scholarly journals Research Review of Feature Extraction and Classification Recognition of Rice Disease Images based on Computer Vision Technology

2020 ◽  
Vol 1544 ◽  
pp. 012116
Author(s):  
Hui Li
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Research Review ◽  
Rice Disease ◽  
Computer Vision Technology ◽  
Classification Recognition

scholarly journals Local feature extraction of sheepskin based on structure contour shape description

2021 ◽  
Vol 336 ◽  
pp. 06026
Author(s):  
Lianhua Hu ◽  
Chengyi Xiang ◽  
Feng Zhang
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Experimental Simulation ◽  
Shape Description ◽  
Contour Shape ◽  
Edge Removal ◽  
Starting Point ◽  
Computer Vision Technology ◽  
Local Feature Extraction ◽  
Implementation Steps

Based on the precise sheepskin contour extracted by computer vision technology in the previous research of the team, this paper proposes the shape description technology based on the structure contour to extract the local features of the sheepskin, such as the head and hooves and the waste edge, which is the basis for the automatic edge removal of the sheepskin in the future. The algorithm uses Angle and position relation to segment the precise contour track of raw sheepskin into graph elements, and then uses geometric parameter shape description operator to describe and extract the edges that need to be removed, so as to obtain the starting point and end point of each local contour that needs to be removed. In this paper, the principle and implementation steps of this method are introduced in detail, and the experimental simulation verification shows that the extraction effect is good, which can meet the requirements of subsequent industrial production of automatic sheepskin cutting.

scholarly journals Computer Vision Used to Monitor The Youth during The Pandemic Covid-19

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Salsyabila Vidia Nur Afni ◽  
Esi Putri Silmina ◽  
Irwanda Budi Pangestu
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Literature Review ◽  
Image Enhancement ◽  
Image Capture ◽  
The Public ◽  
Symbolic Information ◽  
Computer Vision Technology ◽  
The Government ◽  
The City

Computer Vision is a field that studies methods for capturing numerical or symbolic information. Some of the computer vision processes are image capture, image enhancement, segmentation, feature extraction, and clarification. AI-based Computer Vision technology allows the public to carry out more optimal surveillance to deal with Covid-19. During the Covid-19 pandemic, many people have adopted new normal habits by implementing health protocols. But not a few of our people do not understand the importance of implementing health protocols. Where after this year's Eid homecoming, not a few of our people have made trips to their hometown villages. In this case, there is already a prohibition from the Government not to make the Eid homecoming trip. These communities can be at risk of transmitting the Covid-19 virus to their families in their hometowns. This study describes how Computer Vision works in helping the community to monitor travelers from the city to minimize the spread of Covid-19. The paper wa presented using the literature review method. From the description result using the literature review, it has result that computer vision technology has enormous potential in spreading countermeasures Covid-19.

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

scholarly journals A Review on Computer Vision Technology for Monitoring Poultry Farm – Application, Hardware, and Software

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Nur Syazarin Natasha Abd Aziz ◽  
Salwani Mohd Daud ◽  
Rudzidatul Akmam Dziyauddin ◽  
Mohamad Zulkefli Adam ◽  
Azizul Azizan
Keyword(s):  
Computer Vision ◽  
Poultry Farm ◽  
Computer Vision Technology

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

Sign in / Sign up

Export Citation Format

Share Document