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

The impact and importance of software has come a long way. And yet, a new generation of software developers must meet many of the same challenges that faced earlier generations. This paper aims to identify some of the most fundamental issues, challenges and opportunities for research in Software Engineering. The paper starts by examining the past, current, and future states of software engineering. The paper then examines the critical technical issues in software engineering including complexity, structure, and evolution of software systems; economics of software engineering, and measurement of software engineering products and processes, as well as the critical people and organizational issues including learning, motivation and performance improvement.

Abstract

Text classification techniques such as Bayesian classifiers have been proved to be giving as good or better results than much more sophisticated approaches to induction. These algorithms depend on the conditional independence of the attributes given the class and term based statistical representations of documents. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we investigate the use of word sense disambiguation and other lexical semantic based techniques to achieve consistent improvement with Bayesian classifiers. Finally, we discuss the test cases experimented with Rainbow-a standard Bayesian classifier.

Abstract

Multiclass classification is an important problem in pattern recognition. Hierarchical SVM classifiers such as DAG-SVM and BHC-SVM are popular in solving multiclass problems. However, a bottleneck with these approaches is the number of component classifiers, and the associated time and space requirements. In this paper, we describe a simple, yet effective method for efficiently storing support vectors that exploits the redundancies in them across the classifiers to obtain significant reduction in storage and computational requirements. We also present our extension to an algebraic exact simplification method for simplifying hierarchical classifier solutions.

Abstract

Fourier domain methods have had a long association with geometric vision. In this paper, we introduce Fourier domain methods into the field of visual servoing for the first time. We show how different properties of Fourier transforms may be used to address specific issues in traditional visual servoing methods, giving rise to algorithms that are more flexible. Specifically, we demonstrate how Fourier analysis may be used to obtain straight camera paths in the Cartesian space, do path following and correspondenceless visual servoing. Most importantly, by introducing Fourier techniques, we set a framework into which robust Fourier based geometry processing algorithms may be incorporated to address the various issues in servoing.

Abstract

A video from a moving camera produces different number of observations of different scene areas. We can construct an attention map of the scene by bringing the frames to a common reference and counting the number of frames that observed each scene point. Different representations can be constructed from this. The base of the attention map gives the scene mosaic. Super-resolved images of parts of the scene can be obtained using a subset of observations or video frames. We can combine mosaicing with superresolution by using all observations, but the magnification factor will vary across the scene based on the attention received. The height of the attention map indicates the amount of super-resolution for that scene point. We modify the traditional super-resolution framework to generate a varying resolution image for panning cameras in this paper. The varying resolution image uses all useful data available in a video. We introduce the concept of attention-based super-resolution and give the modified framework for it. We also show its applicability on a few indoor and outdoor videos.

Abstract

State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. These solutions need to be adapted frequently and sometimes fail to perform well for complex scripts. This calls for a generalized solution that achieves a one shot segmentation that is globally optimal. This paper describes one such solution based on the optimization problem of spectral partitioning which makes the decision of proper segmentation based on the spectral properties of the pairwise similarity matrix. The solution described in the paper is shown to be general, global and closed form. The claims have been demonstrated on 142 page images from a Telugu book, in a script set in both poetry and prose layouts. This particular class of scripts has been proved to be challenging for the existing state of the art algorithms, where the proposed solution achieves significant results.

Abstract

Image based methods are a new approach for solving problems in mobile robotics. Instead of building a metric (3D) model of the environment, these methods work directly in the sensor (image) space. The environment is represented as a topological graph in which each node contains an image taken at some pose in the workspace, and edges connect poses between which a simple path exists. This type of representation is highly scalable and is also well suited to handle the data association problems that effect metric model based methods. In this paper, we present an efficient, adaptive method for qualitative localization using content based image retrieval techniques. In addition, we demonstrate an algorithm which can convert this topological graph into a metric model of the environment by incorporating information about loop closures

Abstract

The problem of character recognition in a book should be formulated significantly different from that of a single page or word. An ideal approach to design such a recognizer is to adapt the classifier to the font and style of the collection. In this paper, we propose an adaptation framework to recognize characters in a book with a learning framework. In the proposed system, the post processor verifies the output of the recognition module, which is further used for learning and thus to improve the performance over iteration. Experiments are conducted on about 500,000 annotated symbols from five books in Malayalam (an Indian language). We achieve an average improvement of 14% in classification accuracy.

Abstract

Restoration of documents has critical applications in document understanding as well as in digital libraries (for example as in book readers). This paper presents a method for restoration of document images, using a Maximum a Posteriori formulation. The advantage of our method is that the prior need not be learned from the training images. The extraction of a single high-quality enhanced text image from a set of degraded images can benefit from a strong prior knowledge, typical of text images. The restoration process should allow for discontinuities but at the same time discourage oscillations. These properties were represented in a total variation based prior model. Results indicate that our method is appropriate for document image restoration, where resolution enhancement is an added gain.