THE DESIGN AND IMPLEMENTATION OF A COMPUTER-VISION BASED INTELLIGENT SYSTEM FOR THE REFRACTION INDEX IDENTIFICATION OF GEMSTONES

2018 ◽  
Author(s):  
Ying Song ◽  
Keyword(s):  
Computer Vision ◽  
Intelligent System ◽  
Refraction Index ◽  
Design And Implementation

A Framework for Development the Open Systems Interconnection of Integrated Intelligent Knowledge for Management of Networks

2013 ◽  
Vol 411-414 ◽  
pp. 795-798 ◽  
Author(s):  
Yu Qing Shi ◽  
Yue Long Zhu
Keyword(s):  
Knowledge Management ◽  
Intelligent System ◽  
Open Systems ◽  
Management Model ◽  
New Model ◽  
Design And Implementation ◽  
Open Systems Interconnection ◽  
Intelligent Management

In this article, we present a new model for distributed intelligent management networks. This paper presents a approach for the design and implementation of a distributed intelligent system that is designed through the normalization of knowledge management. Our study focuses on a language for formalizing knowledge management descriptions and an intelligent framework and combining them with an existing Open Systems Interconnection (OSI) management model. Further, this work outlines the development of an example based on our proposed standard.

Image Capture Board for The PC

1995 ◽  
Vol 32 (3) ◽  
pp. 235-255
Author(s):  
T. David Binnie ◽  
I. Reading
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Personal Computer ◽  
Low Cost ◽  
Random Access ◽  
Image Data ◽  
Image Capture ◽  
Design And Implementation

Image capture board for the PC We report the design and implementation of a low cost, image capture board for an IBM type personal computer. The board is particularly suited to computer vision education. The board provides: image capture at video rate, random access to xy addressable image data, and options for on-board image processing hardware.

scholarly journals Application of colour, shape, and texture parameters for classifying the defect of Gayo Arabica green coffee bean using computer vision

2022 ◽  
Vol 951 (1) ◽  
pp. 012097
Author(s):  
A Maghfirah ◽  
I S Nasution
Keyword(s):  
Computer Vision ◽  
Intelligent System ◽  
Training Data ◽  
National Standard ◽  
Support Vector ◽  
Coffee Bean ◽  
Green Coffee ◽  
Coffee Beans ◽  
Green Coffee Bean

Abstract Coffee is the most important commodity in the trading industry. Determination of the quality of coffee is still done manually so that it cannot separate good quality coffee beans with bad quality coffee beans. This research conducted the development of a visual-based intelligent system using computer vision to be able to classify the quality of rice coffee based on the Indonesian National Standard (SNI). The models used in the study are the K-Nearest Neighbour (K-NN) method and the Support Vector Machine (SVM) method with 13 parameters used such as; area, contrast, energy, correlation, homogeneity, circularity, perimeter, and colour index R(red), G (green), B (blue), L*, a* and b*. A total of 1200 Arabica green coffee bean captured using Kinect V2 camera with training data of 1000 samples and testing data of 200 samples.

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