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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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Journal ArticleDOI
TL;DR: An embedded platform based framework for implementing summary generation scheme using HW-SW Co-Design based methodology is proposed and the complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board.
Abstract: In any remote surveillance scenario, smart cameras have to take intelligent decisions to generate summary frames to minimize communication and processing overhead. Video summary generation, in the context of smart camera, is the process of merging the information from multiple frames. A summary generation scheme based on clustering based change detection algorithm has been implemented in our smart camera system for generating frames to deliver requisite information. In this paper we propose an embedded platform based framework for implementing summary generation scheme using HW-SW Co-Design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented in hardware using VHDL. The system is designed using Xilinx Embedded Design Kit (EDK).

2 citations

Proceedings ArticleDOI
25 Aug 2013
TL;DR: A framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples and inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance.
Abstract: Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance.

2 citations

Proceedings ArticleDOI
23 Sep 1999
TL;DR: A generic architecture for content based retrieval of images, which can be extended to the requirements of large distributed and heterogeneous collections, and is modeled as a multi agent system where an autonomous search agent encapsulates independent image retrieval algorithms.
Abstract: We present a generic architecture for content based retrieval of images, which can be extended to the requirements of large distributed and heterogeneous collections. The system is modeled as a multi agent system where an autonomous search agent encapsulates independent image retrieval algorithms. An optimal team of agents is dynamically selected for every retrieval problem. A flexible protocol allows for dynamic addition of search agents incorporating new pattern recognition techniques. These agents are coded as mobile agents, so that they can travel across a wide area network and analyze the documents at their sources. The separation of physical image forms and their logical structural composition allows the search agents to operate over heterogeneous repositories. A prototype implementation validates the effectiveness of the architecture.

2 citations

Book ChapterDOI
17 Dec 2019
TL;DR: This paper presents data-driven sensing for spatial multiplexers trained with combined mean square error (MSE) and perceptual loss using Deep convolutional neural networks and experimentally infer that the encoded information from such spatialmultiplexers can directly be used for action recognition.
Abstract: Tasks such as action recognition requires high quality features for accurate inference. But the use of high resolution and large volume of video data poses a significant challenge for inference in terms of storage and computational complexity. In addition, compressive sensing as a potential solution to the aforementioned problems has been shown to recover signals at higher compression ratios with loss in information. Hence, a framework is required that performs good quality action recognition on compressively sensed data. In this paper, we present data-driven sensing for spatial multiplexers trained with combined mean square error (MSE) and perceptual loss using Deep convolutional neural networks. We employ subpixel convolutional layers with the 2D Convolutional Encoder-Decoder model, that learns the downscaling filters to bring the input from higher dimension to lower dimension in encoder and learns the reverse, i.e. upscaling filters in the decoder. We stack this Encoder with Inflated 3D ConvNet and train the cascaded network with cross-entropy loss for Action recognition. After encoding data and undersampling it by over 100 times (10 \(\times \) 10) from the input size, we obtain 75.05% accuracy on UCF-101 and 50.39% accuracy on HMDB-51 with our proposed architecture setting the baseline for reconstruction free action recognition with data-driven sensing using deep learning. We experimentally infer that the encoded information from such spatial multiplexers can directly be used for action recognition.

2 citations

Proceedings ArticleDOI
18 Sep 2012
TL;DR: An unsupervised model is proposed, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time.
Abstract: In this paper, we address the problem of discovering activities and their temporal significance in an area under surveillance. Discovering activities along with its expectation of occurrence at a particular time plays an important role in many surveillance applications. We propose an unsupervised model, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time. We use adaptive background subtraction to detect spatio-temporal patches, which are used as feature representation for activity patterns. Each of these patches are associated with the time slot in which they occur. Multinomial distributions are used to model both activities as distribution over spatio-temporal patches and time significance as distribution over the time-line. We demonstrate the effectiveness of our approach on a real life surveillance feed of an outdoor scene.

1 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