Determination of meat quality by texture analysis

Shiranita, Kazuhiko; Miyajima, Tsuneharu; Takiyama, Ryuzo

doi:10.1016/s0167-8655(98)00113-5

Cited by 66 publications

(27 citation statements)

References 15 publications

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“…Their results from the tests confirmed the effectiveness of the proposed method. Shiranita et al [22] describes a method for extracting a texture feature from a meat image using the gray level co-occurrence matrix and confirmed to be in good result.…”

Section: Original Co-occurrence Matrixmentioning

confidence: 96%

Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data

Koonsanit

Jaruskulchai

2012

IEICE Trans. Inf. & Syst.

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SUMMARYNowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the trico-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.

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Section: Original Co-occurrence Matrixmentioning

confidence: 96%

Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data

Koonsanit

Jaruskulchai

2012

IEICE Trans. Inf. & Syst.

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“…One of those researches tried to determine the quality of beef using texture analysis with the gray level cooccurrence matrix (GLCM) method [3]. Beef quality is categorized into 12 grades based on the amount of fat it contains.…”

Section: Theorymentioning

confidence: 99%

“…The quality itself is measured by four characteristics: marbling, meat color, fat color, and meat density. Specifically, marbling is the dominant parameter that determines meat's quality [3,4]. Determination of beef quality is commonly conducted visually by comparing the actual beef and reference pictures of each beef class.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an automated system based on image processing that is capable of determining beef quality is required. Some researches suggest that image processing can be applied to analyze beef color and texture that will in turn allow analysis results to be used as a reference parameter in the process of meat quality identification [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Beef Quality Identification Using Thresholding Method and Decision Tree Classification Based on Android Smartphone

Adi

Pujiyanto

Nurhayati

et al. 2017

Journal of Food Quality

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Beef is one of the animal food products that have high nutrition because it contains carbohydrates, proteins, fats, vitamins, and minerals. Therefore, the quality of beef should be maintained so that consumers get good beef quality. Determination of beef quality is commonly conducted visually by comparing the actual beef and reference pictures of each beef class. This process presents weaknesses, as it is subjective in nature and takes a considerable amount of time. Therefore, an automated system based on image processing that is capable of determining beef quality is required. This research aims to develop an image segmentation method by processing digital images. The system designed consists of image acquisition processes with varied distance, resolution, and angle. Image segmentation is done to separate the images of fat and meat using the Otsu thresholding method. Classification was carried out using the decision tree algorithm and the best accuracies were obtained at 90% for training and 84% for testing. Once developed, this system is then embedded into the android programming. Results show that the image processing technique is capable of proper marbling score identification.

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“…A wide variety of measures have been proposed related to texture properties. Among them, statistics measures are widely used in the classification and inspection of textured surfaces [10], [11], [12], [13] but although the performance of such algorithms is usually very good [14], their structure is complex and the data flow process is large. Consequently, the computation cost is high and the implementation in high speed production lines is difficult.…”

Section: Algorithmsmentioning

confidence: 99%

Reconfigurable Frame-Grabber for Real-Time Automated Visual Inspection (RT-AVI) Systems

Cuenca¹,

Ibarra²,

Álvarez³

2001

Field-Programmable Logic and Applications

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Abstract. In most of the automated systems for visual inspection tasks, real time requirements constitute an important aspect to have in to account in the design of them. Often, a frame-grabber attached to a MMXoptimised software libraries are not enough to satisfy the above requirements and it is necessary to use expensive specialised hardware and architectures. Reconfigurable hardware gives us the best of both worlds: the flexibility of software and the high performance of customised hardware. In this paper we present a reconfigurable frame-grabber concept to integrate complex real-time processing functions needed for high-speed line inspection applications directly on-board. This allows the efficient hardware-software co-design to achieve high-performance low-cost solutions .

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Determination of meat quality by texture analysis

Cited by 66 publications

References 15 publications

Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data

Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data

Beef Quality Identification Using Thresholding Method and Decision Tree Classification Based on Android Smartphone

Reconfigurable Frame-Grabber for Real-Time Automated Visual Inspection (RT-AVI) Systems

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