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Panigrahi N.,Center for and Robotics
Journal of the Indian Society of Remote Sensing | Year: 2015

Different classification techniques are being designed and under usage for classification of hyper spectral images. The usage of these classifiers differ for different type of hyper spectral data and application domain. The performance of these classifiers are influenced by feature preprocessing stage. In this research work we have investigated the impact of feature preprocessing using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on classification stage. The experiment is carried out using three sets of hyperspectral data. Classifications were carried out using three different classification techniques namely Maximum Likelihood Estimation (MLE), Constraint Energy Minimization (CEM) and Spectral Angle Mapper (SAM) on the preprocessed hyperspectral data. It is observed that the impact of PCA and LDA on the classification techniques are in two aspects (a) The preprocessing techniques facilitates to achieve high classification accuracy even with less number of training features and (b) Preprocessing expedites the classification process for large data sets. Also it can be concluded that PCA outperforms LDA in case of noisy data. © 2014, Indian Society of Remote Sensing. Source

Sharma A.M.,Center for and Robotics | Venkatesh K.S.,Indian Institute of Technology Kanpur | Mukerjee A.,Indian Institute of Technology Kanpur
ICIIP 2011 - Proceedings: 2011 International Conference on Image Information Processing | Year: 2011

Human pose estimation from monocular image sequences is attracting increasing attention, and 2D (image-based) as well as 3D (joint-motion based) approaches have been proposed. The former is computationally fast, and works also for less frequent poses, but reliability is low. The latter is computationally expensive owing to the high-dimensionality of the problem, despite attempts at dimensionality-reduction. We propose to impose temporal continuity constraints on the 2D approaches in order to improve reliability and obtain spatio-temporal descriptions of the action in the image plane. In the first stage, at each frame-level spatial localisation will be done by applying background subtraction, after that static image pose estimation is performed using CRF-based probabilistic assembly of parts, based on the approach by Ramanan [1]. Next, pose continuity is imposed via first-order Markovian constraints on the CRF search. This results in improved spatial accuracy as well as significantly reduced search. We present results from the Weizmann video dataset to demonstrate how such an approach can serve as an image-plane based alternative to full 3D modeling or manifold search. © 2011 IEEE. Source

Mandal A.,Indian Institute of Technology Kharagpur | Prasanna Kumar K.R.,Center for and Robotics | Mitra P.,Indian Institute of Technology Kharagpur
International Journal of Speech Technology | Year: 2014

Spoken term detection (STD) provides an efficient means for content based indexing of speech. However, achieving high detection performance, faster speed, detecting ot-of-vocabulary (OOV) words and performing STD on low resource languages are some of the major research challenges. The paper provides a comprehensive survey of the important approaches in the area of STD and their addressing of the challenges mentioned above. The review provides a classification of these approaches, highlights their advantages and limitations and discusses their context of usage. It also performs an analysis of the various approaches in terms of detection accuracy, storage requirements and execution time. The paper summarizes various tools and speech corpora used in the different approaches. Finally it concludes with future research directions in this area. © 2013 Springer Science+Business Media New York. Source

Singh S.,Center for and Robotics | Krishna K.M.,Robotics Research Center
Proceedings of the IEEE International Conference on Control Applications | Year: 2013

In this paper we develop an algorithm to generate gait sequences to negotiate a discontinuous terrain for a hybrid 4-wheeled legged robot. The gait sequence comprises two main steps - normal force redistribution and hybrid position-force control. The robot climbs the discontinuity one leg at a time. This requires that the entire load of the robot is taken up by the other three legs so that the leg climbing the discontinuity is free. For this purpose a load redistribution methodology is used which makes the center of gravity of chassis coincide with the desired center of pressure (CoP). Subsequently the free leg moves in hybrid position and force control to climb the discontinuity. Force sensing ensures constant contact with the terrain and detection of stand and end of the discontinuity without using any perception sensor. The methodology is validated using multi-body dynamic simulation. © 2013 IEEE. Source

Suri N.N.R.R.,Center for and Robotics | Murty M.N.,Indian Institute of Science | Athithan G.,Scientific Analysis Group
Natural Computing | Year: 2015

Outlier detection is an important data mining task with many contemporary applications. Clustering based methods for outlier detection try to identify the data objects that deviate from the normal data. However, the uncertainty regarding the cluster membership of an outlier object has to be handled appropriately during the clustering process. Additionally, carrying out the clustering process on data described using categorical attributes is challenging, due to the difficulty in defining requisite methods and measures dealing with such data. Addressing these issues, a novel algorithm for clustering categorical data aimed at outlier detection is proposed here by modifying the standard (Formula presented.)-modes algorithm. The uncertainty regarding the clustering process is addressed by considering a soft computing approach based on rough sets. Accordingly, the modified clustering algorithm incorporates the lower and upper approximation properties of rough sets. The efficacy of the proposed rough (Formula presented.)-modes clustering algorithm for outlier detection is demonstrated using various benchmark categorical data sets. © 2015 Springer Science+Business Media Dordrecht Source

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