Recognition and inspection of manufactured parts using line moments of their boundaries

Wang, Wen; Lozzi, Andrei

doi:10.1016/0031-3203(93)90152-m

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…For the processing of this kind of data Lambert and Gao [33] designed the Line Moments. Better results were achieved in identification problems when Line Moments were used to describe contours represented by lines, in comparison with other moment types [33,34].…”

Section: Data Reduction: Normalized Central Line Momentsmentioning

confidence: 99%

“…Since then, Geometrical Moments have been used in pattern recognition, face recognition, ship and aircraft identification and many image analysis applications. In the specific case of Line Moments, the applications have focused on the identification of boundaries, shape description and signal recognition like the inspections of parts [34].…”

Section: Data Reduction: Normalized Central Line Momentsmentioning

confidence: 99%

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Dynamic characterization of Roma Plastilina No. 1 from Drop Test and inverse analysis

Hernández

Buchely

Maranon

2015

International Journal of Mechanical Sciences

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Section: Data Reduction: Normalized Central Line Momentsmentioning

confidence: 99%

Section: Data Reduction: Normalized Central Line Momentsmentioning

confidence: 99%

Dynamic characterization of Roma Plastilina No. 1 from Drop Test and inverse analysis

Hernández

Buchely

Maranon

2015

International Journal of Mechanical Sciences

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“…In order to distinguish two different quadratic curves having the same three invariants, we introduce other three invariants: the central moment with order (1+1) [14] and two angles as follows:…”

Section: For D)mentioning

confidence: 99%

Gesture Recognition Using Quadratic Curves

Dong

2006

Computer Vision – ACCV 2006

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Abstract. This paper presents a novel method for human gesture recognition based on quadratic curves. Firstly, face and hands in the images are extracted by skin color and their central points are kept tracked by a modified Greedy Exchange algorithm. Then in each trajectory, the central points are fitted into a quadratic curve and 6 invariants from this quadratic curve are computed. Following these computations, a gesture feature vector composed of 6n such invariants is constructed, where n is the number of the trajectories in this gesture. Lastly, the gesture models are learnt from the feature vectors of gesture samples and an input gesture is recognized by comparing its feature vector with those of gesture models. In this gesture recognition method, the computational cost is low because the gesture duration does not need to be considered and only simple curvilinear integral and matrix computation are involved. Experiments on hip-hop dance show that our method can achieve a recognition rate as high as 97.65% on a database of 16 different gestures, each performed by 8 different people for 8 different times.

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“…RBF neural network is a kind of multilayer forward neural network which has powerful ability of pattern classification. [3,4] But the generalization ability of the normal RBF network cannot meet the requirement. [5,6] In order to improve the generalization ability and discriminability, we can adopt the neural network technology with fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

An intelligent diagnosing method to compressors' faults based on fuzzy RBFNN

et al. 2008

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In order to diagnose the compressors' fault on line, an intelligent checking method is presented in the paper. A vibration sensor was put on the compressors that should be detected. The vibration signals obtained by the sensor contain a great deal information, which reflects the compressors' qualities and their type of faults. It has been proved that, the vibration signals obtained from compressors with different faults have different time domain features and frequency domain features. We extract those features, and then get a feature vector which is sent to an intelligent information processor. In order to improve the generalization and robustness of the processor, we adopt a fuzzy clustering radial basis function (RBF) neural networks as the information processor. A method of fuzzy C-means clustering based on minimized mean square error rule is used to determine the RBF layer, and the shape factors of RBF neurons are determined by the grades of membership. The experimental results show that, fuzzy clustering RBF neural networks neural networks have powerful ability of pattern recognition, and the faults diagnosis method is feasible to diagnose the fault of the revolving machinery.

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Recognition and inspection of manufactured parts using line moments of their boundaries

Cited by 18 publications

References 14 publications

Dynamic characterization of Roma Plastilina No. 1 from Drop Test and inverse analysis

Dynamic characterization of Roma Plastilina No. 1 from Drop Test and inverse analysis

Gesture Recognition Using Quadratic Curves

An intelligent diagnosing method to compressors' faults based on fuzzy RBFNN

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