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.

Context-Aware Reasoning Framework for Multi-user Recommendations in Smart Home

Abstract: This paper introduces a context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment. User’s activity and mobility plays a crucial role in defining various contexts in and around the home. The observation data acquired from disparate sensors, called user’s context, is interpreted semantically to implicitly disambiguate the users that are being recommended to. The recommendations are provided based on the relationship that exist among multiple users and the decision is made as per the preference or priority. The proposed approach makes extensive use of multimedia ontology in the life cycle of situation recognition to explicitly model and represent user’s context in smart home. Further, dynamic reasoning is exploited to facilitate context-aware situation tracking and intelligently recommending appropriate actions which suit the situation. We illustrate use of the proposed framework for Smart Home use-case.

A Query Based Learning Algorithm for Modelling Robot Manipulator Dynamics using Network Inversion

Abstract: A query based learning algorithm is proposed in this paper to obtain a valid neural emulator of the robot manipulator using radial basis function networks. This algorithm is centred around the conc...

Behavioral Study of Defocus Cue Preserving Image Compression Algorithm for Depth Perception

Abstract: Image and video processing is currently active research field during the past few years. Different coding schemes are available in the literature for image and video compression to improve compression ratio while maintaining picture quality. Many of the algorithms use ROI coding such as saliency based concept using different image features. But very few works related to depth cues preserving compression. In this paper, we present a behavioral study to state that the images compressed using defocus cue preserving compression yields better depth perception as compared to standard JPEG compression. We compare images compressed using different schemes against the original image. We collect data from different participants by showing original and compressed images to them. The responses are analyzed using analysis of variance. The analysis shows that the images compressed using defocus cue based compression provides the better perception of the raw image as compared to standard JPEG compressed image.

A Framework for Image Interpretation in a Distributed Problem Solving Environment

Abstract: This paper presents a framework for constructing image interpretation in a distributed problem solving environment. The interpretation of an image is constructed using abductive inferencing scheme. The inferencing mechanism ensures generation of interpretations of the image which can account for the features detected in the image in the most consistent manner. Two different decomposition strategies have been discussed for identifying the eubproblems. Strategies have been formulated for solving the subproblems and integrating the partial results in a parallel and distributed fashion. Correctness and validity of the strategies have been proved. A black board model based distributed architecture have been shown to satisfy the requirements of the problem solving technique.

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.