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 |[[http://www.cse.iitb.ac.in/~andrew/index.html|{{vigil:research:oca150.png|}}]]| ** [[http://www.cse.iitb.ac.in/~andrew/index.html|Hierarchical Normalized Cuts: Unsupervised Segmentation of
-Vascular Biomarkers from Ovarian Cancer Tissue Microarrays]] \\  (Andrew Janowczyk)** \\ Research has shown that tumor vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. |
+Vascular Biomarkers from Ovarian Cancer Tissue Microarrays]] (Andrew Janowczyk)** \\ Research has shown that tumor vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. |
 | [[http://www.cse.iitb.ac.in/~ashwinkp/btp/btp.html|{{vigil:research:sfm.jpg?150|}}]] | ** [[http://www.cse.iitb.ac.in/~ashwinkp/btp/btp.html|Isometry-based Structure from Motion]]([[http://www.cse.iitb.ac.in/~ashwinkp/|K.P.Ashwin]])**:Structure from motion refers to the process of estimating the 3D structure of a moving object using image measurements taken over a period of time and. This has been an active area of research for a lot of years and is generally considered a hard problem to solve. In this work, we propose a novel solution to solving the age-old Structure from motion problem. We review this problem in the setting of Riemannian Geometry, in which shapes are represented by points on the surface of a high dimensional space. We use isometric constraints on the shape to identify the most likely deformation of the model. |
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