Abstract
Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny’s superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application’s circumstances requires additional preprocessing, which increases the algorithm’s computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection’s performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.
Highlights
Realtime video and image processing are used in a wide range of industrial, medical, consumer electronics and embedded device applications
The approach presented in this paper aims to make the Canny edge detection algorithm more noise robust with the least computational complexity
The Threshold value is updated according to the noise intensity, the edge detection performance for the noisy images is found to be slightly better than the approach that works with fixed parameters
Summary
Realtime video and image processing are used in a wide range of industrial, medical, consumer electronics and embedded device applications. In one end of the spectrum, there are approaches which calculate and set the parameters’ values dynamically for every input image, based on the image characteristics (such as the gradient magnitude histogram) [7]–[9] This technique, which is referred to as ‘‘variable-threshold‘‘ in this paper (since there is no research reporting on dynamically calculating the Sigma value), improves the edge detection performance, imposes a very high computational complexity. On the other end, to tackle the computation workload problem, there are researchers who use fixed values for the Canny parameters [10]–[12] While this approach confines the algorithm complexity, it diminishes the performance when environmental conditions and application requirements constantly change.
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