CS-F441: S ELECTED TOPICS FROM COMPUTER S CIENCE (DEEP L EARNING FOR NLP & CV) Lecture-KT-08: Canny Edge Detector, Feature Engineering Dr. Kamlesh Tiwari, Assistant Professor, Department of Computer Science and Information Systems, BITS Pilani, Rajasthan-333031 INDIA Oct 30, 2019 (Campus @ BITS-Pilani July-Dec 2019)
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It makes the edge point only, not the information about the edgeorientation.It works well in binary images.The Roberts method finds edges using the Roberts approximationto the derivative.√
Standard edge detector: Maximizes probability of detecting real edgeswhile minimizing the probability of false detection of non-edge points.
Five separate steps:
1. Smoothing: Blurring of the image to remove noise. (by applying aGaussian filter). The kernel of a Gaussian filter with a standarddeviation of σ = 1.4 is given by
Standard edge detector: Maximizes probability of detecting real edgeswhile minimizing the probability of false detection of non-edge points.
Five separate steps:
1. Smoothing: Blurring of the image to remove noise. (by applying aGaussian filter). The kernel of a Gaussian filter with a standarddeviation of σ = 1.4 is given by
2. Finding gradients: The edges should be marked where thegradients of the image has large magnitudes (by applyingSobel-operator).
3. Non-maximum suppression: Only local maxima should bemarked as edges.
1 Round the gradient direction to nearest 45◦, corresponding to theuse of an 8-connected neighborhood.
2 Compare the edge strength of the current pixel with the edgestrength of the pixel in the positive and negative gradient direction.i .e. if the gradient direction is north (theta = 90◦), compare with thepixels to the north and south.
3 If the edge strength of the current pixel is largest; preserve thevalue of the edge strength. If not, suppress (i .e. remove) the value.
2. Finding gradients: The edges should be marked where thegradients of the image has large magnitudes (by applyingSobel-operator).
3. Non-maximum suppression: Only local maxima should bemarked as edges.
1 Round the gradient direction to nearest 45◦, corresponding to theuse of an 8-connected neighborhood.
2 Compare the edge strength of the current pixel with the edgestrength of the pixel in the positive and negative gradient direction.i .e. if the gradient direction is north (theta = 90◦), compare with thepixels to the north and south.
3 If the edge strength of the current pixel is largest; preserve thevalue of the edge strength. If not, suppress (i .e. remove) the value.
4. Double thresholding: Edge pixels stronger than the highthreshold are marked as strong; edge pixels weaker than the lowthreshold are suppressed and edge pixels between the twothresholds are marked as weak. Strong edges are white, whileweak edges are grey. Edges with a strength below boththresholds are suppressed
5. Edge tracking by hysteresis: Final edges are determined bysuppressing all edges that are not connected to a very certain(strong) edge. Strong edges are interpreted as certain edges, andare included in the final edge image. Weak edges are included ifand only if they are connected to strong edges.
4. Double thresholding: Edge pixels stronger than the highthreshold are marked as strong; edge pixels weaker than the lowthreshold are suppressed and edge pixels between the twothresholds are marked as weak. Strong edges are white, whileweak edges are grey. Edges with a strength below boththresholds are suppressed
5. Edge tracking by hysteresis: Final edges are determined bysuppressing all edges that are not connected to a very certain(strong) edge. Strong edges are interpreted as certain edges, andare included in the final edge image. Weak edges are included ifand only if they are connected to strong edges.
Compute gradient of each point in the imageCreate the H matrix using gradientsCompute EigenvaluesFind points with large response (λ > threshold)Choose those points where λ is a local maximum as feature point