Spatio Temporal Feature extraction using harris 3d corner detector
Harris detector
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Transcript of Harris detector
Harris detectorConvert image to greyscaleApply Gaussian convolution to blur the image and
remove noiseCalculate gradient of image in x and y direction for
every pixelFor each point in the image,
consider a 3x3 square window of pixels around that point. Compute the Harris matrix H for that point,
Compute the corner strength function Choose points whose c(H) is above threshold and c(H) is local maxima in a 10x10 neighborhood.
These points will be called the feature points
My simple descriptorFor each feature points
Take a 45x45 window centered at the feature pointNormalise the color of each pixels in the window
Make a 9x9 window feature descriptor by applying linear weights to every 5 points and summing up the RGB values separately.
The feature descriptor will contain the RGB value of the points, hence in total our feature descriptor has 9x9x3 dimensions
Simple descriptor – Bike 1
Simple descriptor – Bike 2
Simple descriptor – bike 3
Simple descriptor – bike 4
Simple descriptor – bike 5
Simple descriptor – bike 6
My simple feature descriptor: bikes 1 – bikes 2 – matching with ratio
My simple feature descriptor : bikes 1 –bikes 3 – matching with ratio
Simple descriptor numbers(testMatch results)
My descriptor SIFT
Img1-img2 43.651483 7.281360
Img1-img3 64.347448 13.653063
Img1-img4 112.002063 19.995964
Img1-img5 320.155898 34.570192
The total error is the average Euclidean distance between a (correctly) transformed feature point in the first image and its matched feature point in the second image.
• Your error number will be slightly different: (nInliers/nMatches)
My evaluation:for (unsigned int i=0; i<f1.size(); i++) { applyHomography(f1[i].x, f1[i].y, xNew, yNew, h); if (matches[i].id > 0) { d += sqrt(pow(xNew-f2[matches[i].id-1].x,2)+pow(yNew-f2[matches[i].id-1].y,2)); n++; }}
return d / n;
Your evaluation in skeleton code:for (unsigned int i=0; i<f1.size(); i++) { applyHomography(f1[i].x, f1[i].y, xNew, yNew, h); if (matches[i].id > 0) { d = sqrt(pow(xNew-f2[matches[i].id-1].x,2)+pow(yNew-f2[matches[i].id-1].y,2)); if (d < epsilon) nInliers++; nMatches++; }}return (nInliers / (float)nMatches);