Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

Analysis and classification of myocardial infarction tissue from echocardiography images based on texture analysis

Texture analysis is an important characteristic for automatic visual inspection for surface and object identification from medical images and other type of images. This paper presents an application of wavelet extension and Gray level cooccurrence matrix (GLCM) for diagnosis of myocardial infarction tissue from echocardiography images. Many of applications approach have provided good result in different fields of application, but could not implemented at all when texture samples are small dimensions caused by low quality of images. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposition images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not

Using the beat histogram for speech rhythm description and language identification

In this paper we present a novel approach for the description of speech rhythm and the extraction of rhythm-related features for automatic language identification (LID). Previous methods have extracted speech rhythm through the calculation of features based on salient elements of speech such as consonants, vowels and syllables. We present how an automatic rhythm extraction method borrowed from music information retrieval, the beat histogram, can be adapted for the analysis of speech rhythm by defining the most relevant novelty functions in the speech signal and extracting features describing their periodicities. We have evaluated those features in a rhythm-based LID task for two multilingual speech corpora using support vector machines, including feature selection methods to identify the most informative descriptors. Results suggest that the method is successful in describing speech rhythm and provides LID classification accuracy comparable to or better than that of other approaches, without the need for a preceding segmentation or annotation of the speech signal. Concerning rhythm typology, the rhythm class hypothesis in its original form seems to be only partly confirmed by our results

A systematic algorithm development for image processing feature extraction in automatic visual inspection : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in the Department of Production Technology, Massey University

Image processing techniques applied to modern quality control are described together with the development of feature extraction algorithms for automatic visual inspection. A real-time image processing hardware system already available in the Department of Production Technology is described and has been tested systematically for establishing an optimal threshold function. This systematic testing has been concerned with edge strength and system noise information. With the a priori information of system signal and noise, non-linear threshold functions have been established for real time edge detection. The performance of adaptive thresholding is described and the usefulness of this nonlinear approach is demonstrated from results using machined test samples. Examination and comparisons of thresholding techniques applied to several edge detection operators are presented. It is concluded that, the Roberts' operator with a non-linear thresholding function has the advantages of being simple, fast, accurate and cost effective in automatic visual inspection

3D modelling and recognition

3D face recognition is an open field. In this paper we present a method for 3D facial recognition based on Principal Components Analysis. The method uses a relatively large number of facial measurements and ratios and yields reliable recognition. We also highlight our approach to sensor development for fast 3D model acquisition and automatic facial feature extraction

Content-based video classification and compariSon

Automatic video analysis tools have dramatically increased in importance as the Internet video revolution has blossomed. This thesis presents an approach for automatic comparison of videos based on the inherent content. Also, an approach for creating groups (or clusters) of similar videos from a large video database is given; First, methods simplifying and summarizing the content of videos will be presented. Such methods include shot boundary detection and key frame feature extraction; Next, a comparison of different distance measures between videos will be given. These distance measures will be used to construct video clusters, and results will be compared

A CAD System for the Detection of Clustered Microcalcification in Digitized Mammogram Film

Cluster of microcalcification in mammograms are an important early sign of breast cancer. This report presents a computer aided diagnosis (CAD) system for the automatic detection of cluster rnicrocalcifications in digitized mammograms. The main objective of this study is to present the approach for microcalcifications detection in mammography image. In literature review author illustrate the techniques used in image processing, segmentation, feature extraction and neural network in detecting rnicrocalcification. The proposed system consists of two main steps. First step is image preprocessing and segmentation in order to improve and enhance the quality of image. Then second step is feature extraction to analyze the image and conclude whether the case is malignant or benign. The programming of the project using MATLAB still need to be improved since it produce the output that did not meet the author expectation especially in feature extraction

Automatic feature extraction from conventional CAD model to support feature-based design approach for the sheet metal stamping industries

Despite the continuing improvement in computer aided design (CAD) systems and improvements in computer aided manufacturing (CAM), the process planning activity has still not been completely integrated into the CAD/CAM cycle. Particularly in sheet metal stamping industries human interpretation of CAD data is required to extract the geometry and technological information of a component. As a result most CAD systems are used as advanced drafting and drawing management tools by designers. Thus the responsibility for interpreting the design data required for extracting the manufacturing part features still resides with the process planner. Which has increase possibilities of entering errors with design data. A need, therefore, exists to develop expert system for automatic features extraction from a CAD database. An application software was developed for automatic feature extraction from conventional CAD model database to impliment feature-based design approach for the sheet metal stamping industries