Development of a low cost yarn parameterisation unit by image processing

Sengupta, Anindita; Roy, Subhasish; Sengupta, Surajit

doi:10.1016/j.measurement.2014.09.028

Cited by 29 publications

(11 citation statements)

References 8 publications

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“…Edge is the region of mutation on the grey of the image which represents the beginning or the end of the pits [16]. e background can be split from the pit edge by detecting the pit edge, and then, the information of the area of the pits and so on can be obtained.…”

Section: Edge Detectionmentioning

confidence: 99%

Study of Cavitation and Cavitation Erosion Quantitative Method Based on Image Processing Technique

Lian

Zhang

2018

Advances in Civil Engineering

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Cavitation erosion on the wetted surface of hydraulic machinery is directly related to the cavitation behavior. In this paper, the cavitation behavior and cavitation erosion characteristics on the airfoil surface were observed experimentally, and then, image processing methods for quantifying cavitation structure and cavitation erosion were established. Laser-CCD system was used to obtain the cavitation structure on the airfoil surface and the microtopographies of the cavitation erosion at different magnifications were obtained by SEM. The distribution and shape of cavitation pits were analyzed. An image processing method based on statistical principle was used to analyze the distribution characteristics of the cavitation structure. The mean and mean square value of the cavitation structure were obtained. The average volume and the volume change rate of cavitation cloud in each position of the flow field during a cavitation period were described. According to the characteristics of cavitation pits, an image processing method based on background correction, edge detection, and binary morphology processing was established, and then, the distribution characteristics and the area of the cavitation pits were obtained. Finally, the effectiveness of the methods is verified by the image processing of cavitation pit in different locations on the hydrofoil.

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Section: Edge Detectionmentioning

confidence: 99%

Study of Cavitation and Cavitation Erosion Quantitative Method Based on Image Processing Technique

Lian

Zhang

2018

Advances in Civil Engineering

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“…The module is shown in Figure 1. Photoelectric sensor consists with the light source, reflective beam receiver and through beam receiver [2,3]. When the light source emits light on the yarn, some will be absorbed and some will reflect.…”

Section: A Design Of Photoelectric Sensormentioning

confidence: 99%

The Design and Implementation of Photoelectric Sensor for Yarn Color Fault Detection

Sheng¹

2016

Int. J. Onl. Eng.

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Abstract-The paper mainly aimed at solving the problem of yarn color fault detection. Yarn with different color is hard to detect in yarn production, a special photoelectric sensor is designed in this paper. First, this paper analyzed the requirement of light source and photoelectric receiver in the photoelectric sensor, and designs the light path and driver circuit. Then this paper analyzed the amplifier circuit and noise in the photoelectric sensor, with an amplifier circuit of minimal noise proposed at last. Finally, this paper tested the yarn color fault detection system with virtual instrument, and the test results showed a great application prospect of the photoelectric sensor. Photoelectric yarn clearer was the first type of electronic yarn clearer, but due to the under development of the optical technology and measurement technology, the photoelectric yarn cleaner can't meet the requirements of textile production, gradually replaced by capacitive yarn cleaner. Though photoelectric yarn cleaner had a good visual conformity degree, it's still a unreplaceable method in colored yarn faults [1].

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“…Gonçalves et al (2015) used logical operations (XOR) to isolate and quantify the protruding and loop fibers from the yarn core [19]. Sengupta et al (2015) presented an algorithm including nine steps in their study [20]. Li et al (2016) proposed an image acquisition and processing system with a video camera to evaluate yarn appearance.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

Telli

2021

Tekstil Ve Konfeksiyon

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The resolution, quality and speed of the cameras have improved enormously in recent years. The combination of camera advancements and the software industry offers significant opportunities. In this study, an image processing approach for the determination of yarn hairiness was presented. Yarn images taken under a microscope were examined in MATLAB software. Seven different edge detection algorithms were used in order to separate the hairs from the yarn body. Seven different textural properties of obtained yarn images were compared with Zweigle hairiness test results. The findings have indicated that yarn hairiness can be clearly detected from microscope images with a six-step algorithm. The first four phases are grayscale, double format, 2D median filtering and histogram-fitting, respectively. The fifth stage is the edge detection algorithm and the sixth stage is the use of textural parameters. When compared with the Zweigle hairiness results, the most obvious finding to emerge from this study is that the best appropriate technique for edge detection was the Sobel method, and the textural parameter to be used in the evaluation was the standard deviation of matrix elements.

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Development of a low cost yarn parameterisation unit by image processing

Cited by 29 publications

References 8 publications

Study of Cavitation and Cavitation Erosion Quantitative Method Based on Image Processing Technique

Study of Cavitation and Cavitation Erosion Quantitative Method Based on Image Processing Technique

The Design and Implementation of Photoelectric Sensor for Yarn Color Fault Detection

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

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