Wool and cashmere identification algorithm based on gray level co-occurrence matrix model

A gray-scale symbiotic matrix, wool and cashmere technology, applied in the field of wool and cashmere identification, can solve the problems of time-consuming and labor-intensive, poor measurement consistency, and high subjectivity, and achieve the effect of objective and consistent measurement results.

Active Publication Date: 2016-07-13
TIANFANG TIANJIN STANDARD TESTING TECH CO LTD +1
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Problems solved by technology

By observing the scale shape and texture details of wool and cashmere under the microscope, the inspectors qualitatively classify the components of

Method used

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  • Wool and cashmere identification algorithm based on gray level co-occurrence matrix model
  • Wool and cashmere identification algorithm based on gray level co-occurrence matrix model
  • Wool and cashmere identification algorithm based on gray level co-occurrence matrix model

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Embodiment

[0037] The present invention includes an online recognition process and a model learning process:

[0038] The on-line identification process is to perform qualitative analysis on the fiber images collected in real time, including the following steps:

[0039] (1) Acquisition of images: Using a 3-megapixel industrial-grade ccd with an Olympus CX41 biological microscope to capture images of cashmere wool fibers;

[0040] (2) Pretreatment: including two aspects,

[0041] a Gaussian filter is used to smooth and filter the image to remove noise in the image; Gaussian filter is a low-pass filter, and its process can be formally expressed as input image I(x,y) and Gaussian kernel function G( Convolution of x,y):

[0042] S(x,y)=I(x,y)×G(x,y;σ), where G ( x , y ) = 1 2 πσ 2 exp ...

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Abstract

A wool and cashmere identification algorithm based on a gray level co-occurrence matrix model comprises an on-line identification process and a model learning process. The on-line identification process comprises the following steps: (1) taking an image of wool and cashmere fibers; (2) using a Gaussian filter to smoothly filter the image; (3) extracting a target from the image through canny-based edge detection and contour; (4) calculating the texture feature quantity, and using four statistical magnitudes, namely, contrast, energy, entropy, and correlation, to represent the texture; and (5) directly using an obtained feature vector as an input feature vector. The model learning process comprises the following steps: (1) accumulating a large number of wool and cashmere databases; (2) carrying out artificial labeling to make a machine clear about the type and location of a target fiber; (3) preprocessing and extracting the features of fiber images in the databases; and (4) adopting a three-layer artificial neural network. Through the algorithm, the type of fiber of wool and cashmere can be judged intelligently and accurately.

Description

technical field [0001] The invention belongs to the technical field of wool and cashmere recognition, in particular to a wool and cashmere recognition algorithm based on a gray scale co-occurrence matrix model. Background technique [0002] Cashmere fibers are slender, uniform and soft, and the textiles made of them are soft, smooth and warm, and are the first choice for high-end clothing. Due to its scarce output and high price, manufacturers often use different proportions of cashmere wool for blending. Both wool and cashmere are natural protein fibers, and their structures and shapes are very similar. It is very difficult to accurately judge the fiber type. [0003] The most commonly used fiber identification method is microscope observation. By observing the scale shape and texture details of wool and cashmere under the microscope, the inspectors qualitatively classify the components of cashmere wool according to their personal experience. This method is not only time-...

Claims

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Application Information

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IPC IPC(8): G06K9/60G06K9/62
CPCG06V10/20G06F18/24
Inventor 单学蕾俞浩谢自力葛传兵魏俊玲孙学艳谢勇
Owner TIANFANG TIANJIN STANDARD TESTING TECH CO LTD
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