http://machinevision.iem.yzu.edu.tw/english_version/tech/Pattern1-wavelet.pdf

Rotation-invariant pattern matching using wavelet decomposition

Wavelet neural networks combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. In this paper, an efficient object recognition method using boundary representation and the wavelet neural network is proposed. The method employs a wavelet neural network (WNN) to characterize the singularities of the object curvature representation and to perform the object classification at the same time and in an automatic way. The local time–frequency attributes of the singularities on the object boundary are detected by making a preliminary wavelet analysis of the curvature representation. Then, the discriminative scale–translation features of the singularities are stored as the initial scale–translation parameters of the wavelet nodes in the WNN. These parameters are trained to their optimum status during the learning stage. With our approach, as opposed to matching features by convolving the signal with wavelet functions at a large number of scales, the computational burden is significantly reduced. Only a few convolutions are performed at the optimum scale–translation grids during the classification, which makes it suitable for real-time recognition tasks. Compared with the artificial-neural-network-based approaches preceded by wavelet filter banks with fixed scale–translation parameters, the support vector machine (SVM) using traditional Fourier descriptors and $K$ -nearest-neighbor ( $K$ -NN) classifier based on the state-of-the-art shape descriptors, our scheme demonstrates superior and stable discrimination performance under various noisy and affine conditions.

Efficient Object Recognition Using Boundary Representation and Wavelet Neural Network

Image Analysis and Computer Vision: 1998☆

A 2D object recognition algorithm applicable for partial occluded object recognition is proposed. The boundary of object of interest is extracted first. Then we segment the boundary into curve segments using dominant points, followed by a proportional extension. Normalization is then performed for each segment to make them translation, orientation and scaling invariant. After that, each segment is represented by its wavelet descriptors at multi-scale. A hierarchical iterative matching is performed to identify the object from low to high resolution. Experiment result shows proposed recognition algorithm is robust to similarity transform, noise and occlusion, and it is computational efficient.

2D occluded object recognition using wavelets

Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years. Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence, information security, biometrics, military science and technology, finance and economics, weather forecast, image processing, communication, biomedical engineering, document processing, robot vision, transportation, and endless other areas, with many encouraging results. The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis. This paper aims at a brief survey of pattern recognition with the wavelet theory. It contains the following respects: analysis and detection of singularities with wavelets; wavelet descriptors for shapes of the objects; invariant representation of patterns; handwritten and printed character recognition; texture analysis and classification; image indexing and retrieval; classification and clustering; document analysis with wavelets; iris pattern recognition; face recognition using wavelet transform; hand gestures classification; character processing with B-spline wavelet transform; wavelet-based image fusion, and others.

Status of pattern recognition with wavelet analysis

An optimum solution for scale-invariant object recognition based on the multiresolution approximation

Objective: A purpose of this study was developing the Decision-Support Patient affairs management and building of Data-mart for individually specified department Because it had a OLTP system where we were researching the hospital It can not be analyzied variously because the data for the management, accounts, patient affairs and medical record were changing monthly/annually in conditions also it can not have the specificity of the hospital management information Methods: The Data Set that used for analysis process was examined with the patient's Database for a year They were in hospital where locates in Sun Cheon For developing patient affairs management program, The Microsoft Visual Basic 60 was used and A analysis method of ROLAP which is based on Oracle relation Database was used for general analysis of required works Results: The patient affairs Data-mart and Decision-Support System was built with the main results of the study Those took advantage of the analysis for the each department's revenue and doctor's actual results, it also can compare the patient's hospital using frequency With the analyzied results, each department can have their own special marketing They can offer to the resistants a lecture, consultant and free service for the health Conclusion: The management, the accounts, patient affairs and the medical record can have been analyzied various changing through the study The host System had enormous volume, so it takes lots of times what they want to inquire The host system usually took at night because of low transaction occurrence The new system which was built with Data-mart had been improved the problem a lot

Seong Mi Park

Papers

An optimum solution for scale-invariant object recognition based on the multiresolution approximation

Development of Decision-Support System for Patient Affairs Management and Building of Data Mart