Abstract

This chapter describes the role of ontologies and corporate taxonomies in managing the content and knowledge within organizations. Managing content in a reusable and effective manner is becoming increasingly important in knowledge centric organizations as the amount of content generated, both text based and rich media, is growing exponentially. Search, categorization and document characterization, content staging and content delivery are the key technology challenges in knowledge management systems. This chapter describes how corporate taxonomies and ontologies can help in making sense of huge amount of content that gets generated across the locations in different languages and formats Different information silos can be connected and workflow and collaboration can be achieved using ontologies. As the KM solutions are moving from a centralized approach to a distributed approach, a framework where multiple taxonomies and ontologies can co-exist with uniform interfaces is needed.

Abstract

Commonly used vocabulary in Indian language documents found on the web contain a number of words that have Sanskrit, Persian or English origin. However, such words may be written in different scripts with slight variations in spelling and morphology. In this paper we explore fuzzy string matching techniques to exploit this situation of relatively large number of cognates among Indian languages, which are higher when compared to an Indian language and a non-Indian language. We present an approach to identify cognates and make use of them for improving dictionary based CLIR when the query and documents both belong to two different Indian languages. We conduct experiments using a Hindi document collection and a set of Telugu queries and report the improvement due to cognate recognition and translation.

Abstract

The twin challenges of massive information overload via the web and ubiquitous computers present us with an unavoidable task: developing techniques to handle multilingual information robustly and efficiently, with as high quality performance as possible. Previous research activities on multilingual information access systems have studied cross-language information retrieval (CLIR), information extraction and factoid based question answering tasks in detail. It is believed by the research community and also previously acknowledged in a US-NSF report [Hovy et al., 1999] that a cross-language query focused summarization could play a vital role in multilingual information access, as a bridge between CLIR and machine translation. Surprisingly enough, no detailed studies exist yet on the effects of cross-linguality to query focused summarization. In this paper we study the effects of adding a cross-language dimension to query focused multi-document summarization for the Telugu-English language pair. We use a cross-lingual relevance based language modeling approach to generate extraction based summary. We evaluate the system using DUC1 2005 dataset using ROUGE2 metrics and compare with the mono-lingual baseline which uses relevance based language modeling in mono-lingual setting.

Abstract

This paper reports on the first Oromo-English CLIR system that is based on dictionary-based query translation techniques. The basic objective of the study is to design and develop an Oromo-English CLIR system with a view to enable Afaan Oromo speakers to access and retrieve the vast online information sources that are available in English by using their own (native) language queries. We describe the major approaches and procedures that have been used in designing and developing Oromo-English CLIR system together with the information retrieval evaluation experiments recently conducted at CLEF 2006 ad hoc track. The purpose of the current initial evaluation experiments was to assess the over all performance of the Oromo-English CLIR system by using different fields of Afaan Oromo topics. Thus we submitted three official runs (experiments) that differed in terms of utilized fields in the topic set, ie title run (OMT), title and description run (OMTD), and title, description and narration run (OMTDN) to CLEF 2006. Since appropriate online language processing resources and information retrieval tools are not available for Afaan Oromo, only limited linguistic resources such as Oromo-English dictionary and Afaan Oromo stemmer that had been designed and developed at our research center were used in conducting the experiments. Yet we found the performances of our evaluation experiments at CLEF 2006 very encouraging which is a good news for development and application of CLIR systems in other indigenous African languages.

Abstract

In this paper we report our performance at DUC 2007 summarization tasks. We participated both in the query-focused multidocument summarization main task and in a pilot update summary generation tasks. This year we used a term clustering approach to better estimate a sentence prior. We used only the sentence prior which is query independent, in the update summarization task and found that it’s performance is comparable with the top performing systems. In the main task our system ranked 1 in ROUGE-2, ROUGE-SU4 and ROUGE-BE scores as well as in pyramid scores.

Abstract

A new probabilistic tracking framework for integrating information available from various visual cues is presented in this paper. The framework allows selection of “good” features for each cue, along with factors of their “goodness” to select the best combination form. Two particle filter based trackers, which use edge and texture features, run independently. The output of the master tracker is computed using democratic integration using the “goodness” weights. The final output is used as apriori for both tracker in the next iteration. Finally, particle filters are used to deal with non-Gaussian errors in feature extraction / prior computation. Results are shown for planar object tracking.

Abstract

Most robotic vision algorithms are proposed by envisaging robots operating in structured environments where the world is assumed to be rigid. These algorithms fail to provide optimum behavior when the robot has to be controlled with respect to active non-rigid targets. This paper presents a new framework for visual servoing that accomplishes the robot positioning task even in non-rigid environments. We introduce a space-time representation scheme for modeling the deformations of a non-rigid object and propose a model-free hybrid approach that exploits the two-view geometry induced by the space-time features to perform the servoing task. Our formulation can address a variety of non-rigid motions and can tackle large camera displacements without being affected by the degeneracies in the task space. Experimental results validate our approach and demonstrate the robust and stable behavior.

Abstract

In this paper, we present a new hybrid visual servoing algorithm for robot arm positioning task. Hybrid methods in visual servoing partially combine the 2D and 3D visual information to improve the performance of the traditional image-based and position-based visual servoing. Our algorithm is superior to the state of the art hybrid methods. The objective function has been designed to include the full 2D and 3D information available either from the CAD model or from the partial reconstruction process by decomposing the homography matrix between two views. Here, each of 2D and 3D error functions is used to control the six degrees of freedom. We call this method 5D visual servoing. The positioning task has been formulated as a minimization problem. Gradient decent as a first order approximation and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show that these two methods provide an efficient solution to the camera retreat and features visibility problems. The camera trajectory in the Cartesian space is also shown to be satisfactory.

Abstract

Population size plays a major role in determining the performance of any biometric authentication system, particularly when such systems are used for civilian applications. In this paper, we propose to improve the performance of a biometric authentication system by modeling the variation in the population participating in the process. We show that this technique is helpful when the number of users enrolled into the system is very large as compared to the number of users which actually participate in the process. We show results on aperiodically and periodically varying population using Markov models.

Abstract

We propose a video mosaicing scheme which exploits the motion information, implicitly available in the video. The information about the camera motion is propagated to the homographies used for mosaicing. While some of the recent approaches make use of the information stemming from non-overlapping pairs of frames, the smoothness of the camera motion has gone largely under-capitalized. We present a technique which exploits this useful cue for refining homographies. Moreover, a generic framework which exploits the camera motion model, to relate homographies in a video, is also proposed. The analysis and results of the proposed algorithms demonstrate significant promise, in terms of accuracy and robustness.