Abstract
The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.
Highlights
Object contours play an important role in human visual processing and their composition presents some meaningful geometric concepts
Gao and Liu [10] used the Otsu method to calculate the gradient histogram to obtain the adaptive high and low thresholds, where the low threshold was set to one-half of the high threshold; Song et al [11] used the Otsu method to perform two operations on the gradient magnitude histogram to obtain the adaptive high and low thresholds; Saheba et al [12] used the mean squared error (MSE)
It can be observed from the experimental results that the proposed texture construction edge detection algorithm (TCEDA) can construct the edge information of the object by retaining the effective edge texture change, which effectively improves the edge loss caused by unsuitable threshold setting
Summary
Object contours play an important role in human visual processing and their composition presents some meaningful geometric concepts. Song et al [11] used the Otsu method to analyze the gradient magnitude histogram after non-maximum suppression They first obtained the high-threshold value and excluded the gradient value greater than the high threshold and performed the second operation on the statistical result to obtain a low threshold. The gradient magnitude histogram proposed by Li and Zhang [14] uses a differential operation to obtain the adaptive high and low thresholds after non-maximum suppression. Ferdous et al [15] used the method proposed by Rupalatha et al [16] to adaptively solve the high and low thresholds by the operation of two statistical values in the gradient histogram after non-maximum suppression: one is the average value of the probability density function and the other is the variation in the probability density function. The rest of the paper is organized as follows: Section 2 introduces the TCEDA and process architecture; Section 3 shows the experimental results of the TCEDA and the adaptive threshold value algorithms; and Section 4 summarizes the contribution and follow-up of the TCEDA and applications
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