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
12 Dec 2010
TL;DR: This paper performs Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec.
Abstract: Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to provide a very compact representation and thus achieves very high compression ratios. However, this is achieved at the cost of higher computation complexity. This disadvantage is now being offset by the availability of high speed processors, thus making it possible to exploit the high compression ratios of the parametric video coding techniques. In this paper a novel idea for efficient parametric representation of video is proposed. We perform Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec. The proposed technique exploits spatial-temporal redundancy and therefore inherently achieves multiframe prediction.
Posted Content
TL;DR: The proposed framework is extended to the registration of CT and US images, which is free from any data specific assumptions and has better generalization capability as compared to the existing rule-based/classical approaches.
Abstract: Image registration is a process of aligning two or more images of same objects using geometric transformation. Most of the existing approaches work on the assumption of location invariance. These approaches require object-centric images to perform matching. Further, in absence of intensity level symmetry between the corresponding points in two images, the learning based registration approaches rely on synthetic deformations, which often fail in real scenarios. To address these issues, a combination of convolutional neural networks (CNNs) to perform the desired registration is developed in this work. The complete objective is divided into three sub-objectives: object localization, segmentation and matching transformation. Object localization step establishes an initial correspondence between the images. A modified version of single shot multi-box detector is used for this purpose. The detected region is cropped to make the images object-centric. Subsequently, the objects are segmented and matched using a spatial transformer network employing thin plate spline deformation. Initial experiments on MNIST and Caltech-101 datasets show that the proposed model is able to produce accurate matching. Quantitative evaluation performed using dice coefficient (DC) and mean intersection over union (mIoU) show that proposed method results in the values of 79% and 66%, respectively for MNIST dataset and the values of 94% and 90%, respectively for Caltech-101 dataset. The proposed framework is extended to the registration of CT and US images, which is free from any data specific assumptions and has better generalization capability as compared to the existing rule-based/classical approaches.
Book ChapterDOI
19 Dec 2016
TL;DR: A tissue selective total variation regularization approach is proposed for the enhancement of cardiac ultrasound images that measures the pixel probability of belonging to blood regions and uses it in the total variation framework to remove the unwanted speckle from the blood chamber regions and preserve the usefulSpeckle in the tissue regions.
Abstract: Speckle reduction is desired to improve the quality of ultrasound images. However, a uniform speckle reduction from the entire image results in loss of important information, especially in cardiac ultrasound images. In this paper, a tissue selective total variation regularization approach is proposed for the enhancement of cardiac ultrasound images. It measures the pixel probability of belonging to blood regions and uses it in the total variation framework. As a result, the unwanted speckle from the blood chamber regions is removed and the useful speckle in the tissue regions is preserved. This helps to improve the visible contrast of the images and enhances the structural details. The proposed approach is evaluated using synthetic as well as real images. A better performance is observed as compared to the state-of-the-art filters in terms of speckle region’s signal to noise ratio, structural similarity measure index, figure of merit, and mean square error.
Proceedings ArticleDOI
08 Dec 2022
TL;DR: Zhang et al. as discussed by the authors proposed a novel attention based framework that combines the strength of feature attention, topological loss and residual learning for root segmentation, which reached state-of-the-art performance on Arabidopsis Root Segmentation Challenge 2021 dataset from Computer Vision in Plant Phenotyping and Agriculture.
Abstract: Root morphological traits are key to monitoring plant growth and development. Traditionally, plant biologists relied on manual or semi-automatic approaches to accurately estimate these traits. With high-throughput acquisition of root image data, the computation of these root traits is currently achieved with automatic image analysis, and in this context, root segmentation is an important pre-processing step. However, this is a challenging task because of (1) diverse root characteristics i.e orientation, size and shape, (2) complex image background, (3) low contrast and (4) varying degrees of self-occlusion. Deep learning methods proposed for root segmentation have mainly focused on conventional pixel-wise losses. In addition, they neglected the relationship between deep features which is crucial for segmentation of thin root structures in the presence of complex backgrounds such as water droplets and leaves. In this paper, we propose a novel attention based framework that combines the strength of feature attention, topological loss and residual learning for root segmentation. The proposed framework has reached state-of-the-art performance on Arabidopsis Root Segmentation Challenge 2021 dataset from Computer Vision in Plant Phenotyping and Agriculture (CVPPA). An ablation study has also been conducted to evaluate the contribution of each module to the proposed framework.
Proceedings ArticleDOI
18 Dec 2018
TL;DR: Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed and it can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.
Abstract: Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. This hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.

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