Now showing items 1-3 of 3
| Abstract: | Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance |
| Description: | Neural Computing and Applications Vol 21(7),pp 1757-1763 |
| URI: | http://dyuthi.cusat.ac.in/purl/4207 |
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| Content Based I ... Handwritten Characters.pdf | (547.1Kb) |
| Abstract: | This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor. |
| Description: | 2013 Third International Conference on Advances in Computing and Communications |
| URI: | http://dyuthi.cusat.ac.in/purl/4316 |
| Files | Size |
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| Content Based V ... using SURF Descriptor.pdf | (334.0Kb) |
| Abstract: | Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations |
| Description: | Advances in Computing and Communications (ICACC), 2012 International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/3888 |
| Files | Size |
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| Feature Extract ... ken Words in Malayalam.pdf | (261.9Kb) |
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