Image Processing, Analysis & and Machine Vision - A MATLAB Companion

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 灰度共生矩阵
• 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