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Sri Krishna, A.
- An Iterative Morphological Decomposition Algorithm for Reduction of Skeleton Points
Abstract Views :222 |
PDF Views:112
Authors
Affiliations
1 Dept. of IT, R.V.R & J.C College of Eng. Guntur, IN
2 Dept. of CSE, AMRITA School of Eng., Coimbatore, IN
3 Dept. of IT, R.V.R & J.C College of Eng., Guntur, IN
1 Dept. of IT, R.V.R & J.C College of Eng. Guntur, IN
2 Dept. of CSE, AMRITA School of Eng., Coimbatore, IN
3 Dept. of IT, R.V.R & J.C College of Eng., Guntur, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 7, No 1 (2015), Pagination: 93-104Abstract
Shape representation is an important aspect in image processing and computer vision. There are several skeleton transforms that lead to morphological shape representation algorithm. One of the main problems with these algorithms is in selecting the skeleton points that represent the shape component. If the numbers of skeleton subsets are reduced then the reconstruction process will be easy and time consuming. The present paper proposes a skeleton scheme that selects skeleton points based on the largest shape element. By this, overall skeleton subsets will be reduced. The present method is applied on various images and is compared with generalized skeleton transform and octagon-generating decomposition algorithm.Keywords
Skeleton Subsets, Reconstruction, Shape Component, Structuring Element, Shape Representation.Full Text
- Automatic Speech Recognition System using MFCC-based LPC approach with Back Propagated Artificial Neural Networks
Abstract Views :229 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science Engineering, Centurion University of Technology and Management, IN
2 Department of Information Technology, Shri Vishnu Engineering College for Women, IN
3 Department of Electronics and Communication Engineering, Centurion University of Technology and Management, IN
1 Department of Computer Science Engineering, Centurion University of Technology and Management, IN
2 Department of Information Technology, Shri Vishnu Engineering College for Women, IN
3 Department of Electronics and Communication Engineering, Centurion University of Technology and Management, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 4 (2020), Pagination: 2153-2159Abstract
Over the previous years, a marvelous quantity of study was performed by utilizing the artificial intelligence based deep learning approaches for the speech recognition applications. The automatic speech recognition (ASR) facing the problems in as preprocessing, feature extraction and classification stages mostly, thus solving these problems is mandatory to improve the classification accuracy of speech processing. To solve these issues, an advanced speech recognition methodology has developed by utilizing the Spectral Subtraction (SS) method of denoising with the combination of Mel-frequency Cepstral coefficients (MFCCs) and linear predictive coefficients (LPCs) feature extraction of speech signals. Then back propagated artificial neural networks (BP-ANN) is utilized for classifying the speech signals for the purpose of ASR, respectively. The simulation results show that the proposed approach gives the better classification accuracy compared to the state-of-ASR approaches..Keywords
Speech Processing, Automatic Speech Recognition, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Artificial Neural Networks.
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