An improved industrial sub-pixel edge detection algorithm based on coarse and precise location

Xie, Xin; Ge, Songlin; Xie, Mingye; Hu, Fengping; Jiang, Nan

doi:10.1007/s12652-019-01232-2

Cited by 56 publications

(21 citation statements)

References 23 publications

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“…As shown in Figure 8, a sub-pixel edge point extraction map of the O-ring is shown. Since the sub-pixel edge points cannot be represented in the pixel map, the coordinate points are plotted to visually reflect the sub-pixel edge extraction results [23].…”

Section: Sub-pixel Level Edge Measurement Algorithm Based On Cubic Spline Interpolationmentioning

confidence: 99%

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Zheng

Yin

et al. 2021

Applied Sciences

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Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ± 100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.

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Section: Sub-pixel Level Edge Measurement Algorithm Based On Cubic Spline Interpolationmentioning

confidence: 99%

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Zheng

Yin

et al. 2021

Applied Sciences

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“…In simple computer vision applications, where the sources of uncertainty can be constrained (e.g., in industrial manufacturing lines where lighting and environmental conditions are well controlled), CD techniques such as edge detection in images are a powerful tool. For example, high precision industrial vision/sensing systems for the inspection and categorisation of objects can achieve accuracies well within the allowable tolerance of standard measurement instruments automatically, non-invasively and without requiring precise fixturing with the aid of high-resolution cameras, a lot of specialised knowledge in machine vision and edge detection [ 14 ] and sub-pixel detection techniques [ 15 ].…”

Section: Applications Of Change Detectionmentioning

confidence: 99%

Representation Learning for Fine-Grained Change Detection

Mahony

Campbell

Krpálková

et al. 2021

Sensors

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Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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“…Xie et al proposed an improved subpixel edge detection algorithm combining coarse and precise locations. The algorithm used an improved Roberts operator to detect pixel-level edges and used the Zernike moment method to locate subpixel edges, which effectively improved the detection efficiency and the detection accuracy [7]. Raheja et al proposed a fuzzy-logic-based edge detection method.…”

Section: Introductionmentioning

confidence: 99%

Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

et al. 2021

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As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

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An improved industrial sub-pixel edge detection algorithm based on coarse and precise location

Cited by 56 publications

References 23 publications

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Representation Learning for Fine-Grained Change Detection

Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

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