Abstract

The algorithm presented in this paper is designed to be used in automated multi-sensor surveillance systems which require observation of targets in a bounded area to optimize the performance of the system. There have been many approaches which deal with multi-sensor tracking and observation, but there haven't been many which deal purely with targets detections i.e. each target needs to only be detected once. The metric used to gauge the performance of the system is percentage of targets detected among those that enter the area. Targets enter the area through source points on the side of the area according to Poisson distribution, the rate of entry is constant for all sources. The algorithm presented here uses target arrival information, sensor positions to generate an optimal motion strategy for the multi-sensor system every T time-steps i.e. every T time-steps, the probability of finding undetected targets is estimated, the optimal sensor paths for the next T time-steps are calculated. The algorithm performs robustly and optimally detecting around 80% of the targets that enter the area

Abstract

This paper studies the problem of traversing a rough terrain by wheeled vehicles. Here rough terrain implies terrain which is geometrically not ideal. The criterion for mobility of a wheeled vehicle in any terrain is formally developed, providing insights into the mechanical structure requirements of the vehicle. A vehicle structure with an actively articulated suspension is found as a solution to improved rough terrain mobility. The contact forces of the vehicle with the surface being traversed are identified as the critical factor in determining the traversability of the surface. Hence a control strategy involving the control of the contact forces (normal and traction) is proposed. The key feature of the locomotion strategy, thus developed, is that it provides a solution involving dynamics of the main body for improving mobility in rough terrain.

Abstract

Depth Images are viable representations that can be computed from the real world using cameras and/or other scanning devices. The depth map provides 2-kD structure of the scene. A set of Depth Images can provide hole-free rendering of the scene. Multiple views need to blended to provide smooth hole-free rendering, however. Such a representation of the scene is bulky and needs good algorithms for real-time rendering and efficient representation. In this paper, we present a discussion on the Depth Image representation and provide a GPU-based algorithm that can render large models represented using DIs in real time. We then present a proxy-based compression scheme for Depth Images and provide results for the same. Results are shown on synthetic scenes under different conditions and on some scenes generated from images. Lastly, we initiate discussion on varying quality levels in IBR and show a way to create representations using DIs with different trade-offs between model size and rendering quality. This enables the use of this representation for a variety of rendering situations.

Abstract

Recording dynamic events, such as a sports event,a ballet performance, or a lecture, digitally for ex-periencing in a spatiotemporally distant setting re-quires 4D capture: three dimensions for their geom-etry/appearance over the fourth dimension of time.Cameras are suitable for this task as they are non-intrusive, universal, and inexpensive. Computer Vi-sion techniques have advanced sufficiently to make the4D capture possible. In this paper, we present a his-torical perspective on the Virtualized RealityTMsys-tem developed since early 90s to early 2000 at CMUfor the 4D capture of dynamic events

Abstract

Terrains and other geometric models have been traditionally storedlocally. Their remote access presents the characteristics that are acombination of data serving such as files and real-time streaminglike audio-visual media. In this sketch we describe a client-serversystem to serve and stream large terrains to heterogenous clients.This process is sensitive to both the client’s capabilities as well asthe available network bandwidth. Level of Detail and view predic-tion are used to alleviate the effects of changing latency and band-width. We discuss the design of a terrain streaming system andpresent preliminary results

Abstract

Digitized books and manuscripts in digital libraries are often stored as images or graphics. They are not searchable at the content level due to the lack of OCRs or poor quality of the scanned images. Portable Document Format (PDF) has emerged as the most popular document representation schema for wider access across platforms. When there is no textual (UNICODE, ASCII) representation available, scanned images are stored in the graphics stream of PDF. In this paper, we propose a novel solution to search the textual data in graphics stream of the PDF files at content level. The proposed solution is demonstrated by enhancing an open source PDF viewer (Xpdf). Indian language support is also provided. Users can type a word in Roman (ITRANS), view it in a font, and search in textual and graphics stream of PDF documents simultaneously.

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

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