.1 Overview -- 3.2 Histograms -- 3.3 Point operators -- 3.3.1 Basic point operations -- 3.3.2 Histogram normalization -- 3.3.3 Histogram equalization -- 3.3.4 Thresholding -- 3.4 Group operations -- 3.4.1 Template convolution -- 3.4.2 Averaging operator -- 3.4.3 On different template size -- 3.4.4 Gaussian averaging operator.

3.4.5 More on averaging -- 3.5 Other statistical operators -- 3.5.1 Median filter -- 3.5.2 Mode filter -- 3.5.3 Anisotropic diffusion -- 3.5.4 Force field transform -- 3.5.5 Comparison of statistical operators -- 3.6 Mathematical morphology -- 3.6.1 Morphological operators -- 3.6.2 Gray-level morphology -- 3.6.3 Gray-level erosion and dilation -- 3.6.4 Minkowski operators -- 3.7 Further reading -- 3.8 References -- 4 Low-level feature extraction (including edge detection) -- 4.1 Overview -- 4.2 Edge detection -- 4.2.1 First-order edge-detection operators -- 4.2.1.1 Basic operators -- 4.2.1.2 Analysis of the basic operators -- 4.2.1.3 Prewitt edge-detection operator -- 4.2.1.4 Sobel edge-detection operator -- 4.2.1.5 The Canny edge detector -- 4.2.2 Second-order edge-detection operators -- 4.2.2.1 Motivation -- 4.2.2.2 Basic operators: the Laplacian -- 4.2.2.3 The Marr-Hildreth operator -- 4.2.3 Other edge-detection operators -- 4.2.4 Comparison of edge-detection operators -- 4.2.5 Further reading on edge detection -- 4.3 Phase congruency -- 4.4 Localized feature extraction -- 4.4.1 Detecting image curvature (corner extraction) -- 4.4.1.1 Definition of curvature -- 4.4.1.2 Computing differences in edge direction -- 4.4.1.3 Measuring curvature by changes in intensity (differentiation) -- 4.4.1.4 Moravec and Harris detectors -- 4.4.1.5 Further reading on curvature -- 4.4.2 Modern approaches: region/patch analysis -- 4.4.2.1 Scale invariant feature transform -- 4.4.2.2 Speeded up robust features -- 4.4.2.3 Saliency -- 4.4.2.4 Other techniques and performance issues -- 4.5 Describing image motion -- 4.5.1 Area-based approach -- 4.5.2 Differential approach -- 4.5.3 Further reading on optical flow -- 4.6 Further reading -- 4.7 References -- 5 High-level feature extraction: fixed shape matching -- 5.1 Overview -- 5.2 Thresholding and subtraction.

5.3 Template matching -- 5.3.1 Definition -- 5.3.2 Fourier transform implementation -- 5.3.3 Discussion of template matching -- 5.4 Feature extraction by low-level features -- 5.4.1 Appearance-based approaches -- 5.4.1.1 Object detection by templates -- 5.4.1.2 Object detection by combinations of parts -- 5.4.2 Distribution-based descriptors -- 5.4.2.1 Description by interest points -- 5.4.2.2 Characterizing object appearance and shape -- 5.5 Hough transform -- 5.5.1 Overview -- 5.5.2 Lines -- 5.5.3 HT for circles -- 5.5.4 HT for ellipses -- 5.5.5 Parameter space decomposition -- 5.5.5.1 Parameter space reduction for lines -- 5.5.5.2 Parameter space reduction for circles -- 5.5.5.3 Parameter space reduction for ellipses -- 5.5.6 Generalized HT -- 5.5.6.1 Formal definition of the GHT -- 5.5.6.2 Polar definition -- 5.5.6.3 The GHT technique -- 5.5.6.4 Invariant GHT -- 5.5.7 Other extensions to the HT -- 5.6 Further reading -- 5.7 References -- 6 High-level feature extraction: deformable shape analysis -- 6.1 Overview -- 6.2 Deformable shape analysis -- 6.2.1 Deformable templates -- 6.2.2 Parts-based shape analysis -- 6.3 Active contours (snakes) -- 6.3.1 Basics -- 6.3.2 The Greedy algorithm for snakes -- 6.3.3 Complete (Kass) snake implementation -- 6.3.4 Other snake approaches -- 6.3.5 Further snake developments -- 6.3.6 Geometric active contours (level-set-based approaches) -- 6.4 Shape skeletonization -- 6.4.1 Distance transforms -- 6.4.2 Symmetry -- 6.5 Flexible shape models-active shape and active appearance -- 6.6 Further reading -- 6.7 References -- 7 Object description -- 7.1 Overview -- 7.2 Boundary descriptions -- 7.2.1 Boundary and region -- 7.2.2 Chain codes -- 7.2.3 Fourier descriptors -- 7.2.3.1 Basis of Fourier descriptors -- 7.2.3.2 Fourier expansion -- 7.2.3.3 Shift invariance -- 7.2.3.4 Discrete computation.

7.2.3.5 Cumulative angular function -- 7.2.3.6 Elliptic Fourier descriptors -- 7.2.3.7 Invariance -- 7.3 Region descriptors -- 7.3.1 Basic region descriptors -- 7.3.2 Moments -- 7.3.2.1 Basic properties -- 7.3.2.2 Invariant moments -- 7.3.2.3 Zernike moments -- 7.3.2.4 Other moments -- 7.4 Further reading -- 7.5 References -- 8 Introduction to texture description, segmentation, and classification -- 8.1 Overview -- 8.2 What is texture? -- 8.3 Texture description -- 8.3.1 Performance requirements -- 8.3.2 Structural approaches -- 8.3.3 Statistical approaches -- 8.3.4 Combination approaches -- 8.3.5 Local binary patterns -- 8.3.6 Other approaches -- 8.4 Classification -- 8.4.1 Distance measures -- 8.4.2 The k-nearest neighbor rule -- 8.4.3 Other classification approaches -- 8.5 Segmentation -- 8.6 Further reading -- 8.7 References -- 9 Moving object detection and description -- 9.1 Overview -- 9.2 Moving object detection -- 9.2.1 Basic approaches -- 9.2.1.1 Detection by subtracting the background -- 9.2.1.2 Improving quality by morphology -- 9.2.2 Modeling and adapting to the (static) background -- 9.2.3 Background segmentation by thresholding -- 9.2.4 Problems and advances -- 9.3 Tracking moving features -- 9.3.1 Tracking moving objects -- 9.3.2 Tracking by local search -- 9.3.3 Problems in tracking -- 9.3.4 Approaches to tracking -- 9.3.5 Meanshift and Camshift -- 9.3.5.1 Kernel-based density estimation -- 9.3.5.2 Meanshift tracking -- Similarity function -- Kernel profiles and shadow kernels -- Gradient maximization -- 9.3.5.3 Camshift technique -- 9.3.6 Recent approaches -- 9.4 Moving feature extraction and description -- 9.4.1 Moving (biological) shape analysis -- 9.4.2 Detecting moving shapes by shape matching in image sequences -- 9.4.3 Moving shape description -- 9.5 Further reading -- 9.6 References.

10 Appendix 1: Camera geometry fundamentals -- 10.1 Image geometry -- 10.2 Perspective camera -- 10.3 Perspective camera model -- 10.3.1 Homogeneous coordinates and projective geometry -- 10.3.1.1 Representation of a line and duality -- 10.3.1.2 Ideal points -- 10.3.1.3 Transformations in the projective space -- 10.3.2 Perspective camera model analysis -- 10.3.3 Parameters of the perspective camera model -- 10.4 Affine camera -- 10.4.1 Affine camera model -- 10.4.2 Affine camera model and the perspective projection -- 10.4.3 Parameters of the affine camera model -- 10.5 Weak perspective model -- 10.6 Example of camera models -- 10.7 Discussion -- 10.8 References -- 11 Appendix 2: Least squares analysis -- 11.1 The least squares criterion -- 11.2 Curve fitting by least squares -- 12 Appendix 3: Principal components analysis -- 12.1 Principal components analysis -- 12.2 Data -- 12.3 Covariance -- 12.4 Covariance matrix -- 12.5 Data transformation -- 12.6 Inverse transformation -- 12.7 Eigenproblem -- 12.8 Solving the eigenproblem -- 12.9 PCA method summary -- 12.10 Example -- 12.11 References -- 13 Appendix 4: Color images -- 13.1 Color images -- 13.2 Tristimulus theory -- 13.3 Color models -- 13.3.1 The colorimetric equation -- 13.3.2 Luminosity function -- 13.3.3 Perception based color models: the CIE RGB and CIE XYZ -- 13.3.3.1 CIE RGB color model: Wright-Guild data -- 13.3.3.2 CIE RGB color matching functions -- 13.3.3.3 CIE RGB chromaticity diagram and chromaticity coordinates -- 13.3.3.4 CIE XYZ color model -- 13.3.3.5 CIE XYZ color matching functions -- 13.3.3.6 XYZ chromaticity diagram -- 13.3.4 Uniform color spaces: CIE LUV and CIE LAB -- 13.3.5 Additive and subtractive color models: RGB and CMY -- 13.3.5.1 RGB and CMY -- 13.3.5.2 Transformation between RGB color models -- 13.3.5.3 Transformation between RGB and CMY color models.

13.3.6 Luminance and chrominance color models: YUV, YIQ, and YCbCr.