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

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

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 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

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 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

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

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

Most robotic vision algorithms have been proposed by envisaging robots operating in industrial environments, where the world is assumed to be static and rigid. These algorithms cannot be used in environments where the assumption of a rigid world does not hold. In this paper, we study the problem of visual servoing in presence of nonrigid objects and analyze the design of servoing strategies needed to perform optimally even in unconventional environments. We also propose a servoing algorithm that is robust to non-rigidity. The algorithm extracts invariant features of the non-rigid object and uses these features in the servoing process. We validate the technique with experiments and demonstrate the applicability

Abstract

In this paper, we present a novel method for model estimation for visual servoing. This method employs a particle filter algorithm to estimate the depth of the image features online. A Gaussian probabilistic model is employed to model the object points in the current camera frame. A set of 3D samples drawn from the model is projected into the image space in the next frame. The 3D sample that maximizes the likelihood is the most probable real-world 3D point. The variance value of the depth density function converges to very small value within a few iterations. Results show accurate estimate of the depth/model and a high level of stability in the visual servoing process