scholarly journals Automated Detection of Liver Histopathological Findings Based on Biopsy Image Processing

Information ◽  
2017 ◽  
Vol 8 (1) ◽  
pp. 36 ◽  
Author(s):  
Maria Tsiplakidou ◽  
Markos Tsipouras ◽  
Nikolaos Giannakeas ◽  
Alexandros Tzallas ◽  
Pinelopi Manousou
Keyword(s):  
Image Processing ◽  
Automated Detection ◽  
Histopathological Findings ◽  
Biopsy Image

scholarly journals Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

2018 ◽  
Vol 7 (3.34) ◽  
pp. 61
Author(s):  
R Srividhya ◽  
K Shanmugapriya ◽  
K Sindhu Priya
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Surface Defects ◽  
Processing Method ◽  
Automated Detection ◽  
Image Construction ◽  
Good Classification ◽  
Industrial Materials ◽  
Manual Calculation ◽  
Visible Defect

In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.  

scholarly journals Quantitative analysis of histopathological findings using image processing software

2017 ◽  
Vol 30 (4) ◽  
pp. 351-358 ◽  
Author(s):  
Yasushi Horai ◽  
Tetsuhiro Kakimoto ◽  
Kana Takemoto ◽  
Masaharu Tanaka
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Histopathological Findings ◽  
Processing Software ◽  
Image Processing Software

scholarly journals Automated detection of vehicles license plate using image processing techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1408
Author(s):  
Farhana Ahmad Poad ◽  
Noor Shuraya Othman ◽  
Roshayati Yahya Atan ◽  
Jusrorizal Fadly Jusoh ◽  
Mumtaz Anwar Hussin
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Region Of Interest ◽  
Automated Detection ◽  
License Plate ◽  
Image Processing Techniques ◽  
Individual Character ◽  
Split And Merge ◽  
Processing Techniques

The aim of this project is to design an Automated Detection of License Plate (ADLP) system based on image processing techniques. There are two techniques that are commonly used in detecting the target, which are the Optical Character Recognition (OCR) and the split and merge segmentation. Basically, the OCR technique performs the operation using individual character of the license plate with alphanumeri characteristic. While, the split and merge segmentation technique split the image of captured plate into a region of interest. These two techniques are utilized and implemented using MATLAB software and the performance of detection is tested on the image and a comparison is done between both techniques. The results show that both techniques can perform well for license plate with some error.

scholarly journals Automated detection and identification of white-backed planthoppers in paddy fields using image processing

2017 ◽  
Vol 16 (7) ◽  
pp. 1547-1557 ◽  
Author(s):  
Qing YAO ◽  
Guo-te CHEN ◽  
Zheng WANG ◽  
Chao ZHANG ◽  
Bao-jun YANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Automated Detection ◽  
Paddy Fields ◽  
Detection And Identification

scholarly journals Design and Development of Street Crack Detection

2018 ◽  
Vol 7 (3.8) ◽  
pp. 82 ◽  
Author(s):  
Mr Swapnil Vilas Patil ◽  
Prof. Mangesh M. Ghonge ◽  
. .
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Ground Truth ◽  
Automated Detection ◽  
Driving Safety ◽  
Detection Accuracy ◽  
False Detection ◽  
Safety Assurance ◽  
Ground Truth Image ◽  
Better Than

Automated detection of street cracks is a crucial project. In transportation preservation for driving safety assurance and detection a crack manually is an exceptionally tangled and time excessive method. So with the advance of science and generation, automated structures with intelligence have been accustomed examine cracks instead of people. For crack detection and characterization image processing is used widely. But because of the inhomogeneity along the cracks, the inference of noise with the same texture and complexity of cracks, image processing remain challenging. In this paper, we focused on the system performance and the additional features. System which has crack detection accuracy issue, false detection of crack issue, efficiency issue are solved in this system. For better accuracy in detecting crack and increasing the performance of the system we used the random forest algorithm. This system help to detect and characterized the crack and it find out crack from noise also i.e. it neglect the noise better than existing system. Similarly, proposed method find out the length of the crack width and depth of the crack from image with the help of ground truth image.   

Sign in / Sign up

Export Citation Format

Share Document