Abstract

Different methodologies exist to direct the motion of sensors to detect targets moving across an environment in various scenarios. However, some of these do not model that navigation scenario in an environment in which obstacles are present. We extend our earlier algorithm for optimal target detection, as well as other algorithms reported in literature, to this case, and make a detailed comparison of their performance. This makes clear that the current algorithm is competitive in applications where target statistics are known in advance; otherwise, a heuristic technique by Sukhatme and Jung performs best.

Abstract

We present a strategy for resolving multiple hypotheses of a robot's state during global localization. The strategy operates in two stages. In the first stage a unique direction of the motion is sought that resolves or eliminates maximum number of hypotheses. In the second stage, among the frontier areas arising from the multiple hypotheses states, that frontier is chosen which resolves the maximum number of the hypotheses. The two stages are alternated till a unique hypothesis emerges. Simulation and experimental results verify the efficacy of this method. A comparison with other methods based on entropy minimization, and minimum distance travel portrays the advantage of the current methodology. A convergence proof for the algorithm is also presented.

Abstract

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Abstract

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data

Abstract

Digital Libraries have received wide attention in the recent years allowing access to digital information from anywhere across the world. They have become widely accepted and even preferred information sources in areas of education, science and others. When the full potential of digital libraries is realized, any citizen will for the first time be able to access all human knowledge immediately from any location. For a successful and useful Digital Library, the assurance of quality of the product from digitization process is quite essential. In this paper we discuss the major quality concerns of data from the digitization process, and how we assure for better quality before they are web enabled for the end-user to use. Also we discuss a Quality Management Framework that we applied for the improvement of both the Quality of the digital output and the efficiency of the processes.

Abstract

Many of the human activities such as Jumping, Squatting have a correlated spatiotemporal structure. They are composed of homogeneous units. These units, which we refer to as actions, are often common to more than one activity. Therefore, it is essential to have a representation which can capture these activities effectively. To develop this, we model the frames of activities as a mixture model of actions and employ a probabilistic approach to learn their low-dimensional representation. We present recognition results on seven activities performed by various individuals. The results demonstrate the versatility and the ability of the model to capture the ensemble of human activities.

Abstract

In this paper we propose a novel technique for robust visual servoing in presence of a large proportion of outliers in image measurements. The method employs robust statistical techniques and novel view prediction for improving the performance. We identify a set of points from initial and reference images and compute the essential matrix relating them. The selected points are predicted from the initial and reference images to the current frame using essential matrices. A function of the difference between the observed and predicted image point measurements is used to identify outliers. This technique is validated with many experiments and compared with other robust methods in a simulation framework.

Abstract

In this paper we describe a spectrum of algorithm for rectification of document images of camera based analysis and recognition. Clues like document boundaries page ,page layout information, organisation of text and graphics, components, apriori knowledge of script or selected symbols etc. are effectively used for removal of perspective effect and comforting the frontal view needed for a typical document image analysis algorithm. Appropriate results from projective geometry for planar surfaces are exploited in these situations.

Abstract

In Africa, there are a number of languages with their own indigenous scripts. This paper presents an OCR for Amharic scripts. Amharic is the official and working language of Ethiopia. This is possibly the first attempt towards the development of an OCR system for Amharic. Research in the recognition of Amharic script faces major challenges due to (i) the use of more than 300 characters in writing and (ii) existence of a large set of visually similar characters. In this paper, we propose a two-stage feature extraction scheme using PCA and LDA, followed by a decision DAG classifier with SVMs as the nodes. Recognition results are presented to demonstrate the performance on the various printing variations (fonts, styles and sizes) and real-life degraded documents such as books, magazines and newspapers.

Abstract

A discriminant-based framework for automatic recognition of online handwriting data is presented in this paper. We identify the substrokes that are more useful in discriminating between two online strokes. A similarity/dissimilarity score is computed based on the discriminatory potential of various parts of the stroke for the classification task. The discriminatory potential is then converted to the relative importance of the substroke. Experimental verification on online data such as numerals, characters supports our claims. We achieve an average reduction of
 in the classification error rate on many test sets of similar character pair