Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks

Food Control ◽  
2019 ◽  
Vol 106 ◽  
pp. 106693 ◽  
Author(s):  
I. Muñoz ◽  
P. Gou ◽  
E. Fulladosa
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Computer Image ◽  
Intramuscular Fat ◽  
Computer Image Analysis ◽  
Dry Cured Ham

scholarly journals Artificial neural networks and computer image analysis in the evaluation of selected quality parameters of pea seeds

2019 ◽  
Vol 132 ◽  
pp. 01027 ◽  
Author(s):  
Katarzyna Szwedziak
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Good Method ◽  
Quality Parameters ◽  
Computer Image Analysis ◽  
Pea Seeds ◽  
Artificial Neural

The aim of the study was to develop an innovative method of modelling the process of evaluating the quality of agricultural crops on the basis of computer image analysis and artificial neural networks (ANN). It was therefore assumed that on the basis of the prepared application for processing and analysing the acquired digital images, based on the RGB colour recognition model, a quick and good method of assessing the quality of products would be obtained. An experiment was conducted on the evaluation of selected parameters of pea seeds quality using computer image analysis and the obtained results were verified by artificial neural networks using the geostatic function.

Computer image analysis as a tool for classifying marbling: A case study in dry-cured ham

2015 ◽  
Vol 166 ◽  
pp. 148-155 ◽  
Author(s):  
Israel Muñoz ◽  
Marc Rubio-Celorio ◽  
Núria Garcia-Gil ◽  
Maria Dolors Guàrdia ◽  
Elena Fulladosa
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Computer Image Analysis ◽  
Dry Cured Ham

scholarly journals Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks

2020 ◽  
Vol 10 (16) ◽  
pp. 5721
Author(s):  
Katarzyna Szwedziak ◽  
Żaneta Grzywacz ◽  
Ewa Polańczyk ◽  
Piotr Bębenek ◽  
Marian Olejnik
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Investment Project ◽  
Evaluation Process ◽  
Computer Application ◽  
Stored Grain ◽  
Computer Image Analysis ◽  
Artificial Neural

The paper presents the method of using vision techniques and artificial neural networks to assess the degree of contamination of cereal during grain reception. The aim of the work is to optimize the management of the contaminant evaluation process of grain mass in warehouse and during purchase using vision techniques based on computer image analysis in order to expedite laboratory work. The obtained photographs of wheat seed samples were analyzed using the “Agropol V06” computer application and neural analysis of the obtained empirical results was performed. The application of computer image analysis reduced the time necessary for the quality assessment of the examined material compared to traditional methods. The generated models were characterized by good parameters and high quality, obtaining a high R2 coefficient at the level of 0.999. As part of the investment project, savings resulting from the time of goods receipt and further production process were made. Profitability was estimated at 191.43% per day. The analysis was made without taking into account other costs related to the business activity. The straight payback period is 3 years.

Recent developments in automated Transmission Electron MI

1989 ◽  
Vol 47 ◽  
pp. 46-47
Author(s):  
W.J. de Ruijter ◽  
P. Rez ◽  
David J. Smith
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Lens Design ◽  
Computer Image Analysis ◽  
Transmission Electron ◽  
Resolution Electron ◽  
Recent Developments ◽  
On Line ◽  
Routine Procedures ◽  
Near Future

There is growing interest in the on-line use of computers in high-resolution electron n which should reduce the demands on highly skilled operators and thereby extend the r of the technique. An on-line computer could obviously perform routine procedures hand, or else facilitate automation of various restoration, reconstruction and enhan These techniques are slow and cumbersome at present because of the need for cai micrographs and off-line processing. In low resolution microscopy (most biologic; primary incentive for automation and computer image analysis is to create a instrument, with standard programmed procedures. In HREM (materials researc computer image analysis should lead to better utilization of the microscope. Instru (improved lens design and higher accelerating voltages) have improved the interpretab the level of atomic dimensions (approximately 1.6 Å) and instrumental resolutior should become feasible in the near future.

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