Browsing by Author "Naganjaneyulu, G.V.S.S.K.R."

Now showing 1 - 16 of 16

A Method for QRS Delineation Based on STFT Using Adaptive Threshold
(Elsevier, 2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.
Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. Â© 2015 The Authors.
A multi clue heuristic based algorithm for table detection
(Institute of Electrical and Electronics Engineers Inc., 2017) Naganjaneyulu, G.V.S.S.K.R.; Sathwik, N.V.; Narasimhadhan, A.V.
Research in the field of document analysis and document recognition experienced reverent growth in the past decade as automation of the office document became essential for daily life. Text in documents can take different forms like hand written text, printed text, headings signatures, tables and graphics. Extraction of tables plays a crucial role in layout analysis, and retaining the important information present in tables. In this work, a multi clue heuristic based table detection algorithm using hough lines and corner harris corner is proposed. Hough lines and harris corner points are extracted from the document in two parallel process. The clues extracted from both the process are matched using nearest neighbor framework to yield tables from the documents. The proposed algorithm is a simple paradigm for extraction of tables that are formed by lines. The performance of the proposed algorithm is tested on different types of documents that contain tables to observe an accuracy of 89.7 %. Â© 2016 IEEE.
A novel approach for QRS delineation in ECG signal based on chirplet transform
(Institute of Electrical and Electronics Engineers Inc., 2016) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.
ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection. Â© 2015 IEEE.
A novel method for logo detection based on curvelet transform using GLCM features
(Springer Verlag service@springer.de, 2018) Naganjaneyulu, G.V.S.S.K.R.; Sai Krishna, C.; Narasimhadhan, A.V.
Automatic logo detection is a key tool for document retrieval, document recognition, document classification, and authentication. It helps in office automation as it enables the effective identification of source of a document. In this paper, a novel approach for logo detection using curvelet transform is proposed. The curvelet transform is employed for logo detection because of its ability to represent curved singularities efficiently when compared to wavelet and ridgelet transforms. The proposed algorithm consists of five steps, namely segmentation, noncandidate elimination, computation of curvelet coefficients, gray level co-occurrence matrix (GLCM) features extraction, followed by classification using a pretrained support vector machine classifier. The proposed algorithm is tested on a standard dataset, and the performance is compared with the state-of-the-art methods. The results show good improvement in the accuracy when compared with the competitors. Â© Springer Nature Singapore Pte Ltd. 2018.
A novel method for pitch detection via instantaneous frequency estimation using polynomial chirplet transform
(Institute of Electrical and Electronics Engineers Inc., 2017) Naganjaneyulu, G.V.S.S.K.R.; Ramana, M.V.; Narasimhadhan, A.V.
Speech processing and synthesis have been the interests of many researchers for the past few decades. One of the primary task in speech processing is the estimation of fundamental frequency of speech (also known as pitch). Speech is a non stationary signal whose frequency varies arbitrarily with time. Linear time frequency analysis tools such as short time fourier transform may not be convenient for estimation of pitch of speech. Polynomial chirplet transform models the frequency of speech signal by a higher order polynomial of time, which makes it suitable for analysis of speech to extract pitch. In this work, a novel algorithm is proposed for pitch detection in speech by estimating instantaneous frequency (IF) using polynomial chirplet transform. The proposed algorithm is applied on a part of TIMIT speech database to find the pitch of speech of different male and female persons. Â© 2016 IEEE.
A Method for QRS Delineation Based on STFT Using Adaptive Threshold
(2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.
Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. � 2015 The Authors.
A multi clue heuristic based algorithm for table detection
(2017) Naganjaneyulu, G.V.S.S.K.R.; Sathwik, N.V.; Narasimhadhan, A.V.
Research in the field of document analysis and document recognition experienced reverent growth in the past decade as automation of the office document became essential for daily life. Text in documents can take different forms like hand written text, printed text, headings signatures, tables and graphics. Extraction of tables plays a crucial role in layout analysis, and retaining the important information present in tables. In this work, a multi clue heuristic based table detection algorithm using hough lines and corner harris corner is proposed. Hough lines and harris corner points are extracted from the document in two parallel process. The clues extracted from both the process are matched using nearest neighbor framework to yield tables from the documents. The proposed algorithm is a simple paradigm for extraction of tables that are formed by lines. The performance of the proposed algorithm is tested on different types of documents that contain tables to observe an accuracy of 89.7 %. � 2016 IEEE.
A novel approach for QRS delineation in ECG signal based on chirplet transform
(2016) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.
ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection. � 2015 IEEE.
A novel method for logo detection based on curvelet transform using GLCM features
(2018) Naganjaneyulu, G.V.S.S.K.R.; Sai, Krishna, C.; Narasimhadhan, A.V.
Automatic logo detection is a key tool for document retrieval, document recognition, document classification, and authentication. It helps in office automation as it enables the effective identification of source of a document. In this paper, a novel approach for logo detection using curvelet transform is proposed. The curvelet transform is employed for logo detection because of its ability to represent curved singularities efficiently when compared to wavelet and ridgelet transforms. The proposed algorithm consists of five steps, namely segmentation, noncandidate elimination, computation of curvelet coefficients, gray level co-occurrence matrix (GLCM) features extraction, followed by classification using a pretrained support vector machine classifier. The proposed algorithm is tested on a standard dataset, and the performance is compared with the state-of-the-art methods. The results show good improvement in the accuracy when compared with the competitors. � Springer Nature Singapore Pte Ltd. 2018.
A novel method for pitch detection via instantaneous frequency estimation using polynomial chirplet transform
(2017) Naganjaneyulu, G.V.S.S.K.R.; Ramana, M.V.; Narasimhadhan, A.V.
Speech processing and synthesis have been the interests of many researchers for the past few decades. One of the primary task in speech processing is the estimation of fundamental frequency of speech (also known as pitch). Speech is a non stationary signal whose frequency varies arbitrarily with time. Linear time frequency analysis tools such as short time fourier transform may not be convenient for estimation of pitch of speech. Polynomial chirplet transform models the frequency of speech signal by a higher order polynomial of time, which makes it suitable for analysis of speech to extract pitch. In this work, a novel algorithm is proposed for pitch detection in speech by estimating instantaneous frequency (IF) using polynomial chirplet transform. The proposed algorithm is applied on a part of TIMIT speech database to find the pitch of speech of different male and female persons. � 2016 IEEE.
Performance evaluation of OCR on poor resolution text document images using different pre processing steps
(2015) Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.; Venkatesh, K.
The performance of optical character recognition (OCR) algorithm is poor on low resolution scanned text images. The conventional low pass filters in L2 space can slightly improve the performance. The method of enhancement of poor resolution text images using a low pass signal filtering algorithm in the weighted Sobolev space results in high pass correction similar to un sharp masking. This can further improve the performance of OCR on low resolution text images. In this paper, the performance of a typical OCR system on low resolution scanned text images, is studied without using any preprocessing step, with low pass filtering in L2 space, and compared with low pass filtering in weighted Sobolev space as pre processing steps. � 2014 IEEE.
Performance evaluation of OCR on poor resolution text document images using different pre processing steps
(Institute of Electrical and Electronics Engineers Inc., 2015) Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.; Venkatesh, K.
The performance of optical character recognition (OCR) algorithm is poor on low resolution scanned text images. The conventional low pass filters in L2 space can slightly improve the performance. The method of enhancement of poor resolution text images using a low pass signal filtering algorithm in the weighted Sobolev space results in high pass correction similar to un sharp masking. This can further improve the performance of OCR on low resolution text images. In this paper, the performance of a typical OCR system on low resolution scanned text images, is studied without using any preprocessing step, with low pass filtering in L2 space, and compared with low pass filtering in weighted Sobolev space as pre processing steps. Â© 2014 IEEE.
R peak delineation in ECG signal based on polynomial chirplet transform using adaptive threshold
(2016) Naganjaneyulu, G.V.S.S.K.R.; Shaik, B.S.; Narasimhadhan, A.V.
R peak delineation is fundamental step in any application implicating electrocardiogram (ECG) signal. ECG is non stationary and non linear. Hence, linear transforms like short time fourier transform, wavelet transform and chirplet transform may be inadequate to represent ECG signal and consequently for R peak delineation. Polynomial chirplet transform (PCT) models the frequency into a higher order polynomial to enhance the representation of non stationary signals whose frequency vary non linearly with time. In this paper, PCT based R peak delineation method using adaptive threshold is proposed. The performance of the proposed algorithm is evaluated on ECG ID data base taken from physionet data bank. This work also presents a comparative study of QRS detection methods employing the uni scale family of time frequency analysis methods, short time fourier transform, chirplet transform, stockwell transform, wigner ville distribution, and pseudo wigner ville distribution out of which stockwell transform, pseudo wigner ville distribution along with adaptive threshold are applied to QRS detection for the first time. The results show that the proposed method outperforms the competitors in terms of sensitivity, specificity and detection error rate. � 2016 IEEE.
R peak delineation in ECG signal based on polynomial chirplet transform using adaptive threshold
(Institute of Electrical and Electronics Engineers Inc., 2016) Naganjaneyulu, G.V.S.S.K.R.; Shaik, B.S.; Narasimhadhan, A.V.
R peak delineation is fundamental step in any application implicating electrocardiogram (ECG) signal. ECG is non stationary and non linear. Hence, linear transforms like short time fourier transform, wavelet transform and chirplet transform may be inadequate to represent ECG signal and consequently for R peak delineation. Polynomial chirplet transform (PCT) models the frequency into a higher order polynomial to enhance the representation of non stationary signals whose frequency vary non linearly with time. In this paper, PCT based R peak delineation method using adaptive threshold is proposed. The performance of the proposed algorithm is evaluated on ECG ID data base taken from physionet data bank. This work also presents a comparative study of QRS detection methods employing the uni scale family of time frequency analysis methods, short time fourier transform, chirplet transform, stockwell transform, wigner ville distribution, and pseudo wigner ville distribution out of which stockwell transform, pseudo wigner ville distribution along with adaptive threshold are applied to QRS detection for the first time. The results show that the proposed method outperforms the competitors in terms of sensitivity, specificity and detection error rate. Â© 2016 IEEE.
Reconstruction of Edges from Fan-Beam Projections
(2019) Narasimhadhan, A.V.; Sharma, A.; Koolagudi, S.G.; Naganjaneyulu, G.V.S.S.K.R.; Avinash, S.; Peddireddy, V.; Kishan, N.B.; Rajan, J.
The goal of computerised tomography is to reconstruct cross sectional image of the object under consideration from it's projections whereas edge detection is an image analysis problem of utmost importance in medical imaging to outline the boundaries of tumours, bones etc. In this paper, a technique to reconstruct the edges directly from fan-beam projections, using the Marr-Hildreth operator, is presented. To obtain the edge map of object under consideration, the divergent beam transform of Marr-Hildreth operator is convolved with ramp filter to yield an edge reconstruction filter which is finally convolved with the acquired fan-beam projections and back-projected, resulting in a convolution back-projection, to reconstruct the edges. The paper also discusses about the utilisation of state-of-the-art Noo's algorithm to reconstruct the edges directly from equi-angular fan beam projections. Finally, the proposed technique is simulated to make relevant conclusions and inferences. � 2018 IEEE.
Reconstruction of Edges from Fan-Beam Projections
(Institute of Electrical and Electronics Engineers Inc., 2018) Narasimhadhan, A.V.; Sharma, A.; Koolagudi, S.G.; Naganjaneyulu, G.V.S.S.K.R.; Avinash, S.; Peddireddy, V.; Kishan, N.B.; Rajan, J.
The goal of computerised tomography is to reconstruct cross sectional image of the object under consideration from it's projections whereas edge detection is an image analysis problem of utmost importance in medical imaging to outline the boundaries of tumours, bones etc. In this paper, a technique to reconstruct the edges directly from fan-beam projections, using the Marr-Hildreth operator, is presented. To obtain the edge map of object under consideration, the divergent beam transform of Marr-Hildreth operator is convolved with ramp filter to yield an edge reconstruction filter which is finally convolved with the acquired fan-beam projections and back-projected, resulting in a convolution back-projection, to reconstruct the edges. The paper also discusses about the utilisation of state-of-the-art Noo's algorithm to reconstruct the edges directly from equi-angular fan beam projections. Finally, the proposed technique is simulated to make relevant conclusions and inferences. Â© 2018 IEEE.