Process of Canny Edge Detection Algorithm

Process of Canny edge detection algorithm

The Process of Canny edge detection algorithm can be broken down to 5 different steps: Apply Gaussian filter to smooth the image in order to remove the noise Find the intensity gradients of the image Apply non-maximum suppression to get rid of spurious response to edge detection Apply double threshold to determine potential edges Track edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges.Gaussian filterSince all edge detection results are easily affected by the noise in the image, it is essential to filter out the noise to prevent false detection caused by it. To smooth the image, a Gaussian filter kernel is convolved with the image. This step will slightly smooth the image to reduce the effects of obvious noise on the edge detector. The equation for a Gaussian filter kernel of size (2k1)(2k1) is given by: H i j = 1 2 2 exp ( ( i ( k 1 ) ) 2 ( j ( k 1 ) ) 2 2 2 ) ; 1 i , j ( 2 k 1 ) displaystyle H_ij=frac 12pi sigma ^2exp left(-frac (i-(k1))^2(j-(k1))^22sigma ^2

ight);1leq i,jleq (2k1) Here is an example of a 55 Gaussian filter, used to create the adjacent image, with displaystyle sigma = 1. (The asterisk denotes a convolution operation.) B = 1 159 [ 2 4 5 4 2 4 9 12 9 4 5 12 15 12 5 4 9 12 9 4 2 4 5 4 2 ] A . displaystyle mathbf B =frac 1159beginbmatrix2&4&5&4&24&9&12&9&45&12&15&12&54&9&12&9&42&4&5&4&2endbmatrix*mathbf A . It is important to understand that the selection of the size of the Gaussian kernel will affect the performance of the detector. The larger the size is, the lower the detector's sensitivity to noise. Additionally, the localization error to detect the edge will slightly increase with the increase of the Gaussian filter kernel size. A 55 is a good size for most cases, but this will also vary depending on specific situations. Finding the intensity gradient of the imageAn edge in an image may point in a variety of directions, so the Canny algorithm uses four filters to detect horizontal, vertical and diagonal edges in the blurred image. The edge detection operator (such as Roberts, Prewitt, or Sobel) returns a value for the first derivative in the horizontal direction (Gx) and the vertical direction (Gy). From this the edge gradient and direction can be determined: G = G x 2 G y 2 displaystyle mathbf G =sqrt mathbf G _x^2mathbf G _y^2 = atan2 ( G y , G x ) displaystyle mathbf Theta =operatorname atan2 left(mathbf G _y,mathbf G _x

ight) ,where G can be computed using the hypot function and atan2 is the arctangent function with two arguments. The edge direction angle is rounded to one of four angles representing vertical, horizontal and the two diagonals (0, 45, 90 and 135). An edge direction falling in each color region will be set to a specific angle values, for instance in , 22.5] or .5, 180] maps to 0. Non-maximum suppressionNon-maximum suppression is an edge thinning technique. Non-maximum suppression is applied to find "the largest" edge. After applying gradient calculation, the edge extracted from the gradient value is still quite blurred. With respect to criterion 3, there should only be one accurate response to the edge. Thus non-maximum suppression can help to suppress all the gradient values (by setting them to 0) except the local maxima, which indicate locations with the sharpest change of intensity value. The algorithm for each pixel in the gradient image is: Compare the edge strength of the current pixel with the edge strength of the pixel in the positive and negative gradient directions. If the edge strength of the current pixel is the largest compared to the other pixels in the mask with the same direction (e.g., a pixel that is pointing in the y-direction will be compared to the pixel above and below it in the vertical axis), the value will be preserved. Otherwise, the value will be suppressed.In some implementations, the algorithm categorizes the continuous gradient directions into a small set of discrete directions, and then moves a 3x3 filter over the output of the previous step (that is, the edge strength and gradient directions). At every pixel, it suppresses the edge strength of the center pixel (by setting its value to 0) if its magnitude is not greater than the magnitude of the two neighbors in the gradient direction. For example, if the rounded gradient angle is 0 (i.e. the edge is in the north-south direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the east and west directions, if the rounded gradient angle is 90 (i.e. the edge is in the east-west direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north and south directions, if the rounded gradient angle is 135 (i.e. the edge is in the northeast-southwest direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north west and south-east directions, if the rounded gradient angle is 45 (i.e. the edge is in the north west-south east direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north east and south west directions.In more accurate implementations, linear interpolation is used between the two neighbouring pixels that straddle the gradient direction. For example, if the gradient angle is between 89 and 180, interpolation between gradients at the north and north east pixels will give one interpolated value, and interpolation between the south and south west pixels will give the other (using the conventions of the last paragraph). The gradient magnitude at the central pixel must be greater than both of these for it to be marked as an edge. Note that the sign of the direction is irrelevant, i.e. north-south is the same as south-north and so on. Double thresholdAfter application of non-maximum suppression, remaining edge pixels provide a more accurate representation of real edges in an image. However, some edge pixels remain that are caused by noise and color variation. In order to account for these spurious responses, it is essential to filter out edge pixels with a weak gradient value and preserve edge pixels with a high gradient value. This is accomplished by selecting high and low threshold values. If an edge pixel's gradient value is higher than the high threshold value, it is marked as a strong edge pixel. If an edge pixel's gradient value is smaller than the high threshold value and larger than the low threshold value, it is marked as a weak edge pixel. If an edge pixel's value is smaller than the low threshold value, it will be suppressed. The two threshold values are empirically determined and their definition will depend on the content of a given input image. Edge tracking by hysteresisSo far, the strong edge pixels should certainly be involved in the final edge image, as they are extracted from the true edges in the image. However, there will be some debate on the weak edge pixels, as these pixels can either be extracted from the true edge, or the noise/color variations. To achieve an accurate result, the weak edges caused by the latter reasons should be removed. Usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while noise responses are unconnected. To track the edge connection, blob analysis is applied by looking at a weak edge pixel and its 8-connected neighborhood pixels. As long as there is one strong edge pixel that is involved in the blob, that weak edge point can be identified as one that should be preserved.