Knowledge-based view control of a neural 3-D object recognition system

Büker, Ulrich; Hartmann, Georg

doi:10.1109/icpr.1996.547227

Cited by 12 publications

(2 citation statements)

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“…"Principle Component Analysis (PCA) is used to determine the component that is potentially difficult to recognise due to the uncertainties in EOL condition (e.g., rusty)" (Vongbunyong, 2015, pp.46). In their related work [BH96], the authors suggested a knowledge-based approach with a neural network to solve the problem of occlusion in complicated scenes. In short, this research focused on increasing the flexibility of the vision system to deal with uncertainties in EOL condition.…”

Section: Fully-automated Disassemblymentioning

confidence: 99%

An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry

Erenus¹

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Section: Fully-automated Disassemblymentioning

confidence: 99%

An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry

Erenus¹

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“…Moreover, the 2D image of a 3D object depends on factors such as the camera viewpoint and the viewing geometry. A single 2D view-based approach may not be appropriate for 3D object recognition since only one side of an object can be seen from any given viewpoint [6]. One solution to this problem is to use several 2D views of the object.…”

Section: Introductionmentioning

confidence: 99%

3D object recognition using 2D moments and HMLP network

Mashor¹,

Osman²,

Arshad³

Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004.

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This paper proposes a method for recognition and classification of 3D objects using 2D moments and HMLP network. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly used for 2D pattern recognition. However, the current study proves that with some adaptation to multiple views technique, 2D moments are sufficient to model 3D objects. In addition, the simplicity of 2D moment's calculation reduces the processing time for feature extraction, thus decreases the recognition time. The 2D moments were then fed into a neural network for classification of the 3D objects. In the current study, hybrid multi-layered perceptron (HMLP) network is proposed to perform the classification. Two distinct groups of objects that are polyhedral and free-form objects were used to access the performance of the proposed method. The recognition results show that the proposed method has successfully classified the 3D object with the accuracy of up to 100%.

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Image Analysis and Computer Vision: 1996

Rosenfeld

1997

Computer Vision and Image Understanding

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Knowledge-based view control of a neural 3-D object recognition system

Cited by 12 publications

References 13 publications

An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry

An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry

3D object recognition using 2D moments and HMLP network

Image Analysis and Computer Vision: 1996

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