Sundaresh Ram

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The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to(More)
Recently, patch-based sparse representation has been used as a statistical image modeling technique for various image restoration applications, due to its ability to model well the natural image patches and automatically discover interpretable visual patterns. Standard sparse representation however does not consider the intrinsic and geometric structure(More)
Accurate segmentation of 3-D cell nuclei in microscopy images is an essential task in many biological studies. Traditional image segmentation methods are challenged by the complexity and variability of microscope images, so there is a need to improve segmentation accuracy and reliability, as well as the level of automation. In this paper we present a novel(More)
Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding(More)
Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an(More)
This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well(More)
We propose a new graph-based approach for performing a multilabel, interactive image segmentation using the principle of random walks. Using the random walk principle, given a set of user-defined (or prelabeled) pixels as labels, one can analytically calculate the probability of walking from each unlabeled pixel to each labeled pixel, thereby defining a(More)
In this paper, we propose an automated method to segment and classify the 3-D spots in fluorescence in-situ hybridization images from ovarian germline nurse cells of Drosophila melanogaster. The spot segmentation consists of a smoothing step followed by top-hat filtering and 3-D region growing. After the spots are segmented, a number of features such as(More)
Detection and segmentation of rocks is an important first task in many applications such as geological analysis, planetary science and mining processes. Rocks are usually segmented using a variety of features such as texture, shading, shape and edges. It is easier to compute these features for rock superpixels rather than every pixel in the image. A(More)
Accurate detection of individual cell nuclei in microscopy images is an essential and fundamental task for many biological studies. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei by visual inspection is time consuming, and prone to induce subjective(More)