Abstract

Computing correspondences between pairs of images is fundamental to all structures from motion algorithms. Correlation is a popular method to estimate similarity between patches of images. In the standard formulation, the correlation function uses only one feature such as the gray level values of a small neighbourhood. Research has shown that different features—such as colour, edge strength, corners, texture measures—work better under di0erent conditions. We propose a framework of generalized correlation that can compute a real valued similarity measure using a feature vector whose components can be dissimilar. The framework can combine the e0ects of di0erent image features, such as multi-spectral features, edges, corners, texture measures, etc., into a single similarity measure in a 3exible manner. Additionally, it can combine results of di0erent window sizes used for correlation with proper weighting for each. Relative importances of the features can be estimated from the image itself for accurate correspondence. In this paper, we present the framework of generalised correlation, provide a few examples demonstrating its power, as well as discuss the implementation issues. ? 2002 Published by Elsevier Science Ltd on behalf of Pattern Recognition Society.

Abstract

Multiview studies in Computer Vision have concentrated on the constraints satisfied by individual primitives such as points and lines. Not much attention has been paid to the properties of a collection of primitives in multiple views, which could be studied in the spatial domain or in an appropriate transform domain. We derive an algebraic constraint for planar shape recognition across multiple views based on the rank of a matrix of Fourier domain descriptor coefficients of the shape in different views. We also show how correspondence between points on the boundary can be computed for matching shapes using the phase of a measure for recognition.

Abstract

Adoptable, high performance, large scale ontologies that can be extended to support multimedia play a crucial role in building effective content and knowledge management systems and applications. In the context of developing a unified taxonomy and ontology network (UTON), we have undertaken the task of developing a technology framework for building large scale ontologies. This paper describes the architecture of UTON and how general purpose resources such as WordNet and open directory project can be used to create large scale ontology networks and how application specific taxonomies or ontologies can be derived from these general purpose tools and resources.

Abstract

In this paper we propose an approach to extract community structures in the Web by considering a community structure as a group of content creators that manifests itself as a set of interlinked pages. We abstract the community structure as a dense bipartite graph (DBG) over a group of Web pages and proposed an algorithm to extract the DBGs from the given data set. Also, a high-level community is abstracted as a DBG over a set of a low-level communities. Using the proposed approach, a community hierarchy can be constructed for the given data set that generalizes a large number of low-level communities into a few highlevel communities. Using the proposed approach, we have extracted a a three-level community hierarchy from 10 GB TPEC (Text PEtrieval Conference) data set. We believe that the extracted community hierarchy facilitates easy analysis of the low-level communities, helps in reorganizing the Web sites, and provides a way to understand the sociology of the Web

Abstract

Indian farming community is facing a multitude of problems to maximize cropproductivity. In spite of successful research on new agricultural practices concerning cropcultivation, the majority of farmers is not getting upper-bound yield due to several reasons. Oneof the reasons is that expert/scientific advice regarding crop cultivation is not reaching farmingcommunity in a timely manner. It is true that India possesses a valuable agricultural knowledgeand expertise. However, a wide information gap exists between the research level and practice.Indian farmers need timely expert advice to make them more productive and competitive. In thispaper, we made an effort to present a solution to bridge the information gap by exploitingadvances in Information Technology (IT). We propose a framework of a cost-effectiveagricultural information dissemination system (AgrIDS) to disseminate expert agricultureknowledge to the farming community to improve the crop productivity. Some of the crucialbenefits of AgrIDS are as follows. It is a scalable system which can be incrementally developedand extended to cover all the farmers (crops) of India in a cost effective manner. It enables thefarmer to cultivate a crop with expertise, as that of an agricultural expert, by disseminating bothcrop and location specific expert advice in a personalized and timely manner. With AgrIDS, thelag period between research effort to practice can be reduced significantly. Finally, the proposedsystem assumes a great importance due to the trend of globalization, as it aims to provide expertadvice which is crucial to for the Indian farmer to harvest different kinds of crop varietiesbased on the demand in the world market.

Abstract

In E-commerce sites, recommendation systems are used torecommend products to their customers. Collaborative filtering (CF) iswidely employed approach to recommend products. In the literature,researchers are making efforts to improve the scalability and onlineperformance of CF. In this paper we propose a graph based approach toimprove the performance of CF. We abstract a neighborhood communityof a given customer through dense bipartite graph (DBG). Given adata set of customer preferences, a group of neighborhood customersfor a given customer is extracted by extracting corresponding DBG.The experimental results on the MovieLens data set show that therecommendation made with the proposed approach matches closelywith the recommendation of CF. The proposed approach possesses apotential to adopt to frequent changes in the product preference data set.

Abstract

Watermarking is being used in a wide variety of applications. Steganography, copyright protection, owner identification etc are some of them. But watermarking can also be used as means to store other kind of useful information in the image. This work discusses the advantages of putting such information into the image. A watermarking algorithm suitable for embedding large amount of information in the image, robust of jpeg compression is also presented.

Abstract

Plenty of information is available today from different sources about a particular geographical area. However, none of it is reliable when different needs arise like for tourists, city planners, administrators, and lay people. We have designed a multimedia based city information system which attempts to solve this problem. In this paper we explain the design and implementation of our system. The paper discusses in detail the need for such a system, how it compares with similar existing systems and the features expected in such a system. It also discusses our implementation of the system and the future work we want to pursue in this direction

Abstract

Image databases index in them using features extracted from the image. The indexing scheme is decided apriori and is optimized for a specific querying criterion. This is not suitable for a generic database of images which may be queried by multiple users based on different criteria. IN this paper, we present a flexible scheme which adapts itself to the user 's preferences. Though the method uses a conservative set of features during indexing that includes a large number and type of fundamental features the query processing time does not increase due to this redundancy. A boot-strapping mechanism allows the user to build up a "desired class" from a few samples. The feature selection computation scales linearly with the size of the desired class, rather than that of the entire database. THis feature makes our algorithm viable for very large databases. We present an implementation of our scheme and some results from it.

Abstract

Pixel correspondence is an important problem in stereo vision, motion, structure from motion, etc. Several procedures have been proposed in the literature for this problem, using a variety of image features to identify the corresponding features. Dierent features work wel l under dierent conditions. An algorithm that can seamlessly integrate multiple features in a flexible manner can combine the advantages of each. We propose a framework to combine heterogenous features, each with a dierent measure of importance, into a single correspondence computation in this paper. We also present an unsupervised procedure to select the optimal combination of features for a given pair of images by computing the relative importances of each feature. A unique aspect of our framework is that it is independent of the specic correspondence algorithm used. Optimal feature selection can be done using any correspondence mechanism that can be extended to use multiple features. We also present a few examples that demonstrate the eectiveness of the feature selection framework.