DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Quantification of signaling networks

Studies in living system in the past several decades have generated qualitative understanding of the molecular interactions resulting in large networks. These networks were essentially deciphered by breaking the components of a cell through a reductionist approach. Biological networks comprising of interactions between genes, proteins and metabolites co-ordinate in the regulation of cellular processes. However, understanding the cellular function also requires quantitative information including network dynamics, which results due to an inherent design principle embedded in the network. Interactions within the network are well organized to form a definite regulatory structure, which in turn exhibits different emergent properties. The property of the network helps the cell to achieve the desired phenotypic state in a controlled manner. The dynamics of the network or the relationship between network structure and cellular behavior cannot be understood intuitively from the interaction map of the network. Computational methods can now be employed to study these networks at system level. The field of systems biology looks at integrating the interaction maps obtained through molecular biological approach. Various studies at the system level have been reported for pathways namely chemotactic response in bacteria, cell cycle and osmotic signaling in yeast, growth factor stimulated signaling pathways in mammals. This review focuses on understanding signaling networks with the help of mathematical models

Uterine rupture at the fundus during pregnancy: a case report

Rupture of a gravid uterus is a surgical emergency. Predisposing factors include a scarred uterus. Spontaneous rupture of an unscarred uterus during pregnancy is a rare occurrence. We hereby present the case of a spontaneous complete uterine fundal rupture at Karnataka Institute of Medical Sciences, Hubli, Karnataka, India. The patient had a history of septal resection. Spontaneous uterine rupture occurs when there is an upper segment uterine scar. This case report shows that past history of septal resection is a risk factor for the presence of uterine scar

Landmark based corridor discrimination

For a mobile robot to perform some sort of useful function it usually must have some sort of global understanding of its environment. This is usually expressed in the form of map. Through real-world experiments, using a mobile robot inspired by insect visual guidance, we present results showing the performance of a mobile robot in recognising a previously encountered corridor environment and discriminating between various corridors. This is achieved through the building and refinement of maps based on the observation of simple landmarks en route.<br /

Insect inspired behaviours for the autonomous control of mobile robots

Animals navigate through various uncontrolled environments with seemingly little effort. Flying insects, especially, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments. Through both simulation and real-world experiments, we demonstrate the feasibility of equipping a mobile robot with the ability to navigate a corridor environment, in real time, using principles based on insect-based visual guidance. In particular we have used the bees&rsquo; navigational strategy of measuring object range in terms of image velocity. We have also shown the viability and usefulness of various other insect behaviours: (i) keeping walls equidistant, (ii) slowing down when approaching an object, (iii) regulating speed according to tunnel width, and (iv) using visual motion as a measure of distance travelled.<br /

Lipid profile analysis of type 2 diabetic patients in Bengaluru population, India

Background: Prevalence of Diabetes in India is 69.2 million, among which type 2 diabetes mellitus (T2DM), constitutes for 90% of all the diabetic populations. Previous studies have proved the association of T2DM, with increasing risk of cardiovascular diseases (CVDs) and the level of risk varies among males and females. The present study aims to analyze the lipid profile of T2DM patients and compare the lipid profile of T2DM males and females in Karnataka, Bengaluru population.Methods: The study included 171 T2DM patients, 59 females and 112 males aged 21 years and above. Total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), very low density lipoprotein cholesterol (VLDL-C) and triglycerides (TG) concentrations values were analyzed for each group. Paired students t test was applied to identify the differences in lipid profile values of males and females with T2D.Results: The mean value of TC, VLDL-C and LDL-C were higher in overall T2DM patients than the normal range and HDL-C was lower in T2DM patients. Comparison between males and females showed significantly higher LDL-C in females with T2DM than males. Other lipid parameters TC, TG and HDL-C did not show any significant differences between females and males with T2DM.Conclusions: This study demonstrated the existence of dyslipidemia in T2DM population which is major risk factor for CVD. Greater LDL-C was observed in T2DM females compared to T2DM males suggests higher risk for CVD in females compared to males