scholarly journals AMR Vision System for Perception, Job Detection and Identification in Manufacturing

10.5772/5004 ◽  
2007 ◽  
Author(s):  
Sarbari Datta ◽  
Ranjit Ray
Keyword(s):  
Vision System ◽  
Detection And Identification

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

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Satyam Srivastava ◽  
Sachin Boyat ◽  
Shashikant Sadistap
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Monochromatic Light ◽  
Quality Parameters ◽  
Bakery Products ◽  
Robust Algorithms ◽  
Robust Algorithm ◽  
Detection And Identification ◽  
Specificity And Sensitivity

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.

ROBUST HUMAN DETECTION AND IDENTIFICATION BY USING STEREO AND THERMAL IMAGES IN HUMAN ROBOT INTERACTION

2007 ◽  
Vol 04 (02) ◽  
pp. 161-183 ◽  
Author(s):  
F. GUAN ◽  
L. Y. LI ◽  
S. S. GE ◽  
A. P. LOH
Keyword(s):  
Vision System ◽  
Effective Interaction ◽  
Human Robot Interaction ◽  
Human Detection ◽  
Human Beings ◽  
Robot Interaction ◽  
Thermal Images ◽  
Detection And Identification ◽  
Infrared Thermal Images ◽  
Socially Interactive Robots

In this paper, robust human detection is investigated by fusing the stereo and infrared thermal images for effective interaction between humans and socially interactive robots. A scale-adaptive filter is first designed for the stereo vision system to detect human candidates. To eliminate the difficulty of the vision system in distinguishing human beings from human-like objects, the infrared thermal image is used to solve the ambiguity and reduce the illumination effect. Experimental results show that the fusion of these two types of images gives an improved vision system for robust human detection and identification, which is the most important and essential component of human robot interaction.

Nickel Foam Surface Defect Identification Based on Improved Probability Extreme Learning Machine

2020 ◽  
Vol 13 (4) ◽  
pp. 604-610
Author(s):  
Binfang Cao ◽  
Jianqi Li ◽  
Fangyan Nie
Keyword(s):  
Extreme Learning Machine ◽  
Surface Defect ◽  
Surface Defects ◽  
Vision System ◽  
Nickel Foam ◽  
Differential Evolution Algorithm ◽  
Detection And Identification ◽  
Surface Defect Detection ◽  
Low Efficiency ◽  
Learning Machine

Background: In the nickel foam production process, the detection and identification of surface defects relies heavily upon the operators’ experiences. However, the manual observation is of high labor intensity, low efficiency, strong subjectivity and high error rate. Objective: Therefore, this paper proposes a new method for the nickel foam surface defect detection and identification, based on an improved probability extreme learning machine. Methods: At first, a machine vision system for nickel foam is established, and gray level cooccurrence matrix is used to calculate defect features, which are inputted into extreme learning machine to train the defect classifier. Then a composite differential evolution algorithm is used to optimize the input weights and hidden layer thresholds. Finally, an integrated probabilistic ELM is proposed to avoid misjudgments when multiple probabilities values are almost identical. Conclusion: Experiments show that the proposed method can achieve a defect-identifying accuracy, which meets an enterprise’s needs.

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

115: Detection and Identification of Bacterial DNA and HIV Gag in the Semen of HIV Infected Men

2004 ◽  
Vol 171 (4S) ◽  
pp. 30-30
Author(s):  
Robert C. Eyre ◽  
Ann A. Kiessling ◽  
Thomas E. Mullen ◽  
Rachel L. Kiessling
Keyword(s):  
Bacterial Dna ◽  
Detection And Identification
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