Arti Bang - Academia.edu (original) (raw)
Papers by Arti Bang
Lecture notes in networks and systems, Dec 31, 2022
2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Depth estimation has vital importance in various applications like Advance driver assistance syst... more Depth estimation has vital importance in various applications like Advance driver assistance systems, Robot navigation, 3D movies, etc. Depth information is represented using disparity maps which are generated from various stereo correspondence algorithms. This paper proposes an implementation of Semi global block matching algorithm on raspberry pi using python. The proposed algorithm generates a disparity map by block wise matching and smoothness constraint. The novel algorithm is compared with other algorithms on the standard dataset. It provides an efficient accuracy over local methods. The real time implementation of SGM algorithm on the system uses a block size of 21 × 21 for images having resolution 1280×720P. The system provides faster processing time for the proposed algorithm.
... Aug. 2008. [5] G. Jayakumar and G. Ganapathy, Performance comparison of mobile ad-hoc networ... more ... Aug. 2008. [5] G. Jayakumar and G. Ganapathy, Performance comparison of mobile ad-hoc network routing protocol, International Journal of Computer Science and Network Security, vol. 7, no. 11, pp. 7784, Nov. 2007. [6 ...
International journal of image mining, 2018
Depth estimation has applications like robot navigation, advance driver assistance systems, 3D mo... more Depth estimation has applications like robot navigation, advance driver assistance systems, 3D movies etc. Depth is represented in terms of disparity map which can be generated using various stereo correspondence algorithms. This paper presents an implementation of semi global block matching algorithm on raspberry pi to estimate the depth from the camera. The algorithm computes the disparity using block wise matching and smoothness constraint. The proposed algorithm is compared with SAD algorithm on personal computer as well as on raspberry pi. The algorithm is also further, evaluated on the standard dataset. The project aim is to detect people in an image and estimate their depth from the camera. The real time implementation of the proposed algorithm uses block size of 21 × 21 for images which has resolution of 1280 × 720 P. The algorithm estimates depth with an accuracy of 95%. The system also provides faster processing time to the proposed algorithm.
Zenodo (CERN European Organization for Nuclear Research), Mar 10, 2023
Zenodo (CERN European Organization for Nuclear Research), Mar 10, 2023
Bulletin of Electrical Engineering and Informatics, Aug 1, 2023
Presently, fast proliferation of information enforces novel challenges on content management. Fur... more Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine setup bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with kneighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.
Chapman and Hall/CRC eBooks, Dec 12, 2022
Cognitive science and technology, 2022
Springer International Publishing eBooks, 2023
Artificial Intelligence in Information and Communication Technologies, Healthcare and Education
Bulletin of Electrical Engineering and Informatics
Presently, fast proliferation of information enforces novel challenges on content management. Fur... more Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing...
2022 International Conference on Industry 4.0 Technology (I4Tech)
International Journal of Computer Applications in Technology, 2017
It is necessary to develop efficient methods for monitoring and recognising bird species that wil... more It is necessary to develop efficient methods for monitoring and recognising bird species that will help in evaluating the biodiversity of a region. In this paper we present techniques for automatic recognition of bird species based on audio recordings of their sounds. In this work, various audio features like descriptive features, wavelet packet decomposition-based features and perceptual features like Mel-frequency cepstral coefficients, perceptual linear prediction, and human factor cepstral coefficients are evaluated. Combination of these feature sets has also been evaluated. Classification of ten bird species is carried out using Gaussian Mixture Modelling (GMM) and Support Vector Machines (SVMs). When a number of features are extracted, the feature vector may contain redundancy. Redundant features may either degrade the performance of the system or add no value to the system. For feature subset selection, this work implements a technique based on singular value decomposition and QR decomposition using column pivoting.
International Journal of Image Mining, 2018
Lecture notes in networks and systems, Dec 31, 2022
2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Depth estimation has vital importance in various applications like Advance driver assistance syst... more Depth estimation has vital importance in various applications like Advance driver assistance systems, Robot navigation, 3D movies, etc. Depth information is represented using disparity maps which are generated from various stereo correspondence algorithms. This paper proposes an implementation of Semi global block matching algorithm on raspberry pi using python. The proposed algorithm generates a disparity map by block wise matching and smoothness constraint. The novel algorithm is compared with other algorithms on the standard dataset. It provides an efficient accuracy over local methods. The real time implementation of SGM algorithm on the system uses a block size of 21 × 21 for images having resolution 1280×720P. The system provides faster processing time for the proposed algorithm.
... Aug. 2008. [5] G. Jayakumar and G. Ganapathy, Performance comparison of mobile ad-hoc networ... more ... Aug. 2008. [5] G. Jayakumar and G. Ganapathy, Performance comparison of mobile ad-hoc network routing protocol, International Journal of Computer Science and Network Security, vol. 7, no. 11, pp. 7784, Nov. 2007. [6 ...
International journal of image mining, 2018
Depth estimation has applications like robot navigation, advance driver assistance systems, 3D mo... more Depth estimation has applications like robot navigation, advance driver assistance systems, 3D movies etc. Depth is represented in terms of disparity map which can be generated using various stereo correspondence algorithms. This paper presents an implementation of semi global block matching algorithm on raspberry pi to estimate the depth from the camera. The algorithm computes the disparity using block wise matching and smoothness constraint. The proposed algorithm is compared with SAD algorithm on personal computer as well as on raspberry pi. The algorithm is also further, evaluated on the standard dataset. The project aim is to detect people in an image and estimate their depth from the camera. The real time implementation of the proposed algorithm uses block size of 21 × 21 for images which has resolution of 1280 × 720 P. The algorithm estimates depth with an accuracy of 95%. The system also provides faster processing time to the proposed algorithm.
Zenodo (CERN European Organization for Nuclear Research), Mar 10, 2023
Zenodo (CERN European Organization for Nuclear Research), Mar 10, 2023
Bulletin of Electrical Engineering and Informatics, Aug 1, 2023
Presently, fast proliferation of information enforces novel challenges on content management. Fur... more Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine setup bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with kneighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.
Chapman and Hall/CRC eBooks, Dec 12, 2022
Cognitive science and technology, 2022
Springer International Publishing eBooks, 2023
Artificial Intelligence in Information and Communication Technologies, Healthcare and Education
Bulletin of Electrical Engineering and Informatics
Presently, fast proliferation of information enforces novel challenges on content management. Fur... more Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing...
2022 International Conference on Industry 4.0 Technology (I4Tech)
International Journal of Computer Applications in Technology, 2017
It is necessary to develop efficient methods for monitoring and recognising bird species that wil... more It is necessary to develop efficient methods for monitoring and recognising bird species that will help in evaluating the biodiversity of a region. In this paper we present techniques for automatic recognition of bird species based on audio recordings of their sounds. In this work, various audio features like descriptive features, wavelet packet decomposition-based features and perceptual features like Mel-frequency cepstral coefficients, perceptual linear prediction, and human factor cepstral coefficients are evaluated. Combination of these feature sets has also been evaluated. Classification of ten bird species is carried out using Gaussian Mixture Modelling (GMM) and Support Vector Machines (SVMs). When a number of features are extracted, the feature vector may contain redundancy. Redundant features may either degrade the performance of the system or add no value to the system. For feature subset selection, this work implements a technique based on singular value decomposition and QR decomposition using column pivoting.
International Journal of Image Mining, 2018