# Texture Construction Edge Detection Algorithm
## 1 Introduction  
Object contours play an important role in human visual processing and their composition presents some meaningful geometric concepts. By defining the contour shape, people can effectively and widely identify various objects. The contour of an object does not change with color or ambient brightness, so the object information is stable. It is widely used in object segmentation, object detection, texture identification, and feature analysis, and is popular in fields such as security monitoring systems, smart home systems, and smart vehicles. Besides the shape of the outer contour of the object, subtle texture changes inside the object also imply important object information, so dominant or implicit texture changes should be selected or retained as each possible object identification feature. Edge detection technology is the main method used to detect the contour of objects. Recently, many experts and scholars have been working on various image edge detection technologies. However, finding rich object edges from target images is still a challenging and popular topic. The traditional edge detection technology developed in the early days mainly aimed to find the discontinuity in the gray-level intensity of a pixel. After obtaining the gradient information by a first- or second-order differential operation, the intensity difference between the center and adjacent pixels was observed and analyzed to obtain the edge. For example, [PERSON]'s operator [1] uses a 2 \(\times\) 2 mask to calculate the difference between adjacent pixels in the diagonal direction to obtain the gradient information and determines the edge retention through a threshold setting. Because the mask used is 2 \(\times\) 2 in size and has no clear center, resulting in an inaccurate range of values, the Prewitt operator [2] was developed, which uses a 3 \(\times\) 3 mask for gradient operations, combining horizontal and vertical gradients. In this operator, the component obtains the gradient information of the entire image, and finally, retains the edges through a threshold setting. The Sobel [3] operator is similar to the Prewitt [2] operator. One slight difference between the operators is that the mask center uses the weighting coefficient value and some noise suppression is achieved by giving the center point a larger weighting value. Finally, one still has to keep the edges through a threshold setting. Because the setting of the threshold value directly affects the output of the edge detection, some scholars have proposed an adaptive threshold valuemethod to solve the error caused by manual adjustment. [PERSON] and [PERSON] [4] proposed the combination of Otsu [5] and Sobel [3] algorithms to achieve image filtering, and used the Watershed algorithm to achieve image segmentation. [PERSON] et al. [6] used the adaptive threshold method proposed by [PERSON] and [PERSON] [7] to calculate the percentage of the average grayscale value of the mask using the integral image, as compared to the center pixel, as the setting of the image binarization threshold, and finally, applied the binarized image to object identification. [PERSON] et al. [8] calculated and retained the maximum of the two statistical values as the basis for determining the edge retention of the adaptive threshold. One is the sum of the gradient average and the one-half standard deviation in the mask and the other is a custom threshold. The calculation of the adaptive threshold value significantly improves the detection effect of the edge detection algorithm, and effectively avoids the error caused by manual operation. However, since the acquisition of a single threshold value only depends on the statistical result, when the image grayscale variations are not obvious, the high-threshold setting may cause the less-obvious edge texture to be filtered out. On the contrary, the low-threshold setting may cause too much noise or too many false edges to be retained.