Abstract
Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.
Highlights
Edges of an image are considered to be a low-level image feature
In addition to Dice coefficient, we tested our algorithm with a boundary F1 score (BFS) [60], Jaccard coefficient (JC) [60], and Pratt’s figure of merit (FOM) [61] metrics to analyze the performance
We have developed a method for edge detection using a combination of the Wavelet transform, Shannon entropy and thresholding
Summary
Edges of an image are considered to be a low-level image feature. They are undoubtedly one of the most important features in an image. Prevalent edge detection methods such as Canny [12], Prewitt [13], Roberts [14], Sobel [15] and Zerocross [16] have proven popular due to their efficiency and ease of implementation, making them useful for real world image processing applications [11,17,18]. We propose a new edge detection method based on the coupling of wavelet and entropy based techniques. Entropy is used to assess the multi-scale decomposition data and determine which wavelet decomposition level contains the greatest image structure This process produces a low redundancy edge detection output image with a significant increase in computational efficiency.
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