A Computer Vision-Based Shape-Classification System UsingImage Projection and a Neural Network

Hou, Tung-Hsu; Pern, Ming-Der

doi:10.1007/s001700050141

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Among the different NN structures, the best known to provide best results in multiclass patern classification is the multilayer perceptron or backpropagation neural network [8,9,10]. …”

Section: Neural Network For Pattern Recognition and Error Detectionmentioning

confidence: 99%

3D machine vision and artificial neural networks for quality inspection in mass production pieces

Tellaeche

Robles

2010

2010 IEEE 15th Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2010)

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The exhaustive quality control is becoming very important in the world´s globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, a neural network has been tested to provide a feasible classification of the possible errors present in the percussion caps.

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“…Among the different NN structures, the best known to provide best results in multiclass patern classification is the multilayer perceptron or backpropagation neural network [8,9,10]. …”

Section: Neural Network For Pattern Recognition and Error Detectionmentioning

confidence: 99%

3D machine vision and artificial neural networks for quality inspection in mass production pieces

Tellaeche

Robles

2010

2010 IEEE 15th Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2010)

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“…The first part represents the geometry of the object, e.g., width and height. The second part is based on the notion of projection [25], [27]. 2) Define a distance function to compare the similarity between pairs of elements.…”

Section: A Image Processingmentioning

confidence: 99%

Web Interface Interpretation Using Graph Grammars

Kong

Barkol²,

Bergman³

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Abstract-With the advent of the Internet, it is desirable to interpret and extract useful information from the Web. One major challenge in Web interface interpretation is to discover the semantic structure underlying a Web interface. Many heuristic approaches have been developed to discover and group semantically related interface objects. However, those approaches cannot solve the problem of nonuniformity satisfactorily and are not able to tag the semantic role of each object. Distinct from existing approaches, this paper develops a robust and formal approach to recovering interface semantics using graph grammars. Because of the distinct capability of spatial specifications in the abstract syntax, the spatial graph grammar (SGG) is selected to perform the semantic grouping and interpretation of segmented screen objects. Instead of analyzing HTML source codes, we apply an efficient imageprocessing technology to recognize atomic interface objects from the screenshot of an interface and produce a spatial graph, which records significant spatial relations among recognized objects. A spatial graph is more concise than its corresponding document object model structure and, thus, facilitates interface analysis and interpretation. Based on the spatial graph, the SGG parser recovers the hierarchical relations among interface objects.

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“…2D texture analysis presents a simpler way to evaluate the bone structure This work was supported by the National University of Sciences and Technology, Islamabad, Pakistan and Manchester Metropolitan University, Manchester, UK using radiographs [9]. The 2D texture analysis is an indirect assessment of 3D micro-architecture [10]. This assists in extracting the information about the bone structure and helps in separating the healthy from the osteoporotic cases.…”

Section: Introductionmentioning

confidence: 99%

Osteoporosis Classification Using Texture Features

Riaz¹,

Nemati²,

Ajmal³

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Assessment of osteoporotic disease from the radiograph image is a significant challenge. Texture characteristics when observed from the naked eye for the bone microarchitecture of the osteoporotic and healthy cases are visually very similar making it a challenging classification problem. To extract the discriminative patterns in all the orientations and scales simultaneously in this study we have proposed an approach that is based on a combination of multi resolution Gabor filters and 1D local binary pattern (1DLBP) features. Gabor filter are used due to their advantages in yielding a scale and orientation sensitive analysis whereas LBPs are useful for quantifying microstructural changes in the images. Our experiment show that the proposed method shows good classification results with an overall accuracy of about 72.71% and outperforms the other methods that have been considered in this paper.

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A Computer Vision-Based Shape-Classification System UsingImage Projection and a Neural Network

Cited by 10 publications

References 13 publications

3D machine vision and artificial neural networks for quality inspection in mass production pieces

3D machine vision and artificial neural networks for quality inspection in mass production pieces

Web Interface Interpretation Using Graph Grammars

Osteoporosis Classification Using Texture Features

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