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Hyderabad andhra pradesh, India

Kashappa N.,GND Engineering College | Ramesh Reddy K.,GNITS
Research Journal of Applied Sciences, Engineering and Technology | Year: 2011

This study deals with comparison of 3-level inverter- fed induction motor drive with 9-level inverterfed induction motor drive. A conventional Voltage Source Inverter (VSI) fed induction motor drive is modelled and simulated using matlab simulink and the results are presented. 9-level inverter is also simulated and the corresponding results are presented. The FFT spectrums for the outputs are analyzed to study the reduction in the harmonics. © Maxwell Scientific Organization, 2011. Source


Seetha M.,GNITS | Ravi G.,CBIT
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

Image Classification is the process of assigning classes to the pixels in remote sensed images and important for GIS applications, since the classified image is much easier to incorporate than the original unclassified image. To resolve misclassification in traditional parametric classifier like Maximum Likelihood Classifier, the neural network classifier is implemented using back propagation algorithm. The extra spectral and spatial knowledge acquired from the ancillary information is required to improve the accuracy and remove the spectral confusion. To build knowledge base automatically, this paper explores a non-parametric decision tree classifier to extract knowledge from the spatial data in the form of classification rules. A new method is proposed using a data structure called Peano Count Tree (P-tree) for decision tree classification. The Peano Count Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification than other data mining techniques. The accuracy is assessed using the parameters overall accuracy, User's accuracy and Producer's accuracy for image classification methods of Maximum Likelihood Classification, neural network classification using back propagation, Knowledge Base Classification, Post classification and P-tree Classifier. The results reveal that the knowledge extracted from decision tree classifier and P-tree data structure from proposed approach remove the problem of spectral confusion to a greater extent. It is ascertained that the P-tree classifier surpasses the other classification techniques. © 2010 Copyright SPIE - The International Society for Optical Engineering. Source


Hegde G.P.,SDMIT | Seetha M.,GNITS
Advances in Intelligent Systems and Computing | Year: 2015

Face recognition is one of the widely used research topic in biometric fields and it is rigorously studied. Recognizing faces under varying facial expressions is still a very challenging task because adjoining of real time expression in a person face causes a wide range of difficulties in recognition systems. Moreover facial expression is a way of nonverbal communication. Facial expression will reveal the sensation or passion of a person and also it can be used to reveal someone’s mental views and psychosomatic aspects. Subspace analysis are the most vital techniques which are used to find the basis vectors that optimally cluster the projected data according to their class labels. Subspace is a subset of a larger space, which contains the properties of the larger space. The key contribution of this article is, we have developed and analyzed the 2 state of the art subspace approaches for recognizing faces under varying facial expressions using a common set of train and test images. This evaluation gives us the exact face recognition rates of the 2 systems under varying facial expressions. This exhaustive analysis would be a great asset for researchers working world-wide on face recognition under varying facial expressions. The train and test images are considered from standard public face databases ATT, and JAFFE. © Springer International Publishing Switzerland 2015. Source


Rao D.S.,VNRVJIET | Seetha M.,GNITS | Krishna Prasad M.H.M.,JNTUK
Advanced Science Letters | Year: 2015

Image Classification possess supreme role in great many applications like image analysis, remote sensing, image recognition, medical imaging, image interpretation and pattern recognition etc., In given occurrences, the classification process alone can shape the object of the examination. Original images acquired from satellites are unlike at spiritual and structural intentions. Utilization of image processing approaches can refine the grade of image content. Image fusion is a method to converge input images and to compute fused image with more spectral and spatial information furthermore. Image classification using K-Means on fused images produces better results when contrast to image classification of original images. In this paper fused images obtain from different techniques (Principal Component Analysis (PCA), image fusion using Wavelet Transform, Fuzzy logic and image fusion using Neuro fuzzy logic) are classified and assessed using accuracy assessment parameters for the image classification. LISS III multispectral and panchromatic images have been used in this experiment to show the quality enhancement and improved accuracy assessment of fused image over the original images using MATLAB. Due to potentiality of the fusion approach, fused output images generated from fuzzy, neuro fuzzy, iterative fuzzy and iterative neuro fuzzy logic obtained the better classification accuracy and Kappa coefficient compared to classification of original images and classification of PCA based and wavelet based fused images. © 2015, American Scientific Publishers. All rights reserved. Source


Hegde G.,SDMIT | Seetha M.,GNITS | Seetha M.,Jawaharlal Nehru Technological University | Hegde N.,VCE Inc
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

In this study appearance based facial expression recognition is presented by extracting the Gabor magnitude feature vectors (GMFV) and Gabor Phase Congruency vectors (GPCV). Feature vector space of these two vectors dimensions are reduced and redundant information is removed using subspace methods. Both GMFV and GPCV spaces are projected with Eigen score and projected matching scores are normalized and fused. Final matching score of each subspace method are normalized using Z-score normalization and fused together using maximum rule. Dimension of entire Gabor feature vector space consumes larger area of memory and high processing time with more redundant data. To overcome this problem in this paper entire Gabor matching score level fusion (EGMSLF) approach based on subspace methods is introduced. The JAFFE database is used for experiment. Support vector machine classifier technique is used as classifier. Performance evaluation is carried out by comparing proposed approach with state of art approaches. Proposed EGMSLF approach enhances the performance of earlier methods. © Springer International Publishing Switzerland 2015. Source

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