Organoleptic damage classification of potatoes with the use of image analysis in production process

2014 ◽  
Author(s):  
K. Przybyl ◽  
M. Zaborowicz ◽  
K. Koszela ◽  
P. Boniecki ◽  
W. Mueller ◽  
...  
Keyword(s):  
Image Analysis ◽  
Production Process ◽  
Damage Classification

Image analysis and peanut seeds performance during the production process

Científica ◽  
2016 ◽  
Vol 44 (3) ◽  
pp. 412 ◽  
Author(s):  
Rafael Marani Barbosa ◽  
Bruno Guilherme Torres Licursi Vieira ◽  
Francisco Guilhien Gomes-Junior ◽  
Roberval Daiton Vieira
Keyword(s):  
Image Analysis ◽  
Production Process

Colour image analysis for the classification of turkey breast meat according to cooking yield

2003 ◽  
Vol 23 (1) ◽  
pp. 124-127
Author(s):  
Isabel Sebastáan ◽  
V Santé ◽  
G Le Pottier ◽  
Pascale Marty-Mahé ◽  
P Loisel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Breast Meat ◽  
Colour Image ◽  
Cooking Yield

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Acoustic emission for diagnosing cable way steel support towers

2019 ◽  
Vol 284 ◽  
pp. 09002
Author(s):  
Aleksandra Krampikowska ◽  
Grzegorz Świt
Keyword(s):  
Acoustic Emission ◽  
Image Analysis ◽  
Structural Damage ◽  
Corrosion Damage ◽  
Classification Method ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Damage Classification ◽  
Mathematical Processing ◽  
Destructive Processes

The paper reports results of the study on the possibility of using the acoustic emission method in diagnosing fatigue and corrosion damage in steel elements of the cable way support towers. The assessment of the sensitivity of the structure to the recorded destructive processes is based on the structural damage classification method using the patterns created as a result of statistical and mathematical processing of acoustic emission signals through image analysis and grouping methods.

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