Fixing genius

Ross, Travis E.

doi:10.7765/9781526132819.00007

Cited by 2 publications

(1 citation statement)

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“…Unlike computers, humans can easily recognize a face, understand spoken words and distinguish different styles of handwriting. Ross (1998) emphasizes the work of Nobel Laureate Herbert Simon whose central finding was that pattern recognition is critical in most human decision-making tasks: The more relevant patterns at your disposal, the better your decisions will be. Automatic (machine) recognition, description, classification and grouping of patterns are important problems in a variety of engineering and scientific disciplines.…”

Section: Introductionmentioning

confidence: 99%

Industrial objects recognition in intelligent manufacturing for computer vision

Jain¹

2021

IJIUS

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PurposeThe overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.Design/methodology/approachComputer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.FindingsOne important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.Originality/valueThis paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

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Section: Introductionmentioning

confidence: 99%