Computer Vision - Advanced

Reading Guide


The table below lists the major algorithms and methods in the computer vision and image processing field.

I intend this page to be a "check-list" and provide  quick references for papers and code.



 

Algorithms list



Topic Sub-topic Algorithm /Author Level Concept Useful links
Development Software tools Matlab * widely used, fast development, slow run time homepage
openCV ** fast run time, medium documentation documentation
CUDA *** very-fast, difficult to use homepage
Books Image processing Gonzales & Woods * great for beginners and to review the material amazon
signal processingDSP guide*great for understand signals and FFTfree
computer visionSzeliski**most up-to-date. few explanations, many good referencesfree
3D reconstruction Hartley & Zisserman ** in-depth understanding of 3D concepts amazon
Usefull linksmatlab codeMatlab Central*large community, open source link
Peter Kovesi*many good implementations and demoslink
papersVisionBib*huge list of paperslink
CVpapers*papers from the most important conferenceslink
General concepts Math FFT * DSP book
Wavelets **
Gabor filters ** wiki, intro
Kalman filter * wiki, intro
Markov Random Fields ** wiki
Feature detection
Edge detection Sobel, Scharr, Roberts, Prewitt * Fast 1st derivative approximation
LoG *
Canny ** Smooth, gradient, non-maximum suppression, hysteresis thrsholding wiki
phase congruency ** location of matched phases correspond to edges description
Corner detection Hessian *
FAST**openCV
Line detection Frangi ** line's 2nd derivative should be one-directional and strong matlab
Hough transform * find lines in parameter-space wiki
Features for registration (Key points)SIFT**
SURF**
Registration Basic Normalized Cross Correlation * invariant for mean+gain differences
phase-shift-correlation ** wiki
Optic flow Lucas-Kanade ** wiki
Horn-Schunck ** wiki
Noise removal Basic mean filter * good for gaussian noise
median filter * good for salt-and-pepper
Wiener filter ** estimation of noise spectrum
Advanced Bilateral filter ** averaged pixels must be similar in both location and value
papers and code
Non-local means ** like bilateral, but use region-values instead of pixel-value. slow
Anistropic (Perona-Malik) ** preserve edges
Classification Maximum-Likelihood (ML) *
Maximum Aposteriori (MAP) *
Fisher *
Nearest-neighbor, FLANN ** good if classes are highly mixed
adaboost ** use several weak classifiers to create one strong classifier
Support Vector Machines (SVM) ** maximize the separation between classes
Neural Networks (NN) ***
Clustering
k-means*parametric, known number of clusters, fastwiki
mean-shift*non-parametric, unknown number of clusters, slowtutorial
Segmentationgraph cuts ** wiki
Hierarchical clustering **
grow cut***interactivebuy
Dynamic programming Vitterbi *
Dijkstra **
Active contour model (snakes) **
Parameter esimation Gaussian Mixture Model (GMM) **
RANSAC ** Test correctness of random-samples of data wiki
Detection Face detection Viola & Jones * pdf
Algorithms collection ** link
Optimization Gradient descent
*
Simulated annealing ** wiki
Image enhancement Contrast improvement Histogram equalization * wiki
Gamma correction * wiki
Sharpenning unsharp masking * add the high-pass to the image wiki
Video analysisboundary trackinglevel sets**intro
fast marching**intro
3D General topics Epipolar geometry * wiki



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