Rituparna Sarkar

Rituparna Sarkar
Institut Pasteur · Bioimage analysis unit

PhD

About

29
Publications
1,754
Reads
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142
Citations
Citations since 2016
22 Research Items
138 Citations
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Introduction
My research interests include biological image and data analysis with specific application to segmentation, classification and tracking

Publications

Publications (29)
Article
Full-text available
The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight of this work is the manner in which the model’s hyperparameters are estimated. The detriments of ad-hoc parameter estimation are well known, but this issue remains largely unadd...
Preprint
Full-text available
This paper develops a generative statistical model for representing, modeling, and comparing the morphological evolution of biological cells undergoing motility. It uses the elastic shape analysis to separate cell kinematics (overall location, rotation, speed, etc.) from its morphology and represents morphological changes using transported square-r...
Chapter
With an expanding market of mobile devices consisting of dual camera configuration, depth estimation has become a core technology for various camera applications. However, due to the constraints imposed by camera configurations and relatively lower computational capabilities of mobile devices, state of the art methods, do not fare well on embedded...
Preprint
Full-text available
Detecting and segmenting individual cells from microscopy images is critical to various life science applications. Traditional cell segmentation tools are often ill-suited for applications in brightfield microscopy due to poor contrast and intensity heterogeneity, and only a small subset are applicable to segment cells in a cluster. In this regard,...
Preprint
Full-text available
Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which results in significant loss o...
Article
Full-text available
In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classif...
Thesis
Full-text available
Sparse representation based dictionary learning has been exploited in solving various image analysis problems - image classification, tracking, quality assessment, de-noising, image reconstruction. The objective of dictionary learning is to obtain an adaptive basis function from the data and simultaneously provide a compact representation. In this...
Conference Paper
The problem of detecting rare and unusual events in video is critical to the analysis of large video datasets. Such events are identified as those occurrences within a sequence that cause a significant change in the scene. We propose to determine the significance of a frame, while preserving its compact representation, by introducing a saliency-dri...
Article
We propose a novel region based segmentation technique using dictionary learning. In a previous work we have developed a method which uses a set of pre-specified Legendre basis functions to perform region based segmentation of an object in presence of heterogeneous illumination. We hypothesize that in problems where a set of training images for the...
Conference Paper
Recent studies have indicated the efficacy of selecting and combining the salient features from a pool of feature types in image retrieval and classification applications. In contrast to previous work, in this paper, we approach this problem as a selection and combination of the salient feature type(s) from a pool of feature types rather than selec...
Conference Paper
With increasing complexity of the dataset it becomes impractical to use a single feature to characterize all constituent images. In this paper we describe a method that will automatically select the appropriate image features that are relevant and efficacious for classification, without requiring modifications to the feature extracting methods or t...
Conference Paper
Full-text available
Study of the sunflower movement may reveal clues regarding unknown mechanisms that regulate periodicity and spatial complexity in plant growth and development. In this paper, we introduce an automated process to track circumnutation of sunflower seedlings. The objective is to track the leaves of the sunflower plant in a video captured by an overhea...
Conference Paper
Full-text available
In this paper we propose a novel compressed sensing based Fourier shape descriptor method to compute the shape feature vector of an arbitrary object. First, the object contour obtained via segmentation is represented as a complex-valued signal. We then formulate an optimization problem that exploits the sparsity of the shape feature of the contour....
Conference Paper
In this work, we develop algorithms for tracking time sequences of sparse spatial signals with slowly changing sparsity patterns, and other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. A key example of the above problem occurs in tracking moving objects across spatially varying illumination c...
Article
Full-text available
We study the problem of tracking (causally estimating) a time sequence of sparse spatial signals with changing sparsity patterns, as well as other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. In many applications, particularly those in visual tracking, the unknown state can be split into a sm...

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