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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
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Proceedings ArticleDOI
18 Dec 2006
TL;DR: This work presents a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media and proposes the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty.
Abstract: To enable seamless integration of video information on the semantic web, we require that the knowledge of a video domain be formally specified in an ontology. We present a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media. We use the ontology specified knowledge for recognizing concepts relevant to a video scene by making observations for the media properties of concepts as well as making inferences from other ontological concept definitions and relations. For this purpose we introduce new language constructs to OWL (Web Ontology Language), which are used to specify the inherently uncertain nature of media observations. The new constructs also allow additional semantics concerned with the association of media properties with concepts. We propose the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty. The video is annotated with the relevant concepts defined in the ontology. These conceptual annotations are used to create hyperlinks in the video collection.

14 citations

Journal ArticleDOI
TL;DR: A new heuristic search based approach for recognition of partially obscured planar shapes using an admissible heuristic function which is not dependent upon the features actually used for representing the shapes.

14 citations

Journal ArticleDOI
TL;DR: A new hybrid Kinect-variety-based synthesis scheme that renders artifact-free multiple views for autostereoscopic/automultiscopic displays and provides guarantees on the completeness, optimality with respect to the inter-view consistency of the algorithm is presented.
Abstract: This paper presents a new hybrid Kinect-variety-based synthesis scheme that renders artifact-free multiple views for autostereoscopic/automultiscopic displays. The proposed approach does not explicitly require dense scene depth information for synthesizing novel views from arbitrary viewpoints. Instead, the integrated framework first constructs a consistent minimal image–space parameterization of the underlying 3D scene. The compact representation of scene structure is formed using only implicit sparse depth information of a few reference scene points extracted from raw RGB depth data. The views from arbitrary positions can be inferred by moving the novel camera in parameterized space by enforcing Euclidean constraints on reference scene images under a full-perspective projection model. Unlike the state-of-the-art depth image-based rendering (DIBR) methods, in which input depth map accuracy is crucial for high-quality output, our proposed algorithm does not depend on precise per-pixel geometry information. Therefore, it simply sidesteps to recover and refine the incomplete or noisy depth estimates with advanced filling or upscaling techniques. Our approach performs fairly well in unconstrained indoor/outdoor environments, where the performance of range sensors or dense depth-based algorithms could be seriously affected due to scene complex geometric conditions. We demonstrate that the proposed hybrid scheme provides guarantees on the completeness, optimality with respect to the inter-view consistency of the algorithm. In the experimental validation, we performed a quantitative evaluation as well as subjective assessment of the scene with complex geometric or surface properties. A comparison with the latest representative DIBR methods is additionally performed to demonstrate the superior performance of the proposed scheme.

13 citations

Journal ArticleDOI
TL;DR: The proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges, and the performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes.
Abstract: One of the well-established research domains among computer vision scientists is object tracking. However, not much work has been done in underwater scenarios. This article addresses the problem of visual tracking in the underwater environment with the stationary and nonstationary camera setups. In order to deal with the underwater optical dynamics, a dominant color component-based scene representation is employed in the YCbCr color space. An adaptive approach is devised to select the Walsh–Hadamard (WH) kernels for the efficient extraction of color, edge, and texture strengths, whereas a new feature called range strength is proposed to extract the variation of intensity from underwater sequences in the local neighborhood using the WH kernel. The likelihood of these feature strengths is integrated in a particle filter framework to track the object of interest in underwater sequences. The reference feature strengths used in assigning weights to the particles are updated based on the S $\phi$ rensen distance. The coefficients of feature strengths are calculated in such a way that if one feature fails, then its coefficient become insignificant, whereas the more suitable features get higher feature coefficients. The effectiveness of the proposed scheme is evaluated using the underwater video datasets: reefVid, fish4knowledge (F4K), underwaterchangedetection (UWCD), and National Oceanic and Atmospheric Administration (NOAA). The performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes. The quantitative analysis of the proposed scheme is carried out using three evaluation measures: overall intersection over union , centroid location error , and average tracking error . The performance of the proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges.

13 citations

Journal ArticleDOI
TL;DR: A soft-segmentation visualization scheme to generate pixel partitions from the histogram of MR image data using a connectionist approach and then generate selective visual depictions of pixel partitions using pseudo color based on an appropriate fuzzy membership function is proposed.

13 citations


Cited by
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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