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Abstract
To automatically evaluate beef quality, a system must be developed to properly segment lean tissue in a sectional image of the 13th beef rib. To this end, a mobile color computer vision system and its corresponding image processing algorithms were developed for on-site application. The algorithms implement Renyi entropy and a texture index to provide adaptive thresholding. Automatic smoothing and modification followed boundary extraction, and binary morphological approaches were also taken. When 54 images of beef cut samples were assessed without manual intervention, the proposed algorithms exhibited an average boundary extraction error of 3% and an average pixel distance error of 1.8 pixels relative to assessments made by a human expert. The computing time for these sample images was of approximately 5.5 s. In addition, a user-friendly interactive man-machine interface was also developed to allow for a human expert to modin) the extracted boundary.
With growing consumer interest in meat quality, the need for accurate quality assessment becomes increasingly important. One crucial factor of Korean beef quality is the longissimus muscle area, which is closely associated with both quality and yield grade. Currently, the measurement is visually assessed, introducing subjectivity and placing a substantial burden on inspectors in terms of labor. To address these challenges, we have developed a compact image acquisition system designed to acquire accurate grading assessment images of beef carcasses. Several preprocessing steps after image acquisition were conducted, including radial distortion correction and color calibration. We have employed conventional image-processing techniques and four deep-learning models to segment the longissimus muscle area using the calibrated images. Among the segmentation models, DeepLab model based on ResNet50 achieved the highest accuracy. It demonstrated a Global Accuracy, Weighted IoU, and Mean BF Score of approximately 99.26%, 98.54%, and 95.70%, respectively. The results of our study are expected to contribute to the development of objective criteria for loin area assessment. By enabling precise and consistent determination of beef carcass quality, our research has the potential to reduce labor requirements for inspectors and provide a standardized approach to assessing loin area.
ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, NY 10036 USA, fax +1 (212) 869-0481, or permissions@acm.org. c fl 1996 by the Association for Computing Machinery, Inc. ACM Transactions on Mathematical Software, Vol.22, No. 4 (Dec. 1996), pp. 469-483, http://www.acm.org/pubs/toc/Abstracts/toms/235821.html 2 Delta Geometric algorithms, languages, and systems General Terms: Algorithms, Reliability Additional Key Words and Phrases: convex hull, Delaunay triagulation, Voronoi diagram, halfspace intersection 1. INTRODUCTION The convex hull of a set of points is the smallest convex set that contains the points. The convex hull is a fundamental construction for mathematics and computational geometry. For example, Boardman uses the convex h...
Image segmentation is an important and fundamental task in many digital image processing systems. Image segmentation by thresholding is the simplest technique and involves the basic assumption that objects and background in the digital image have distinct gray-level distributions. In this paper, we present a general technique for thresholding of digital images based on Rényi’s entropy. Our method includes two of the previously proposed well known global thresholding methods. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.
A hybrid image processing system which automatically distinguishes lean tissues in the image of a complex beef cut surface and generates the lean tissue contour has been developed. Because of the inhomogeneous distribution and fuzzy pattern of fat and lean tissues on the beef cut, conventional image segmentation and contour generation algorithms suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network to enhance the robustness of processing. The system is composed of pre-network, network, and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted to a grid pattern image, whose grid was formed as 4 × 4 pixel size. At the network stage, the normalized gray value of each grid image was taken as the network input. The pre-trained network generated the grid image output of the isolated lean tissue. A sequence of post-network processing was conducted to obtain the detailed contour of the lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human-like robust object segmentation and contour generation for the complex, fuzzy and irregular image.
In evaluating beef quality, proper extraction of the boundary of lean-tissue from the section of beef rib is the crucial first step. The quality features of a beef carcass are determined by size, marbling state, and the colour of the lean-tissue on the 13th rib. Because of the inhomogeneous distribution and fuzzy pattern of the fat and lean-tissues on various beef-cuts, it is difficult to automatically extract a proper contour of the lean-tissue. In this research, image-processing algorithms have been developed to automatically extract the boundary of the lean-tissue in a robust way as human expert does. Algorithms include automatic threshold determination using Rényi entropy for beef-cut segmentation and contour modification, along with binary morphological approaches. The algorithms were applied to 36 beef-cut samples and successfully demonstrated contour extraction with average percentage error of 2.63% and average pixel distance error of 2.51 pixels when compared to results from a human expert. Computing time for the contour extraction process was around 5 s. A mobile image acquisition and processing system was also developed for on-site application. The system was composed of a hand-held image acquisition unit and a mobile computing unit mounted with a touch-pad screen. The algorithms developed were applied to the mobile processing unit as a supplementary grading device.
We describe a method of determining meat quality using the concepts of “marbling score” and texture analysis. The marbling score is a measure of the distribution density of fat in the rib-eye region. In this system, we consider the marbling of meat as a texture pattern. We use a gray level cooccurrence matrix as a texture feature and make standard texture–feature vectors for each grade using it. The grade of an unevaluated image is determined by comparing the texture–feature vector of this unevaluated image with the standard texture–feature vectors. Experimental results show that the system is effective.
We study the implementation of a meat-quality grading system, using the concept of the “marbling score”, as well as image processing, neural network techniques and multiple regression analysis. The marbling score is a measure of the distribution density of fat in the rib-eye region. We identify five features used for grading meat images. For the evaluation of the five features, we propose a method of image binarization using a three-layer neural network developed on the basis of inputs given by a professional grader and a system of meat-quality grading based on the evaluation of two of five features with multiple regression analysis. Experimental results show that the system is effective.