Contents目录

  • Chapter 0: Introduction to the companion book本辅导书简介
  • Chapter 1: Introduction 简介
    • Viewing an image: image_view_demo 查看一张图像: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

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Last modified at 15:56, 28 April 2014 CEST.

关于机器视觉与机器学习的大量资源及书籍 可在线阅读:http://blog.exbot.net/archives/48

demo videos:http://visionbook.felk.cvut.cz/demos.html

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