Contents

• Chapter 0: Introduction to the companion book
• Chapter 1: Introduction
  • Viewing an image: image_view_demo
• Chapter 2: The image, its representations and properties
  • Displaying a coarse binary image: coarse_pixels_draw
  • Distance transform, an example: dist_trans_demo
  • Border of a region, an example: region_border_demo
• Chapter 3: The image, its mathematical and physical background
  • Convolution, shift-multiply-add approach: conv_demo
  • Discrete Fourier Transform: dft_edu
  • Inverse DFT: idft_edu
  • 1D Discrete Fourier Transform: dft1d_demo
  • 2D Discrete Fourier Transform: dft2d_demo
  • Basis functions for the 2D Discrete Cosine Transform: dct2base
  • Principal Component Analysis: pca
• Chapter 4: Data structures for image analysis
  • \MATLAB\/ data structures: structures
  • Displaying image values: showim_values
  • Co-occurrence matrix: cooc
  • Integral image construction: integralim
• Chapter 5: Image pre-processing
  • Grayscale transformation, histogram equalization: hist_equal
  • Geometric transformation: imgeomt
  • Smoothing using a rotating mask: rotmask
  • Image sharpening by Laplacian: imsharpen
  • Harris corner detector: harris
  • Frequency filtering: buttfilt
• Chapter 6: Segmentation I
  • Iterative threshold selection: imthresh
  • Line detection using Hough transform: hough_lines
  • Dynamic programming boundary tracing: dpboundary
  • Region merging via boundary melting: regmerge
  • Removal of small regions: remsmall
• Chapter 7: Segmentation II
  • Mean shift segmentation: meanshsegm
  • Active contours (snakes): snake
  • Gradient vector flow snakes: mgvf
  • Level sets: levelset
  • Graph cut segmentation: GraphCut
• Chapter 8: Shape representation and description
  • B-spline interpolation: bsplineinterp
  • Convex hull construction: convexhull
  • Region descriptors: regiondescr
  • Boundary descriptors: boundarydescr
• Chapter 9: Object recognition
  • Maximum probability classification for normal data: maxnormalclass
  • Linear separability and basic classifiers: linsep_demo
  • Recognition of hand-written numerals: ocr_demo
  • Adaptive boosting: adaboost
• Chapter 10: Image understanding
  • Random sample consensus: ransac
  • Gaussian mixture model estimation: gaussianmixture
  • Point distribution models: pointdistrmodel
  • Active shape model fit: asmfit
• Chapter 11: 3D vision, geometry
  • Homography estimation from point correspondences---DLT method: u2Hdlt
  • Mathematical description of the camera: cameragen
  • Visualize a camera in a 3D plot: showcams
  • Decomposition of the projection matrix P: P2KRtC
  • Isotropic point normalization: pointnorm
  • Fundamental matrix---8-point algorithm: u2Fdlt
  • 3D point reconstruction---linear method: uP2Xdlt
• Chapter 12: Use of 3D vision
  • Iterative closest point matching: vtxicrp
• Chapter 13: Mathematical morphology
  • Top hat transformation: tophat
  • Object detection using opening: objdetect
  • Sequential thinning: thinning
  • Ultimate erosion: ulterosion
  • Binary granulometry: granulometry
  • Watershed segmentation: wshed
• Chapter 14: Image data compression
  • Huffman code: huffman
  • Predictive compression: dpcm
  • JPEG compression pictorially, step by step: jpegcomp_demo
• Chapter 15: Texture
  • Haralick texture descriptors: haralick
  • Wavelet texture descriptors: waveletdescr
  • Texture based segmentation: texturesegm
  • L-system interpreter: lsystem
• Chapter 16: Motion analysis
  • Adaptive background modeling by using a mixture of Gaussians: bckggm
  • Particle filtering: particle_filtering
  • Importance sampling: importance_sampling
  • Kernel-based tracking: kernel_based_tracking