Particle filtered modified compressed sensing and applications in visual tracking

The main idea of the thesis is to design an efficient tracking algorithm that is able to track moving objects in presence of spatial illumination variation. The state vectors constitute of the motion parameters and the illumination vectors. The illumination vector is designed as a sparse vector using the fact that the scene parameters (e.g. illumination) at any given instant, can have a sparse representation with respect to the basis i.e. only a few basis elements will contribute to the scene dynamics at each instant. The observation is the entire image frame.The non-linearity and the multimodality of the state-space necessitates the use of Particle Filter. The illumination vector along with motion makes the state-space large dimensional thus making the implementation of regular particle filter expensive. PF-MT has been designed to tackle this problem but it does not utilize the sparsity constraint and hence fails to detect the sparse illumination vector. So we design an algorithm that would use particle filter and importance sample on the motion or the \u27effective space\u27 and the mode tracking step of PF-MT is replaced by the Modified Compressed Sensing for estimating the \u27residual space\u27. Simulation and also experiments with real video demonstrate the advantage of the proposed algorithm over other existing PF based algorithms

Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection

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, we introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. The learning problem is posed in a novel min-max framework which enables adaptive estimation of the hyper-parameters in an automatic fashion. The region and cell boundary predictions are combined via morphological operations and active contour model to segment individual cells. The proposed methodology is particularly suited to segment touching cells from brightfield microscopy images without manual interventions. Quantitatively, we observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8\%$ on average

Particle filtered modified compressed sensing and applications in visual tracking

The main idea of the thesis is to design an efficient tracking algorithm that is able to track moving objects in presence of spatial illumination variation. The state vectors constitute of the motion parameters and the illumination vectors. The illumination vector is designed as a sparse vector using the fact that the scene parameters (e.g. illumination) at any given instant, can have a sparse representation with respect to the basis i.e. only a few basis elements will contribute to the scene dynamics at each instant. The observation is the entire image frame.The non-linearity and the multimodality of the state-space necessitates the use of Particle Filter. The illumination vector along with motion makes the state-space large dimensional thus making the implementation of regular particle filter expensive. PF-MT has been designed to tackle this problem but it does not utilize the sparsity constraint and hence fails to detect the sparse illumination vector. So we design an algorithm that would use particle filter and importance sample on the motion or the 'effective space' and the mode tracking step of PF-MT is replaced by the Modified Compressed Sensing for estimating the 'residual space'. Simulation and also experiments with real video demonstrate the advantage of the proposed algorithm over other existing PF based algorithms.</p

Online) An Open Access

ABSTRACT Chronic toxic effects of non-permitted food colour Metanil Yellow was carried out on the tongue papillae of albino rat (Rattus norvegicus) for exposure periods of 30 and 45 days at a dose of 3.0 g/kg body weight. Topological study displayed the toxic effects of Metanil Yellow on the tongue papillae especially on the filiform and fungiform papillae. The degenerative changes were found in both the filiform and fungiform papillae. Taste buds became necrosed after treatment. All these changes marked the toxicosis of Metanil Yellow on albino rat

The ρ\rho parameter and the CDF W-mass anomaly: observations on the role of scalar triplets

The CDF claim on the W-boson mass, if confirmed, may open up several directions in the quest for physics beyond the standard model. We show that, in general, theoretical models which predict $\rho =1$ at the tree-level are rendered inconsistent with the CDF claims at 4-6 standard deviations if one confines oneself to the existing Z-boson mass, and the earlier $M_W$ value from either the global fit or the ATLAS measurement. However, consistency can still be retained at about $1\sigma$-level if one uses instead the earlier mass from just the {\em LEP2 + Tevatron} measurements. We show further that, in the first two cases, both a scenario including a complex scalar triplet and one with a complex as well as a real triplet can be made consistent with the new data, provided that a small splitting between the complex and the triplet vacuum expectation values is allowed. Finally, we comment briefly on the leptogenesis potential of such scenarios

A Min-Max Based Hyperparameter Estimation For Domain-Adapted Segmentation Of Amoeboid Cells

International audienc

3D cell morphology detection by association for embryo heart morphogenesis

International audienceAdvances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation