Color grading of beef fat by using computer vision and support vector machine

Chen, K.; Sun, Xiaorui; Qin, Ch.; Tang, Xiaochen

doi:10.1016/j.compag.2009.08.006

Cited by 79 publications

(31 citation statements)

References 14 publications

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“…The prediction coefficients (R 2 ) of volume shrinkage, surface area, and major axis were 0.684, 0.674, and 0.745, respectively. The color scores of beef fat were obtained using computer vision and a support vector machine (SVM) by Chen et al (2010), who selected 120 beef rib eye steaks for sensory evaluation and image processing. By boundary tracking, morphological operations, and thresholding, subcutaneous fat of rib eye were successfully segmented.…”

Section: Beefmentioning

confidence: 99%

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

Sun

et al. 2014

Critical Reviews in Food Science and Nutrition

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With consumer concerns increasing over food quality and safety, the food industry has begun to pay much more attention to the development of rapid and reliable food-evaluation systems over the years. As a result, there is a great need for manufacturers and retailers to operate effective real-time assessments for food quality and safety during food production and processing. Computer vision, comprising a nondestructive assessment approach, has the aptitude to estimate the characteristics of food products with its advantages of fast speed, ease of use, and minimal sample preparation. Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting defects, and estimating properties such as color, shape, size, surface defects, and contamination. Therefore, in order to track the latest research developments of this technology in the agri-food industry, this review aims to present the fundamentals and instrumentation of computer vision systems with details of applications in quality assessment of agri-food products from 2007 to 2013 and also discuss its future trends in combination with spectroscopy.

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Section: Beefmentioning

confidence: 99%

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

Sun

et al. 2014

Critical Reviews in Food Science and Nutrition

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“…Classic threshold value method has the following four: minimum points threshold value method and optimal threshold search method, the iterative threshold value method and OTSU method. Through the method of one or several, in general are able to choose the appropriate threshold and make an accurate picture of segmentation [8,9].…”

Section: B Threshold Segmentationmentioning

confidence: 99%

On-Line Detection of Hami Big Jujubes' Size and Shape Based on Machine Vision

Xiao

2011

2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

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In this paper , we present a on-line machine vision system for Hami big jujubes' size and shape detection, which solved the problems such as fast processing the large amount of image information, improving system performance for real time dynamic image capture and processing capability, increasing precision of detection etc. Based on the application of Visual Basic 6.0 designed the user's interface . SOLIOS images gathering card linked with the CCD camera to collect image data, Which consisted united application of MIL9.0 and VB on Auto-Detecting of the Hami big jujubes' size and shape. With image processing control of ActiveMil 9.0, comparison of the algorithm and verified, we can find the feature extraction to distinguish the rapid defects about the size and shape. During the image processing, many methods were used to identify the quality defects as soon as possible, such as application of wavelet denoising, threshold segmentation, mathematical morphology processing methods etc. A Hierarchical Clustering Analysis method was used to realize the recognition and to determine the classification ， which the recognition rate can research 83%.

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“…One-against-one and one-against-all are two main strategies in constructing a multi-class SVM (MSVM) by combining several binary SVMs. MSVM had been practically used in pattern recognition, i.e., color grading of beef fat [13], classification of pizza [11], and automatic classification of fruits and vegetables [14].…”

Section: Introductionmentioning

confidence: 99%

Fast recognition of foreign fibers in cotton lint using machine vision

Yang¹,

Lu²,

Wang³

et al. 2011

Mathematical and Computer Modelling

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a b s t r a c tThis paper presents an approach for fast segmentation of foreign fiber images and precise recognition of foreign fiber objects using machine vision. Live images were acquired in real time using a line scan CCD camera. After an image was acquired it was transferred to a host computer immediately for image processing and object classification. The captured image was firstly segmented according to the mean and standard deviation of R, G and B values of each pixel in the image. Then noises were removed using the area threshold method. Afterwards, color features, shape features and texture features of each foreign fiber object were extracted. Finally, a one-against-one directed acyclic graph multi-class support vector machine (OAO-DAG MSVM) was constructed and used to perform the classification.The results indicate that the image processing algorithm is fast and precise; the OAO-DAG MSVM gets a mean accuracy of 92.34% and a mean classification time of 12 ms, which can satisfy the accuracy and speed requirement of online classification of foreign fibers.

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Color grading of beef fat by using computer vision and support vector machine

Cited by 79 publications

References 14 publications

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

On-Line Detection of Hami Big Jujubes' Size and Shape Based on Machine Vision

Fast recognition of foreign fibers in cotton lint using machine vision

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