Effective subpixel edge detection for LED probes

Su, Chung-Yen; Lan, Yu; Chen, Nai-Kuei

doi:10.1109/smc.2016.7844270

Cited by 6 publications

(2 citation statements)

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“…To improve measurement accuracy, sub-pixel boundary extraction methods based on pixellevel boundary extraction approach are always adopted. Interpolation [14,15], moment estimation [16][17][18] and fitting [19][20][21] are the most common methods used in sub-pixel extraction. The image processing algorithm adopted in this paper is shown in Fig.…”

Section: Measurement Algorithmmentioning

confidence: 99%

Metrology and Measurement Systems

Su,

Cai,

et al. 2021

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When machine tool spindles are running at a high rotation speed, thermal deformation will be introduced due to the generation of large amounts of heat, and machining accuracy will be influenced as a result, which is a generalized issue in numerous industries. In this paper, a new approach based on machine vision is presented for measurements of spindle thermal error. The measuring system is composed of a Complementary Metal-Oxide-Semiconductor (CMOS) camera, a backlight source and a PC. Images are captured at different rotation angles during end milling process. Meanwhile, the Canny edge detection and Gaussian sub-pixel fitting methods are applied to obtain the bottom edge of the end mill which is then used to calculate the lowest point coordinate of the tool. Finally, thermal extension of the spindle is obtained according to the change of the lowest point at different time steps of the machining process. This method is validated through comparison with experimental results from capacitive displacement sensors. Moreover, spindle thermal extension during the processing can be precisely measured and used for compensation in order to improve machining accuracy through the proposed method.

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Section: Measurement Algorithmmentioning

confidence: 99%

Metrology and Measurement Systems

Su,

Cai,

et al. 2021

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“…Nalwa et al [9] proposed a sub-pixel edge detection algorithm base on Hyperbolic tangent fitting edge model; Ye et al [10] proposed an algorithm to locate the position of a sub-pixel edge by two-dimensional Gaussian fitting edge model. Nalwa et al [9] proposed an algorithm to locate the position of a sub-pixel edge by Bessel fitting edge model and Su et al [11] proposed an algorithm to locate the position of a sub-pixel edge by parabolic fitting edge model. The fitting sub-pixel edge detection algorithm is robust to noise, but it is difficult to apply to real-time online detection because of its long calculation time.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

et al. 2019

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The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the 3×3 neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the 3×3 neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).INDEX TERMS Image processing, image edge detection, gray relation analysis, gray entropy theory, textural feature analysis.

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