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updated 5 months ago

Multidimensional distributions visualisation and analysis. by Mark Matusevich

Matrix of planar projections of multidimensional distributions with tools for interactive analysis. (classification, statistics, statistical modelling)

dependents_plot(X, Y, nBins, funcs)

map2bins(X, vBins, mode)

plot_1feature_2class(vFeature, vClass, sFeature_name, csC...

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updated 1 year ago

SIFT feature computation by I Chieh Chen

I Chieh Chen (view profile)

An testing version of SIFT feature detection and calculation (sift, feature)

SiftMain.m

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updated 1 year ago

Block Segmentation by Anand g

Anand g (view profile)

Face feature can be extracted by block segmentation. (block, segmentation, matrix separation)

block( bs_r,bs_c,I )

test.m

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updated 2 years ago

HOG (Histogram of Oriented Gradients) MEX Implementation by Paul Sastrasinh

Rectangular histogram of gradients (R-HoG) descriptor MEX implementation. (hog, descriptor, image)

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updated almost 4 years ago

Texture Feature Extraction - GLDM by Athi

Athi (view profile)

GLDM calculates the Gray level Difference Method Probability Density Functions for the given image. (image processing, medical imaging, mammogram)

GLDM(inImg, d)

SampleUsage.m

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updated 4 years ago

Corner Detection using SUSAN operator by Edward Wiggin

Edward Wiggin (view profile)

Detect corner points in an image using SUSAN operator. (corner, susan, image analyse)

susanCorner( img )

testSusan.m

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updated 4 years ago

OpenSURF (including Image Warp) by Dirk-Jan Kroon

Dirk-Jan Kroon (view profile)

SURF (Speeded Up Robust Features) image feature point detection / matching, as in SIFT (registration, surf, opensurf)

D=FastHessian_BuildDerivative(r,c,t,m,b)

Iout=affine_warp(Iin,M,mode)

PaintSURF(I, ipts)

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updated 5 years ago

Fast Eyetracking by Peter Aldrian

Peter Aldrian (view profile)

  • 1 file
  • 200 downloads
  • 4.81818

Extract feature points from faces to track eye's movement (image processing, demo, eye)

[L]=lineint(I,C,r,n,part)

[O]=drawcircle(I,C,r,n)

[b,r,blur]=partiald(I,C,rmin,rmax,sigma,n,part);

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