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Feature Extraction to Polar Image  [PDF]
Donghua Gu, Zhenyu Han, Qinge Wu
Journal of Computer and Communications (JCC) , 2017, DOI: 10.4236/jcc.2017.511002
Abstract: Some algorithms of feature extraction in existing literature studied for image processing was the gray image with one-dimensional parameter. However, some feature points’ extraction for three-dimensional color of polar image, such as the color edge extraction, inflection points, and so on, was urgently to be solved a polar color problem. For achieving quickly and accurately the color feature extraction to polar image, this paper proposed a similar region of color algorithm. The algorithm was compared to polar image, and the effect to color extraction was also described by the combination of the proposed and existing algorithms. Moreover, this paper gave the comparison of the proposed algorithm and an existing classical algorithm to extraction of color feature. These researches in this paper provided a powerful tool for polar image classification, color feature segmentation, precise recognition, and so on.
Virtual Instrument for Modelling and Measuring the Distorting State  [PDF]
Katalin áGOSTON
Scientific Bulletin of the ''Petru Maior" University of T?rgu Mure? , 2012,
Abstract: This paper presents a virtual instrument developed in LabView for modeling generating a distorting voltage by adding harmonics with different level and phase to the base signal. The other virtual instrument models the single-phase power system and calculates the power and energy proper to the harmonics. The virtual instrument can be enlarged very easy for three-phase power system. Developing proper conditional circuits for current and voltage acquisition the virtual instrument can be modify to measure real data. The designed virtual instrument calculates from the acquired data the active and reactive power, the power factor and the frequency and level of the harmonics in case of nonsinusoidal signals.
Feature Extraction in Speechreading  [cached]
Jun He,Hua Zhang
Journal of Software , 2010, DOI: 10.4304/jsw.5.7.705-712
Abstract: To solve the problem of feature extraction in speechreading, several appearance-based feature extraction method are compared and a new improved LDA algorithm is proposed in this paper. In speech or speechreading recognition application, Linear Discriminant Analysis(LDA) usually choose syllable、HMM state or other units as class unit. but the feature dimensionality reduction direction based on this traditional LDA have no direct relations with recognition accuracy,To this problem, A LDA algorithm based on Object (LDAO) which is fit for isolated words recognition in speechreading is proposed, LDAO choose the objects to be recognized as class unit to Linear Discriminant Analysis, which guarantees feature extraction follow the most discriminant directions among objects in theory. Subsequently, training and recognizing method fo
Feature Extraction of Mammograms  [PDF]
Monika Sharma,R. B. Dubey,Sujata,S. K. Gupta
International Journal of Advanced Computer Research , 2012,
Abstract: Breast cancer is the second leading cause of cancer deathsin women today. Early detection of the cancer can reducemortality rate. Studies have shown that radiologists canmiss the detection of a significant proportion ofabnormalities in addition to having high rates of falsepositives. Pattern recognition in image processing requiresthe extraction of features from regions of the image andthe processing of these features with a pattern recognitionalgorithm. We consider the feature extraction part of thisprocessing; with a focus on the problem of microcalcification detection in digital mammography. For everypattern classification problem, the most important stage isfeature extraction. The accuracy of the classificationdepends on the feature extraction stage. We have extractedtextural, statistical and structural features which showpromising results than most of the existing technology.
Feature Extraction and Selection for Image Retrieval
J.P. Ananth,M.A. Leo Vijilous,V. Subbiah Bharathi
International Journal of Soft Computing , 2012,
Abstract: In this study feature extraction process is analyzed and a new set of edge features is proposed. A revised edge-based structural feature extraction approach is introduced. A principle feature selection algorithm is also proposed for new feature analysis and feature selection. The results of the PFA is tested and compared to the original feature set, random selections, as well as those from Principle Component Analysis and multivariate linear discriminant analysis. The experiments showed that the proposed features perform better than wavelet moment for image retrieval in a real world image database and the feature selected by the proposed algorithm yields comparable results to original feature setstudy better results than random sets.
Pradeep N., Girisha H., Sreepathi B. and Karibasappa K.
International Journal of Bioinformatics Research , 2012,
Abstract: Cancer is uncontrolled growth of cells. Breast Cancer is the uncontrolled growth of cells in the breast region. Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Early detection of Breast Cancer can be achieved using Digital Mammography, typically through detection of characteristic masses and/or microcalcifications. A Mammogram is an x-ray of the breast tissue which is designed to identify abnormalities. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Therefore, it would be valuable to develop a computer aided method for mass/tumor classification based on extracted features from the Region Of Interest (ROI) in mammograms. ROI has to be segmented from the digital mammogram using the Segmentation techniques. Pattern recognition in image processing requires the extraction of features from regions of the image, and the processing of these features with a pattern recognition algorithm. We consider the feature extraction part of this processing, with a focus on the problem of tumor detection in digital mammography.Features are nothing but observable patterns in the image which gives some information about the image. For every Pattern Classification problem, the most important stage is Feature Extraction. The accuracy of the classification depends on the Feature Extraction stage. The different features that can be extracted for a digital mammogram are: Texture Features, Statistical Features, Structural Features. In this paper, we are able to calculate Texture, Statistical and Structural Features. We have used MATLAB for extracting the tumors from input mammogram and for calculating various features.
Construction of Feature-Matching Perception in Virtual Assembly
Cheng Cheng,HongAn Wang,GuoZhong Dai,

计算机科学技术学报 , 2003,
Abstract: An important characteristic of virtual assembly is interaction. Traditional direct manipulation in virtual assembly relies on dynamic collision detection, which is very time-consuming and even impossible in desktop virtual assembly environment. Feature-matching is a critical process in harmonious virtual assembly, and is the premise of assembly constraint sensing. This paper puts forward an active object-based feature-matching perception mechanism and a feature-matching interactive computing process, both of which make the direct manipulation in virtual assembly break away from collision detection. They also help to enhance virtual environment understandability of user intention and promote interaction performance. Experimental results show that this perception mechanism can ensure that users achieve real-time direct manipulation in desktop virtual environment.
International Journal of Engineering Science and Technology , 2011,
Abstract: Content Based Image Retrieval is the application of computer vision techniques to the image retrieval problem of searching for digital images in large databases. The method of CBIR discussed in this paper can filter images based their content and would provide a better indexing and return more accurate results. In this paper we wouldbe discussing: Feature vector generation using color averaging technique, Similarity measures and Performance evaluation using randomly selected 5 query images per class out of which result of one class is discussed. Precision –Recall cross over plot is used as the performance evaluation measure to check the algorithm. As thesystem developed is generic, database consists of images from different classes. The effect due to the size of database and number of different classes is seen on the number of relevancy of the retrievals.
Feature Extraction in Radar Target Classification
J. Kurty,F. Nebus,Z. Kus
Radioengineering , 1999,
Abstract: This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT) and Multiple Signal Characterisation (MUSIC), based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.
A Study of Structural Feature Extraction of Handwritten Numerals
Akhilesh Pandey,Fanimani,P. S. Gupta,Kushal Gupta
International Journal of Electronics and Computer Science Engineering , 2012,
Abstract: Feature extraction is an important step of pattern recognition. Proposed system uses profile based feature extraction and uses Simple Profile (with cropping and without cropping image samples), Contour based feature extraction for recognizing handwritten numerals. The features are computed by using 28 X 28 as a feature length classifier used is Linear Discreminant Analysis. The classifier were trained and tested by using the MNIST Handwritten Numeral database. The average recognition rate of proposed system is observed as 87.10 % in case of simple profile without cropping the samples and also we found skeleton based feature extraction reduce the recognition result.
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