Edge detection of digital image with different edge types

Ruslau, Maria F. V.; Pratama, Rian Ade; Meirista, Etriana

doi:10.1088/1742-6596/1569/4/042069

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…Due to the structural difference of the surface of the flat back cover, the intensity and angle of the reflected light in different areas are different [19]: The highest reflected light intensity and smallest reflected light deviation are found in A1, whereas the lowest reflected light intensity and greatest reflected light deviation are found in the R area. Figure 6a Distinct sections of the flat back cover exhibit distinct gray distributions when imaged as a result of differences in surface-reflected light, and the regional border is visible as an edge feature in the image [20]. On this basis, the local image of the R region is qualitatively simulated, as shown in Figure 6b.…”

Section: Theoretical Analysis Of Surface Imaging and Image Characteri...mentioning

confidence: 98%

Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision

Wang

Yang

et al. 2023

Applied Sciences

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In view of the problem that the measurement accuracy of screen gaps is affected by the R region of the transition rounded corner of the large-size flat back cover, a mobile vision system with dual linear motor synchronous drive is designed to realize precise positioning of monocular vision in 16 local image acquisition positions set in the R region. Full-size measurement of the R region is achieved using local information fusion to accurately measure the screen gap. First, to solve the problem of edge breaking and the blurring of local images, a region extraction and segmentation method based on block statistics is proposed to quickly obtain accurate outer edges. Then, an adaptive Hough line detection method based on dichotomy is proposed to accurately locate the outer edges. The experimental results show that the accuracy of region extraction and segmentation is up to 99.68% within 60 ms; the angle error and position error of line detection are less than 0.01° and 0.2 pixels, respectively. With optimal anti-interference performance, the proposed algorithm realizes the adaptive and accurate positioning of the outer edge within 200 ms; the measurement time and the measurement error of the measurement system are less than 6.0 s and 0.03 mm, respectively.

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Section: Theoretical Analysis Of Surface Imaging and Image Characteri...mentioning

confidence: 98%

Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision

Wang

Yang

et al. 2023

Applied Sciences

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“…Initially, select an image from the corresponding category of image data set. Here we considered PattenNet dataset IMG:DS {I1, I2,….., IN} and consider the image as Ip of M rows and N columns which is loaded into the operational set for performing segmentation operations for edge detection by evaluating (1). The shadow image of dark area will be removed by using intensity of images as per ( 4) and (5).…”

Section: Explanationmentioning

confidence: 99%

“…Step edge detectors [1], have been a key component of many computer vision systems for many years. In addition, edge detection decreases the quantity of data that must be processed while keeping important structural information about object boundaries.…”

Section: Introductionmentioning

confidence: 99%

Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence

Devareddi

Srikrishna²

2023

IJ-AI

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<span lang="EN-US">An Image's Edge Detection is the process of finding and pinpointing sharp discontinuities in an image. Detecting the edges of an image significantly reduces the quantity of data and removes unnecessary information while keeping the fundamental structural aspects of an image. Edge detection is essential when it comes to image categorization in computer vision and object identification. The primary goal of this research is to investigate several strategies for edge detection and shadow of objects in Aerial view Images. Machine vision, face detection, medical imaging, and object detection are just a few examples of applications where image segmentation has been widely utilized. Image segmentation is categorizing or identifying sub-patterns in a given image. Many algorithms and strategies for picture segmentation have been presented to improve segmentation issues in a given application area. Techniques such as threshold-based and region-based picture segmentation were used in this study. An edge detection method such as Sobel, Prewitt and Roberts and the canny approach is applied to the benchmark image and compared with the proposed Octagonal Pixel Divergence Edge Detection (ODED) algorithm. MATLAB R2018b was used for this research, and findings show that the proposed approach is more effective than the other methods, with a quality image with edges. </span>

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“…In the calculation method of gradient amplitude, the two directions of the traditional algorithm are changed into four directions, which can make the edge positioning more accurate, and play a better detection effect for objects such as agricultural products. Ruslau and Pratama [22] discussed various types of edge detection. In this study, the adaptive threshold segmentation method was adopted for rough segmentation based on the characteristics of agricultural products.…”

Section: Improvement Of Reshold Calculationmentioning

confidence: 99%

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

Wang

et al. 2021

Mathematical Problems in Engineering

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The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

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Edge detection of digital image with different edge types

Cited by 6 publications

References 5 publications

Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision

Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision

Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

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