Abstract

This paper presents a novel approach for retrieval of relevant documents from a large collection of printed document images, where search in corresponding textual content is practically impossible due to the unavailability of robust OCRs. We achieve effective search by matching at wordlevel using image features. Importance of a document for a given query is also computed at image-level.

Abstract

Searching in scanned documents is an important problem in Digital Libraries. If OCRs are not available, the scanned images are inaccessible. In this paper, we demonstrate a searching procedure without an intermediate textual representation. We achieve effective retrieval from document databases by matching at word-level using image features. Word profiles, structural features and transform domain representations are employed for characterising the word images. A novel partial matching approach based on dynamic time warping (DTW) is proposed to take care of word form variations. With the new partial matching procedure, morphologically variant words become similar in image space. This is specially useful for grouping together similar words for indexing purpose. We extend our formulation for cross-lingual search with the help of transliteration.

Abstract

Image Based Rendering holds a lot of promise for navigating through a real world scene without modeling it manually. Different representations have been proposed for IBR in the literature. In this paper, we argue that a representation using depth maps and texture images from a number of viewpoints is a rich and viable representation for IBR. We discuss different aspects of this representation including capture, representation, compression and rendering. We show several results to show how the representation can be used to model and render complex scenes.

Abstract

Graphics models for virtual environments are increasingly getting massive, requiring large amounts of memory to store and high end graphics capabilities to render. It may not be possible or desirable to store the entire model in the station where it will be rendered, especially for environments that change. Such virtual models could be stored on a server and streamed to remote clients on demand. In this paper, we present the design and implementation of a client-server system to render large virtual environments. Our system can provide the best visualization quality for a wide range in connection bandwidth and latency and the rendering capacity of the client. The system uses a visibility-based geometry representation so that little invisible geometry is sent to the client. Incremental updating and client-side prediction of user motion optimize the communication requiremnts for a given client. We present results of the study conducted on a representative range of the relevant parameters in this paper

Abstract

Large virtual environments arise in many applications and may be used by many users simultaneously for walkthroughs. It is not feasible to store a copy of the whole environment at each of the client locations due to their size. The dynamic nature of the environment also makes it more convenient for a central server to ensure consistency across different users. In this paper, we present a client-server based system for visualizing large environments from remote users. The client gets only the portion of the geometry necessary for navigating around the current user location. The system can adapt to different client parameters such as graphics capability, network bandwidth, communication latency, etc. We present the design and implementation of the system in this paper and produce relevant results using it

Abstract

The size and detail of graphics environments in everyday use has gone up considerably recently. Still, most applications use locally resident geometric content for rendering. In this paper, we present a system to stream large graphics environments from a central server to multiple number of clients. The streaming is transparent to the user who can treat remote models just like local ones. The streaming system automatically adapts to the rendering capabilities, network bandwidth and latency of the client and transmits an optimized model. We present the design of the streaming system and give results of streaming a large model using it

Abstract

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Abstract

Most natural spatiotemporal systems are an organized ensemble of dynamical subsystems whose behaviour is regulated by nonlinearly coupled multi-variable processes. The response of each of these variables and/or combinations of them to any single or composite external perturbation can influence its spatiotemporal behaviour in a complex and non-intuitive manner. This paper attempts to study the dynamical response of two coupled map lattice systems having local dynamics described by (a) coupled discrete maps, and (b) coupled differential equations, to external perturbation (or “pinning”) applied to each variable individually or simultaneously. We show that the response of different variables to external pinning is quite different. Our results indicate that, though this pinning approach is useful in controlling complex dynamics both globally and locally, enhancing complexity in dynamics (“anti-control”) is …

Abstract

In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is to compare their performance against an “Oracle algorithm” that knows in advance the identities of all frequent itemsets in the database and only needs to gather their actual supports to complete the mining process. Our experimental results show that current mining algorithms do not perform uniformly well with respect to the Oracle for all database characteristics and support thresholds. In many cases there is a substantial gap between the Oracle’s performance and that of the current mining algorithms. Second, we present a new mining algorithm, called ARMOR, that is constructed by making minimal changes to the Oracle algorithm. ARMOR consistently performs within a factor of two of the Oracle on both real and synthetic datasets over practical ranges of support specifications.

Abstract

The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning “uninteresting” rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, namely, the generalized closed (or
-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating g-closed itemsets provides significant performance improvements on a variety of databases.