scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
Proceedings ArticleDOI
14 Dec 2014
TL;DR: The proposed technique provides a suitable method to separate the text layer from the historic inscription images by considering the problem as blind source separation which aims to calculate the independent components from a linear mixture of source signals, by maximizing a contrast function based on higher order cumulants.
Abstract: In this paper a novel method to address the problem of enhancement and binarization of historic inscription images is presented. Inscription images in general have no distinction between the text layer and background layer due to absence of color difference and possess highly correlated signals and noise. The proposed technique provides a suitable method to separate the text layer from the historic inscription images by considering the problem as blind source separation which aims to calculate the independent components from a linear mixture of source signals, by maximizing a contrast function based on higher order cumulants. Further, the results are compared with existing ICA based techniques like NGFICA and Fast-ICA.

2 citations

Proceedings ArticleDOI
01 Jul 2017
TL;DR: A novel multimedia ontology based framework to recommend garments to the users for a specific occasion to form a complement outfit pair, which requires dealing with intrinsic subjectivity involved in modeling the domain.
Abstract: In this work, we propose a novel multimedia ontology based framework to recommend garments to the users for a specific occasion to form a complement outfit pair, which requires dealing with intrinsic subjectivity involved in modeling the domain. The framework automatically derives personal traits of a user such as body color, dimensions from her photograph and garment attributes such as color, pattern from input garment to interpret their respective context. Depending on these contexts, system then recommends outfits that are complementary and satisfactory in terms of reference garment color and user personality using semantic web and image processing techniques. The recommendation model compiles knowledge of clothing in an ontology encoded in Multimedia Web Ontology Language (MOWL) which is capable of describing domain concepts in terms of their media properties and utilizes probabilistic reasoning scheme to reason with them. We have experimented and validated our approach with clothing liking of various models on a collection of garments downloaded from various websites, thus providing an effective garment recommendation interface to the user.

2 citations

Proceedings ArticleDOI
12 Dec 2010
TL;DR: A novel probabilistic Latent Semantic Analysis based algorithm for pair-wise interaction recognition is proposed and presented as an application of the distributed composite event recognition framework, where the events are interactions between pairs of objects.
Abstract: In this paper, we propose a real-time distributed framework for composite event recognition in a calibrated pan-tilt camera network. A composite event comprises of events that occur simultaneously or sequentially at different locations across time. Distributed composite event recognition requires distributed multi-camera multi-object tracking and distributed multi-camera event recognition. We apply belief propagation to reach a consensus on the global identities of the objects in the pan-tilt camera network and to arrive at a consensus on the event recognized by multiple cameras simultaneously observing it. We propose a hidden Markov model based approach for composite event recognition. We also propose a novel probabilistic Latent Semantic Analysis based algorithm for pair-wise interaction recognition and present an application of our distributed composite event recognition framework, where the events are interactions between pairs of objects.

2 citations

Book ChapterDOI
11 Jul 2017
TL;DR: The proposed filter requires almost no parameter tuning and provides good quality outputs for synthetic as well as real ultrasound images and is compared with the state-of-the-art speckle reducing filters.
Abstract: Despeckling of ultrasound images is essential for subsequent computational analysis. In this paper, an edge aware geometric filter (GF) is proposed for speckle reduction. The behaviour of conventional GF is approximated using commonly used functions like unit step. These approximations help in identifying the natural relationship between GF and other existing spatially adaptive filters. Subsequently, the modifications in GF framework are proposed to take the advantage of edge characteristics. The proposed filter requires almost no parameter tuning and provides good quality outputs for synthetic as well as real ultrasound images. It is compared with the state-of-the-art speckle reducing filters. Improvements of 10.46% and 42% are noticed in mean square error and figure of merit, respectively.

2 citations

Proceedings ArticleDOI
29 Oct 2009
TL;DR: This paper proposes to use handwriting without recognition as a temporal medium of communication in synchronization with other media like audio and video to ensure that the interfaces developed are language independent and provide rich, natural and intuitive interaction.
Abstract: Handwriting has been conventionally used for input by applying handwriting recognition. In this paper we propose to use handwriting without recognition as a temporal medium of communication in synchronization with other media like audio and video. This ensures that the interfaces developed are language independent and provide rich, natural and intuitive interaction. We present multiple applications exploiting this concept.

2 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
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.

14,054 citations

01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
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.

3,627 citations

Journal ArticleDOI
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
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.

2,653 citations

Reference EntryDOI
15 Oct 2004

2,118 citations