A Low Redundancy Wavelet Entropy Edge Detection Algorithm
Article
Article Title | A Low Redundancy Wavelet Entropy Edge Detection Algorithm |
---|---|
ERA Journal ID | 213284 |
Article Category | Article |
Authors | Tao, Yiting, Scully, Thomas, Perera, Asanka G., Lambert, Andrew and Chahl, Javaan |
Journal Title | Journal of Imaging |
Journal Citation | 7 (9) |
Article Number | 188 |
Number of Pages | 19 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2313-433X |
Digital Object Identifier (DOI) | https://doi.org/10.3390/jimaging7090188 |
Web Address (URL) | https://www.mdpi.com/2313-433X/7/9/188 |
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. |
Keywords | Shannon entropy; wavelet decomposition; edge detection |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4603. Computer vision and multimedia computation |
Byline Affiliations | University of South Australia |
University of New South Wales | |
Defence Science and Technology Group, Australia |
https://research.usq.edu.au/item/z77wy/a-low-redundancy-wavelet-entropy-edge-detection-algorithm
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