Santanu Chaudhury

Bio: Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Image segmentation. The author has an hindex of 28, co-authored 380 publications receiving 3691 citations. Previous affiliations of Santanu Chaudhury include Central Electronics Engineering Research Institute & Indian Institute of Technology Delhi.

MOWL: An ontology representation language for web-based multimedia applications

Abstract: Several multimedia applications need to reason with concepts and their media properties in specific domain contexts. Media properties of concepts exhibit some unique characteristics that cannot be dealt with conceptual modeling schemes followed in the existing ontology representation and reasoning schemes. We have proposed a new perceptual modeling technique for reasoning with media properties observed in multimedia instances and the latent concepts. Our knowledge representation scheme uses a causal model of the world where concepts manifest in media properties with uncertainties. We introduce a probabilistic reasoning scheme for belief propagation across domain concepts through observation of media properties. In order to support the perceptual modeling and reasoning paradigm, we propose a new ontology language, Multimedia Web Ontology Language (MOWL). Our primary contribution in this article is to establish the need for the new ontology language and to introduce the semantics of its novel language constructs. We establish the generality of our approach with two disperate knowledge-intensive applications involving reasoning with media properties of concepts.

Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion

Abstract: Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region’s signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter’s outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.

Experiences of integration and performance testing of multilingual OCR for printed Indian scripts

Abstract: This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.

Semantic clustering-based cross-domain recommendation

Abstract: Cross-domain recommendation systems exploit tags, textual descriptions or ratings available for items in one domain to recommend items in multiple domains. Handling unstructured/ unannotated item information is, however, a challenge. Topic modeling offer a popular method for deducing structure in such data corpora. In this paper, we introduce the concept of a common latent semantic space, spanning multiple domains, using topic modeling of semantic clustered vocabularies of distinct domains. The intuition here is to use explicitly-determined semantic relationships between non-identical, but possibly semantically equivalent, words in multiple domain vocabularies, in order to capture relationships across information obtained in distinct domains. The popular WordNet based ontology is used to measure semantic relatedness between textual words. The experimental results shows that there is a marked improvement in the precision of predicting user preferences for items in one domain when given the preferences in another domain.

Genetic algorithm for node partitioning problem and applications in VLSI design

Abstract: The group of classical graph-theoretic problems, including graph colouring, clique cover, and maximal clique, may be viewed as instances of a general node partitioning problem (NPP). A wide variety of real life problems can be modelled as instances of NPP. Finding an optimal partition for the NPP is said to be NP-complete. In this work a stochastic search by a genetic algorithm (GA) is employed to find a near optimal solution for the NPP. The critical aspects of the GA for NPP, such as the solution representation by elegant data structure, together with genetic operations and selection policies employed in the GA procedure, are also presented. The proposed GA does not require the number of disjunct node sets to be given a priori. Three application problems is VLSI design are solved as instances of NPP. The experimental results presented in each case of these application problems bring out the efficacy of genetic algorithms.

Data clustering: a review

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Online and off-line handwriting recognition: a comprehensive survey

Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.