Sub-pixel edge detection algorithm based on Gauss fitting

Shang, Ya-ceng; Chen, Jing; Tian, Junwei

doi:10.3724/sp.j.1087.2011.00179

Cited by 15 publications

(5 citation statements)

References 1 publication

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“…To enhance edge detection in conveyor belt images, this paper utilizes the Canny sub-pixel edge detection algorithm. It maintains edge continuity and controls the probability of errors while achieving sub-pixel accuracy [10], [11] .…”

Section: Canny Sub-pixel Edge Detectionmentioning

confidence: 99%

Adaptive conveyor belt edge detection algorithm with enhanced robustness

Li,

Hou,

Chen

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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Production safety has always been a paramount concern for coal preparation plants. With the widespread availability of video capture equipments, using monitoring videos to enhance the warning capabilities and detection functionalities for equipment malfunctions has become a priority. This paper will focus on the problem of the conveyor belt deviation detection which occurs frequently in the coal preparation plants. Nowadays, most conveyor belt edge detection algorithms can only detect straight edges and cannot adapt to edge shapes. In addition, some algorithms cannot be applied in scenarios with external influences such as strong changes in illumination, excessive image exposure, or water accumulation. In response to these shortcomings, this paper proposes an algorithm with enhanced robustness that can adaptively detect conveyor belt edges. Experiments show that this algorithm has good illumination equalization effect, edge detection effect and accuracy, deviation detection accuracy, and works well in various situations. At the same time, this algorithm has already been successfully implemented for use in coal mining production.

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Section: Canny Sub-pixel Edge Detectionmentioning

confidence: 99%

Adaptive conveyor belt edge detection algorithm with enhanced robustness

Li,

Hou,

Chen

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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“…erefore, research on subpixel edge detection is essential. Current subpixel detection methods include the probability theory method, demodulation measurement method, and polynomial interpolation method [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Research on Subpixel Algorithm of Fixed‐Point Tool Path Measurement

Zhang

Guo

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Tool safety is an important part of machining and machine tool safety, and machine tool path image detection can effectively obtain the in-machine condition of a tool. To obtain an accurate image edge and improve image processing accuracy, a novel subpixel edge detection method is proposed in this study. The precontour is segmented by binarization, the second derivative in the neighborhood of the demand point is calculated, and the obtained value is sampled according to the specified rules for curve fitting. The point whose curve ordinate is 0 is the subpixel position. The experiment proves that an improved subpixel edge can be obtained. Results show that the proposed method can extract a satisfactory subpixel contour, which is more accurate and reliable than the edge results obtained by several current pixel-level operators, such as the Canny operator, and can be used in edge detection with high-accuracy requirements, such as the contour detection of online tools.

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“…Therefore, edge detection is an important processing step in image processing and the effect of edge extraction directly determines the image quality of the subsequent processing. At present, there are many methods of edge detection which has been the hotspot in image processing [2][3][4][5][6]. We expect to find a method for strong anti-noise, accurate positioning, which can not produce missing and error detection.…”

Section: Introductionmentioning

confidence: 99%

An Edge Detection Method Based on Improved Sobel Operator

Sun

Zhao

et al. 2014

AMR

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The edge detection which comes from the classical Sobel operator in the image is based on the horizontal gradient and vertical gradient direction. On the basis of the template from two directions the method which is to detect the edges of eight directions is discussed in this paper. The image edge detection based on multiple directions can improve the accuracy of edge detection. The numerical experimental results show that the proposed method can get the smooth, continuous, multiple directions edges, and can reduce the loss of the information. The method proposed in this paper is to detect the edge of the image, which will result in higher accuracy. Especially for more complex texture image, the effect of edge detection is more obvious, and the pixel width is closer to a single pixel width. It is an effective method for edge detection.

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Sub-pixel edge detection algorithm based on Gauss fitting

Cited by 15 publications

References 1 publication

Adaptive conveyor belt edge detection algorithm with enhanced robustness

Adaptive conveyor belt edge detection algorithm with enhanced robustness

Research on Subpixel Algorithm of Fixed‐Point Tool Path Measurement

An Edge Detection Method Based on Improved Sobel Operator

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