Abstract

Retrieval and indexing in multimedia databases has been an active topic both in the Information Retrieval and computer vision communities for a long time. In this paper we propose a novel region based indexing and retrieval scheme for images. First we present our virtual textual description using which, images are converted to text documents containing keywords. Then we look at how these documents can be indexed and retrieved using modified elastic bucket tries and show that our approach is one order better than standard spatial indexing approaches. We also show various operations required for dealing with complex features like relevance feedback. Finally we analyze the method comparatively and and validate our approach.

Abstract

In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof-the-art methods in handwritten stroke recognition have used a pre-determined combination of these features, which is not optimal in all situations. The proposed model addresses this issue by learning mixtures of offline and online characteristics from a set of exemplars. Each stroke is represented as a probabilistic sequence of substrokes with varying compositions of these features. The model adapts to any stroke and chooses the feature composition that best characterizes it. The superiority of the method is demonstrated on handwritten numeral and character strokes.

Abstract

In this paper, we present a new integration method for improving the performance of visual servoing. The method integrates both image-based visual servoing (IBVS) and positionbased visual servoing (PBVS) to satisfy the requirements of the visual servoing process. We define a probabilistic integration rule for IBVS and PBVS controllers. Density functions that determine the probability of each controller are defined to satisfy the above constraints. We prove that this integration method provides global stability, and avoids local minima. The new integration method is validated on positioning tasks, and compared with other switching methods. I. I

Abstract

Synthesis of handwriting has a variety of applications including generation of personalized documents, study of writing styles, automatic generation of data for training recognizers, and matching of handwritten data for retrieval. Most of the existing algorithms for handwriting synthesis deal with English, where the spatial layout of the components are relatively simple, while the cursiveness of the script introduces many challenges. In this paper, we present a synthesis model for generating handwritten data for Indian languages, where the layout of characters is complex while the script is fundamentally non-cursive. The algorithm learns from annotated data and improves its representation with feedback.

Abstract

In this paper we share the experience gained from establishing a process and a supporting architecture for the Digital Library of India (DLI) project. The DLI project was started with a vision of digitizing books and making them available online, in a searchable and browseable form. The digitization of the books takes place at geographically distributed locations. This raises many issues related to policy and collaboration. We discuss these problems in detail and present the process and workflow that is established to solve them. We also share the architecture of the project that supports the smooth implementation of the process. The architecture of the DLI project has been arrived at after considering factors like high performance, scalability, availability and economy.

Abstract

Relevance feedback in Content Based Image Retrieval(CBIR) has been an active field of research for quite some time now. Many schemes and techniques of relevance feedback exist with many assumptions and operating criteria. Yet there exist few ways of quantitatively measuring and comparing different relevance feedback algorithms. Such analysis is necessary if a CBIR system is to perform consistently. In this paper we propose an abstract model of a CBIR system where the effects of different modules over the entire system is observed. Using this model we thoroughly analyse performance a set of basic relevance feedback algorithms. Besides using standard measures like precision and recall we also suggest two new measures to gauge the performance of any contemporary CBIR system.

Abstract

In this paper we present a new integration method for improving the performance of visual servoing. The method integrates image-based visual servoing (IBVS) and positionbased visual servoing (PBVS) approaches to satisfy the widely varying requirements of the visual servoing process. We define an integration rule for IBVS and PBVS controllers. Density functions that determine the weighting factor of each controller are defined to satisfy the above constraints. We prove that this integration method provides global stability, and avoids local minima. The new integration method is validated on positioning tasks and compared with other switching methods.

Abstract

Many problems in computer vision such as pose recovery and structure estimation are formulated as a minimization process. These problems vary in the use of image measurements directly or using them to extract 3D cues in the minimization process. Hybrid methods have the advantage of combining the 2D and 3D visual information to improve the performance over the above two methods. In this paper, we present a new formulation for minimizing a class of hybrid error functions. This is done by using 2D information from the image space and 3D information from the Cartesian space in one error function. Applications to visual servoing and image alignment problems are presented. The positioning task of a robot arm has been formulated as a minimization problem. Gradient decent as a first order approxima ion and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show, comparing with 2 1/2 D hybrid method, that these two\ methods provide an efficient solution to the features visibility problems and the camera trajectory in the Cartesian space.

Abstract

Most of the state-of-the-art segmentation algorithms are de-signed to handle complex document layouts and backgrounds, while assuming a simple script structure such as in Roman script. They perform poorly when used with Indian languages, where the components are not strictly collinear. In this paper, we propose a document segmentation algorithm that can handle the complexity of Indian scripts in large document image collections. Segmentation is posed as a graph cut problem that incorporates the a prior information from script structure in the objective function of the cut. We show that this information can be learned automatically and be adapted within a collection of documents (a book)and across collections to achieve accurate segmentation. We show the results on Indian language documents in Telugu script. The approach is also applicable to other languages with complex scripts such as Bangla,Kannada, Malayalam, and Urdu

Abstract

Annotated datasets of handwriting are a prerequisite to attempt a variety of problems such as building recognizers, developing writer identification algorithms, etc. However, the annotation of large datasets is a tedious and expensive process, especially at the character or stroke level. In this paper we propose a novel, automated method for annotation at the character level, given a parallel corpus of online handwritten data and the corresponding text. The method employs a model-based handwriting synthesis unit to map the two corpora to the same space and the annotation is propagated to the word level and then to the individual characters using elastic matching. The initial results of annotation are used to improve the handwriting synthesis model for the user under consideration, which in turn refines the annotation. The method can take care of errors in the handwriting such as spurious and missing strokes or characters. The output is stored in the UPXInkML format