Determination of meat quality by image processing and neural network techniques

2002 ◽  
Author(s):  
K. Shiranita ◽  
K. Hayashi ◽  
A. Otsubo ◽  
T. Miyajima ◽  
R. Takiyama
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Meat Quality

Determination of Meat Quality by Image Processing and Neural Network Techniques

2000 ◽  
Vol 12 (4) ◽  
pp. 474-479
Author(s):  
Kazuhiko Shiranita ◽  
◽  
Kenichiro Hayashi ◽  
Akifumi Otsubo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Meat Quality ◽  
Distribution Density ◽  
Marbling Score ◽  
Quality Grading

We study the implementation of a meat-quality grading system, using the concept of the marbling score, and 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 based on inputs given by a professional grader and a system of meat-quality grading based on the evaluation of three of five features with multiple regression analysis. Experimental results show that the system is effective.

scholarly journals DETERMINING THE OPTIMUM MATURITY OF MAIZE USING GOOGLENET

2021 ◽  
Vol 5 (1) ◽  
pp. 517-523
Author(s):  
A. Peter
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Food Production ◽  
Climatic Changes ◽  
Window Of Opportunity ◽  
Agricultural Produce ◽  
Food Shortages ◽  
Growing Population ◽  
Trained Network

Climatic changes, animal and human activities that lead to desertification and deforestation have affected the increase in agricultural produce especially in sub-Sahara Africa. Several efforts have been put in place to reduce these effects. However, that has not fully resolved the problem food shortages due to the growing population in sub-Sahara Africa. The application of image processing and convolutional neural network in the determination of the optimum maturity of SAMMAZ 17 variety of maize plant is being considered to mitigate for the shortage of food production. The optimum maturity is determined by using GoogleNet pre trained network on 3000 samples of maize comb captured using a camera at different maturity stages in a farmland. GoogleNet pre-trained network gave an accuracy of 82.44%. The result obtained showed a 10.44% improvement over an earlier result using Alexnet pre-trained network. The results suggest that when made operational there is a window of opportunity for increase in the production of food in sub-Sahara Africa

scholarly journals Grading Beef Meat Quality by Image Processing and Neural Network Techniques

1999 ◽  
Vol 11 (6) ◽  
pp. 1128-1134 ◽  
Author(s):  
Kazuhiko SHIRANITA ◽  
Kenichiro HAYASHI ◽  
Akifumi OTSUBO
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Meat Quality ◽  
Beef Meat

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

The quantitative comparison of experimental and simulated diffraction contrast images

1995 ◽  
Vol 53 ◽  
pp. 102-103
Author(s):  
Stuart McKernan
Keyword(s):  
Image Processing ◽  
Personal Computer ◽  
Close Agreement ◽  
Quantitative Comparison ◽  
Image Simulation ◽  
Computing Power ◽  
Diffraction Contrast ◽  
Simulated Images ◽  
Made In

For many years the concept of quantitative diffraction contrast experiments might have consisted of the determination of dislocation Burgers vectors using a g.b = 0 criterion from several different 2-beam images. Since the advent of the personal computer revolution, the available computing power for performing image-processing and image-simulation calculations is enormous and ubiquitous. Several programs now exist to perform simulations of diffraction contrast images using various approximations. The most common approximations are the use of only 2-beams or a single systematic row to calculate the image contrast, or calculating the image using a column approximation. The increasing amount of literature showing comparisons of experimental and simulated images shows that it is possible to obtain very close agreement between the two images; although the choice of parameters used, and the assumptions made, in performing the calculation must be properly dealt with. The simulation of the images of defects in materials has, in many cases, therefore become a tractable problem.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

2020 ◽  
Author(s):  
CSN Koushik ◽  
Shruti Bhargava Choubey ◽  
Abhishek Choubey ◽  
D. Naresh ◽  
N. Bhanu Prakash Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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