Establishment of cellular automata image model and its application in image dimension measurement

Peng, Fei; Wang, Shuqiang; Liang, Shuo

doi:10.1186/s13640-018-0404-5

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…First, the kernel function is constructed by introducing the gaussian radial basis kernel and polynomial kernel to ensure that the least square support vector machine can accurately fit the pixel gray value. Then, the gradient value is obtained through the convolution of gray value, and the cellular automatic part is used to detect the edge of the image through the evolutionary gradient value, which proves that the detection performance of this algorithm is better than the traditional algorithm [6]. Yuan et al proposed an image edge detection method based on quantum algorithm, designed a quantum comparator, made full use of quantum parallelism, and analyzed all steps of quantum circuit.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator

Kanchanatripop

Zhang

2020

Symmetry

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In order to improve the accuracy of image edge detection, this paper studies the adaptive image edge detection technology based on discrete algorithm and classical Canny operator. First, the traditional sub-pixel edge detection method is illustrated based on the related literature research. Then, Canny operator is used for detection, the edge model of the quadric curve is established using discrete data, and the adaptive image edge parameters are obtained using one-dimensional gray moment. Experimental results show that the accuracy of feature detection is 99%, which can be applied to the practice of image edge detection to a certain extent.

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Section: Related Workmentioning

confidence: 99%

Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator

Kanchanatripop

Zhang

2020

Symmetry

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“…With the image processing method, dimensional measurements, position values, and geometric structures of many materials can be determined. [22][23][24] For these purposes, a free-form surface was milled with a ball and mill tool. During milling, the tool was properly illuminated and an image was taken with an industrial camera.…”

Section: Introductionmentioning

confidence: 99%

“…With the image processing method, dimensional measurements, position values, and geometric structures of many materials can be determined. 22–24…”

Section: Introductionmentioning

confidence: 99%

Prediction of tool deflection using image processing in ball-end milling

Özdemir

Bahçe

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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In milling, some of the factors that contribute to the poor quality of products are the cutting forces. Depending on the machining parameters, the cutting forces may significantly affect the tool being used in the machining process. Tool deflection can be modeled as bending deformation. Tool deflection causes poor surface quality, geometrical and dimensional errors. For this reason, it must be addressed during milling and reduced by changing the machining parameters. In the determination of tool deflection, force-based analytical and finite element methods (FEM) and sensor measurement methods are widely used. These technologies have drawbacks such as not being able to obtain fast data, being expensive, demanding precise control, and requiring continual calibration. This study aims to determine the deflection of the tool by image processing dependent on the tool/material pair and machining parameters in the milling process. For this purpose, the AL7075 material with a free-form surface was machined on a CNC milling machine. A mathematical equation is proposed to estimate the tool deflection based on the image processing results. The method has shown that tool deviation can be detected more quickly and simply by image processing.

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Establishment of cellular automata image model and its application in image dimension measurement

Cited by 2 publications

References 30 publications

Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator

Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator

Prediction of tool deflection using image processing in ball-end milling

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