Abstract

Dynamic events comprise of spatiotemporal atomic units. In this paper we model them using a mixture model. Events are represented using a framework based on the Mixture of Factor Analyzers (MFA) model. It is to be noted that our framework is generic and is applicable for any mixture modelling scheme. The MFA, used to demonstrate the novelty of our approach, clusters events into spatially coherent mixtures in a low dimensional space. Based the observations that, (i) events comprise of varying degrees of spatial and temporal characteristics, and (ii) the number of mixtures determines the composition of these features, a method that incorporates models with varying number of mixtures is proposed. For a given event, the relative importance of each model component is estimated, thereby choosing the appropriate feature composition. The capabilities of the proposed framework are demonstrated with an application: recognition of events such as hand gestures, activities

Abstract

Factorization methods are used extensively in computer vision for a wide variety of tasks. Existing factorization techniques extract factors that meet requirements such as compact representation, interpretability, efficiency, dimensionality reduction etc. However, when the extracted factors lack interpretability and are large in number, identification of factors that cause the data to exhibit certain properties of interest is useful in solving a variety of problems. Identification of such factors or factor selection has interesting applications in data synthesis and recognition. In this paper simple and efficient methods are proposed, for identification of factors of interest from a pool of factors obtained by decomposing videos represented as tensors into their constituent low rank factors. The method is used to select factors that enable appearance based facial expression transfer and facial expression recognition. Experimental results demonstrate that the factor selection facilitates efficient solutions to these problems with promising results.

Abstract

This paper presents a system for retrieval of relevant documents from large document image collections. We achieve effective search and retrieval from a large collection of printed document images by matching image features at word-level. For representations of the words, profile-based and shape-based features are employed. A novel DTWbased partial matching scheme is employed to take care of morphologically variant words. This is useful for grouping together similar words during the indexing process. The system supports cross-lingual search using OM-Trans transliteration and a dictionary-based approach. Systemlevel issues for retrieval (eg. scalability, effective delivery etc.) are addressed in this paper.

Abstract

This paper describes the challenges for document image analysis community for building large digital libraries with diverse document categories. The challenges are identified from the experience of the on-going activities toward digitizing and archiving one million books. Smooth workflow has been established for archiving large quantity of books, with the help of efficient image processing algorithms. However, much more research is needed to address the challenges arising out of the diversity of the content in digital libraries.

Abstract

. This paper presents a novel approach for designing a semi-automatic adaptive OCR for large document image collections in digital libraries. We describe an interactive system for continuous improvement of the results of the OCR. In this paper a semi-automatic and adaptive system is implemented. Applicability of our design for the recognition of Indian Languages is demonstrated. Recognition errors are used to train the OCR again so that it adapts and learns for improving its accuracy. Limited human intervention is allowed for evaluating the output of the system and take corrective actions during the recognition process.

Abstract

Homography estimation is an important step in many computer vision algorithms. Most existing algorithms estimate the homography from point or line correspondences which are difficult to reliably obtain in many real-life situations. In this paper we propose a technique based on correspondences of contours. Homography estimation is carried out in Fourier domain. Starting from an affine estimate, the proposed algorithm computes the projective homography in an iterative manner. This technique does not require explicit point to point correspondences; in fact such point correspondences are a by-product of the proposed algorithm. Experimental results and applications validate the use of our technique.

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

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

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.