Browsing by Author "Farhand, Sepehr"

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    Foreground discovery in streaming videos with dynamic construction of content graphs
    (Elsevier, 2023-01) Farhand, Sepehr; Tsechpenakis, Gavriil; Computer and Information Science, School of Science
    We study the problem of unknown foreground discovery in image streaming scenarios, where no prior information about the dynamic scene is assumed. Contrary to existing co-segmentation principles where the entire dataset is given, in streams new information emerges as content appears and disappears continually. Any object classes to be observed in the scene are unknown, therefore no detection model can be trained for the specific class set. We also assume there is no available repository of trained features from convolutional neural nets, i.e., transfer learning is not applicable. We focus on the progressive discovery of foreground, which may or may not correspond to contextual objects of interest, depending on the camera trajectory, or, in general, the perceived motion. Without any form of supervision, we construct in a bottom up fashion dynamic graphs that capture region saliency and relative topology. Such graphs are continually updated over time, and along with occlusion information, as fundamental property of the foreground–background relationship, foreground is computed for each frame of the stream. We validate our method using indoor and outdoor scenes of varying complexity with respect to content, objects motion, camera trajectory, and occlusions.
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    Probabilistic Multi-Compartment Deformable Model, Application to Cell Segmentation
    (2013-07-12) Farhand, Sepehr; Tsechpenakis, Gavriil; Fang, Shiaofen; Tuceryan, Mihran
    A crucial task in computer vision and biomedical image applications is to represent images in a numerically compact form for understanding, evaluating and/or mining their content. The fundamental step of this task is the segmentation of images into regions, given some homogeneity criteria, prior appearance and/or shape information criteria. Specifically, segmentation of cells in microscopic images is the first step in analyzing many biomedical applications. This thesis is a part of the project entitled "Construction and profiling of biodegradable cardiac patches for the co-delivery of bFGF and G-CSF growth factors" funded by National Institutes of Health (NIH). We present a method that simultaneously segments the population of cells while partitioning the cell regions into cytoplasm and nucleus in order to evaluate the spatial coordination on the image plane, density and orientation of cells. Having static microscopic images, with no edge information of a cytoplasm boundary and no time sequence constraints, traditional cell segmentation methods would not perform well. The proposed method combines deformable models with a probabilistic framework in a simple graphical model such that it would capture the shape, structure and appearance of a cell. The process aims at the simultaneous cell partitioning into nucleus and cytoplasm. We considered the relative topology of the two distinct cell compartments to derive a better segmentation and compensate for the lack of edge information. The framework is applied to static fluorescent microscopy, where the cultured cells are stained with calcein AM.