Low-cost Machine Vision System Research Articles

A Real-Time Machine Vision System for Grading Quartz Mineral

Abstract Classification is an indispensable process in industrial mass production applications when competitive marketing and increasing the product value by reducing time and saving costs are in concern. In this regard, the machine vision system (MVS) is a prominent technology, especially for automated industrial production flow lines. Recent studies consisted of increasing the accuracy of such systems using advanced technology and complex solutions. Nevertheless, for automated industrial production flow lines, considering only the accuracy rate of an MVS by ignoring the cost and processing speed is not a sufficient parameter to evaluate the success rate concerning the marketing capability. In this study, a relatively low-cost and automated MVS production line for grading white stones in real-time is demonstrated. A conveyor line and a rotary mechanism are integrated into the system for performing a stone grading task. A conditioned cabinet is used for inspecting the flow line continuously. Forty different stone samples in four groups are evaluated in the experimental stage to observe the flow speed and processing accuracy. Different flow speeds of the conveyor line are investigated. The results are demonstrated that the low-cost MVS is successfully operated for grading white stones at relatively high speed with a 92 % accuracy.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

3,4 Abstract: A simple and low cost machine vision system for fault detection and identification is envisaged. The proposed system works on a Beagle Board single board computer. The system can be used to detect cracks or texture differences in classifying objects, especially in food processing industries. It essentially uses a camera for the image capture and process the image in the Beagleboneblack. Currently an online machine vision system on a conveyor line is made up of several cameras that are networked to a single computer. The camera provides the images at high rates and the software on the computer does the processing and makes the decisions to reject the product or not. The goal of this project is to design a smart board level machine vision system using a beagleboneblack with its own image sensors/cameras. The system will have its own algorithms for common machine vision tasks and will be networked to a main system to get output results.

A Robust Machine Vision Algorithm Development for Quality Parameters Extraction of Circular Biscuits and Cookies Digital Images

Biscuits and cookies are one of the major parts of Indian bakery products. The bake level of biscuits and cookies is of significant value to various bakery products as it determines the taste, texture, number of chocolate chips, uniformity in distribution of chocolate chips, and various features related to appearance of products. Six threshold methods (isodata, Otsu, minimum error, moment preserving, Fuzzy, manual method, and k-mean clustering) have been implemented for chocolate chips extraction from captured cookie image. Various other image processing operations such as entropy calculation, area calculation, parameter calculation, baked dough color, solidity, and fraction of top surface area have been implemented for commercial KrackJack biscuits and cookies. Proposed algorithm is able to detect and investigate about various defects such as crack and various spots. A simple and low cost machine vision system with improved version of robust algorithm for quality detection and identification is envisaged. Developed system and robust algorithm have a great application in various biscuit and cookies baking companies. Proposed system is composed of a monochromatic light source, and USB based 10.0 megapixel camera interfaced with ARM-9 processor for image acquisition. MATLAB version 5.2 has been used for development of robust algorithms and testing for various captured frames. Developed methods and procedures were tested on commercial biscuits resulting in the specificity and sensitivity of more than 94% and 82%, respectively. Since developed software package has been tested on commercial biscuits, it can be programmed to inspect other manufactured bakery products.

COMPUTERIZED SPOILED TOMATO DETECTION

In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sortin g decision was based on three features extracted by the different image processing algorithms. This methodology based on the color fea tures, which used for detecting the BER from good t omatoes. Two methods were developed for decision based sorting. The color ima ge threshold method with shape factor was found eff icient for differentiating good and defective tomatoes. The overall accuracy of def ect detection attained was 94 and 96.5% respectivel y. Comparison of the results is also presented in this paper.

Machine Vision for Green Citrus Detection in Tree Images

The overall goal of this study was to develop a low-cost machine vision system for detecting green fruit on citrus trees. It is difficult to distinguish green citrus fruit from the background leaves using color information because the leaves and fruits are similar in color. Therefore, spatial characteristics such as shape and texture were analyzed. After background extraction, edge detection was performed. Then, circle-template matching was applied to the edge image to detect green fruits. The detection tests revealed that 86% of the fruit were correctly detected.

Transputer-based feature extraction and shape analysis

This paper describes computer vision software for a project, the aim of which is to develop a prototype of a fast, low-cost machine vision system for the inspection of surface mount electronic assemblies and solder joints. The system uses advanced edge detection and shape description software to analyse the board, taking data from a simple optical platform. This platform is used as opposed to more complex alternatives such as laser range-finding/profiling, structured light, thermal or X-ray techniques. Hence the sophistication of this system lies in the software and not in the data capture technology. Transputers are used to provide an economical means of attaining the necessary computational power for the image analysis. This leads to a low cost solution that would be attractive to surface mount manufacturers. This paper describes the software modules required to perform feature extraction and shape analysis for inspection and how the modules are parallelized to run on a transputer network.