Learning OpenCV

Canny

The method just described for finding edges was further refined by J. Canny in 1986 into what is now commonly called the Canny edge detector [Canny86]. One of the differences between the Canny algorithm and the simpler, Laplace-based algorithm from the previous section is that, in the Canny algorithm, the first derivatives are computed in x and y and then combined into four directional derivatives. The points where these directional derivatives are local maxima are then candidates for assembling into edges.

Figure 6-6. Laplace transform (upper right) of the racecar image: zooming in on the tire (circled in white) and considering only the x-dimension, we show a (qualitative) representation of the brightness as well as the first and second derivative (lower three cells); the 0s in the second derivative correspond to edges, and the 0 corresponding to a large first derivative is a strong edge

However, the most significant new dimension to the Canny algorithm is that it tries to assemble the individual edge candidate pixels into contours. ^[67] These contours are formed by applying an hysteresis threshold to the pixels. This means that there are two thresholds, an upper and a lower. If a pixel has a gradient larger than the upper threshold, then it is accepted as an edge pixel; if a pixel is below the lower threshold, it is rejected. If the pixel's gradient is between the thresholds, ...