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Search Results: 1 - 10 of 1278 matches for " Kannan Balakrishnan "
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Modeling and Annotating the Expressive Semantics of Dance Videos
Rajkumar Kannan,Balakrishnan Ramadoss
Computer Science , 2010,
Abstract: Dance videos are interesting and semantics-intensive. At the same time, they are the complex type of videos compared to all other types such as sports, news and movie videos. In fact, dance video is the one which is less explored by the researchers across the globe. Dance videos exhibit rich semantics such as macro features and micro features and can be classified into several types. Hence, the conceptual modeling of the expressive semantics of the dance videos is very crucial and complex. This paper presents a generic Dance Video Semantics Model (DVSM) in order to represent the semantics of the dance videos at different granularity levels, identified by the components of the accompanying song. This model incorporates both syntactic and semantic features of the videos and introduces a new entity type called, Agent, to specify the micro features of the dance videos. The instantiations of the model are expressed as graphs. The model is implemented as a tool using J2SE and JMF to annotate the macro and micro features of the dance videos. Finally examples and evaluation results are provided to depict the effectiveness of the proposed dance video model. Keywords: Agents, Dance videos, Macro features, Micro features, Video annotation, Video semantics.
Semantic Modeling and Retrieval of Dance Video Annotations
Rajkumar Kannan,Balakrishnan Ramadoss
Computer Science , 2010,
Abstract: Dance video is one of the important types of narrative videos with semantic rich content. This paper proposes a new meta model, Dance Video Content Model (DVCM) to represent the expressive semantics of the dance videos at multiple granularity levels. The DVCM is designed based on the concepts such as video, shot, segment, event and object, which are the components of MPEG-7 MDS. This paper introduces a new relationship type called Temporal Semantic Relationship to infer the semantic relationships between the dance video objects. Inverted file based index is created to reduce the search time of the dance queries. The effectiveness of containment queries using precision and recall is depicted. Keywords: Dance Video Annotations, Effectiveness Metrics, Metamodeling, Temporal Semantic Relationships.
A prototype Malayalam to Sign Language Automatic Translator
Jestin Joy,Kannan Balakrishnan
Computer Science , 2014,
Abstract: Sign language, which is a medium of communication for deaf people, uses manual communication and body language to convey meaning, as opposed to using sound. This paper presents a prototype Malayalam text to sign language translation system. The proposed system takes Malayalam text as input and generates corresponding Sign Language. Output animation is rendered using a computer generated model. This system will help to disseminate information to the deaf people in public utility places like railways, banks, hospitals etc. This will also act as an educational tool in learning Sign Language.
Significance of Classification Techniques in Prediction of Learning Disabilities
Julie M. David,Kannan Balakrishnan
International Journal of Artificial Intelligence & Applications , 2010,
Abstract: The aim of this study is to show the importance of two classification techniques, viz. decision tree andclustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent ofall children enrolled in schools. The problems of children with specific learning disabilities have been acause of concern to parents and teachers for some time. Decision trees and clustering are powerful andpopular tools used for classification and prediction in Data mining. Different rules extracted from thedecision tree are used for prediction of learning disabilities. Clustering is the assignment of a set ofobservations into subsets, called clusters, which are useful in finding the different signs and symptoms(attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing thedecision tree and K-means algorithm is used for creating the clusters. By applying these classificationtechniques, LD in any child can be identified.
Significance of Classification Techniques in Prediction of Learning Disabilities
Julie M. David And Kannan Balakrishnan
Computer Science , 2010, DOI: 10.5121/ijaia.2010.1409
Abstract: The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.
A System for Offline Recognition of Handwritten Characters in Malayalam Script
Jomy John,Kannan Balakrishnan,Pramod K. V
International Journal of Image, Graphics and Signal Processing , 2013,
Abstract: In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.
Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis
Sindhumol S.,Anil Kumar,Kannan Balakrishnan
ISRN Biomedical Imaging , 2013, DOI: 10.1155/2013/473437
Abstract: Multispectral analysis is a potential approach in simultaneous analysis of brain MRI sequences. However, conventional classification methods often fail to yield consistent accuracy in tissue classification and abnormality extraction. Feature extraction methods like Independent Component Analysis (ICA) have been effectively used in recent studies to improve the results. However, these methods were inefficient in identifying less frequently occurred features like small lesions. A new method, Multisignal Wavelet Independent Component Analysis (MW-ICA), is proposed in this work to resolve this issue. First, we applied a multisignal wavelet analysis on input multispectral data. Then, reconstructed signals from detail coefficients were used in conjunction with original input signals to do ICA. Finally, Fuzzy C-Means (FCM) clustering was performed on generated results for visual and quantitative analysis. Reproducibility and accuracy of the classification results from proposed method were evaluated by synthetic and clinical abnormal data. To ensure the positive effect of the new method in classification, we carried out a detailed comparative analysis of reproduced tissues with those from conventional ICA. Reproduced small abnormalities were observed to give good accuracy/Tanimoto Index values, 98.69%/0.89, in clinical analysis. Experimental results recommend MW-ICA as a promising method for improved brain tissue classification. 1. Introduction Multispectral analysis of Magnetic Resonance Imaging (MRI) to access the relevant and complementary information from different sequences has been a widely discussed research topic for many years [1, 2]. MRI sequences like T1-weighted images (T1WI), T2-weighted images (T2WI), Proton Density Images (PDI), Fluid Attenuated Inversion Recovery (FLAIR), and so forth provide a huge repository of unique information on different tissues [2, 3]. For example, considerable contrast between Gray Matter (GM) and White Matter (WM) is available from T1WI. T2WI can give details of Cerebral Spinal Fluid (CSF) and abnormalities, whereas FLAIR images suppress CSF effects to give hyperintense lesions details. Simultaneous analysis of each sequence to collect the prominent pathological information is a tedious job for radiology experts. Computer-aided diagnosis using multispectral approach is helpful in this context to save time and to improve the accuracy and consistency of the clinical results [4]. But conventional algorithms used in normal data mining process are not efficient and robust to yield good results with expected clinical
Betweenness Centrality in Some Classes of Graphs
Sunil Kumar R,Kannan Balakrishnan,M. Jathavedan
Mathematics , 2014,
Abstract: There are several centrality measures that have been introduced and studied for real world networks. They account for the different vertex characteristics that permit them to be ranked in order of importance in the network. Betweenness centrality is a measure of the influence of a vertex over the flow of information between every pair of vertices under the assumption that information primarily flows over the shortest path between them. In this paper we present betweenness centrality of some important classes of graphs.
Handwritten Character Recognition of South Indian Scripts: A Review
John Jomy,K. V. Pramod,Balakrishnan Kannan
Computer Science , 2011,
Abstract: Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.
Tutoring System for Dance Learning
Rajkumar Kannan,Frederic Andres,Balakrishnan Ramadoss
Computer Science , 2010,
Abstract: Recent advances in hardware sophistication related to graphics display, audio and video devices made available a large number of multimedia and hypermedia applications. These multimedia applications need to store and retrieve the different forms of media like text, hypertext, graphics, still images, animations, audio and video. Dance is one of the important cultural forms of a nation and dance video is one such multimedia types. Archiving and retrieving the required semantics from these dance media collections is a crucial and demanding multimedia application. This paper summarizes the difference dance video archival techniques and systems. Keywords: Multimedia, Culture Media, Metadata archival and retrieval systems, MPEG-7, XML.
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