An Optimized Computer Vision and Image Processing Algorithm for Unmarked Road Edge Detection

2019 ◽  
pp. 429-437 ◽  
Author(s):  
Jayalakshmi Annamalai ◽  
C. Lakshmikanthan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Processing Algorithm ◽  
Image Processing Algorithm

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

An Image Processing Algorithm to Find New and Old Bruises

1995 ◽  
Vol 11 (5) ◽  
pp. 751-757 ◽  
Author(s):  
J. A. Throop ◽  
D. J. Aneshansley ◽  
B. L. Upchurch
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm

scholarly journals Image Processing Algorithm for Weight Estimation of Dairy Cattle

2011 ◽  
Vol 36 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Kwang-Wook Seo ◽  
Hyeon-Tae Kim ◽  
Dae-Weon Lee ◽  
Yong-Cheol Yoon ◽  
Dong-Yoon Choi
Keyword(s):  
Image Processing ◽  
Dairy Cattle ◽  
Processing Algorithm ◽  
Weight Estimation ◽  
Image Processing Algorithm
Sign in / Sign up

Export Citation Format

Share Document