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The standard graph cut technique is a robust method for globally optimal image segmentation. However, because of its global nature, it is prone to capture outlying areas similar to the object of interest. This paper proposes a novel method to constrain the standard graph cut technique for tracking objects in a region of interest. By introducing an(More)
The purpose of this paper is to describe certain alternative metrics for quantifying distances between distributions, and to explain their use and relevance in visual tracking. Besides the theoretical interest, such metrics may be used to design filters for image segmentation, that is for solving the key visual task of separating an object from the(More)
We propose a technique to simultaneously estimate the local fiber orientations and perform multi-fiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a(More)
We describe a technique that uses tractography to drive the local fiber model estimation. Existing techniques use independent estimation at each voxel so there is no running knowledge of confidence in the estimated model fit. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those(More)
This paper proposes a novel method to apply the standard graph cut technique to segmenting multimodal tensor valued images. The Riemannian nature of the tensor space is explicitly taken into account by first mapping the data to a Euclidean space where non-parametric kernel density estimates of the regional distributions may be calculated from user(More)
The standard graph cut technique is a robust method for globally optimal image segmentation Because of its global nature, it is prone to capture outlying areas similar to the object of interest. The standard graph cut technique is a robust method for globally optimal image segmentation Because of its global nature, it is prone to capture outlying areas(More)
Graph cut image segmentation with intensity information alone is prone to fail for objects with weak edges, in clutter, or under occlu-sion. Existing methods to incorporate shape are often too restrictive for highly varied shapes, use a single fixed shape which may be prone to misalignment, or are computationally intensive. In this note we show how highly(More)
We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the(More)
We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step(More)
Recent advances in diffusion weighted MR imaging (dMRI) has made it a tool of choice for investigating white matter abnormalities of the brain and central nervous system. In this work, we design a system that detects abnormal features (biomarkers) of first-episode schizophrenia patients and then classifies them using these features. We use two different(More)