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Image matting deals with the estimation of the alpha matte at each pixel, i.e., the contribution of the foreground and background objects to the composition of the image at that pixel. Existing methods for image matting are typically limited to estimating the alpha mattes for two image layers only. However, in several applications one is interested in editing images with multiple objects. In this...
The inclusion of the free-running Purkinje network in computational simulations provides a significant insight into understanding the mechanisms of cardiac pathophysiologies. However, its automatic extraction is challenging due to the presence of abundant local complexities. We thereby introduce a novel algorithm to track the Purkinje fibers in high resolution magnetic resonance (MR) images. Our formulation...
Interactive image segmentation traditionally involves the use of algorithms such as graph cuts or random walker. Common concerns with using graph cuts are metrication artifacts (blockiness) and the shrinking bias (bias towards shorter boundaries). The random walker avoids these problems, but suffers from the proximity bias (sensitivity to location of pixels labeled by the user). In this work, we introduce...
We propose a scheme to introduce directionality in the random walker algorithm for image segmentation. In particular, we extend the optimization framework of this algorithm to combinatorial graphs with directed edges. Our scheme is interactive and requires the user to label a few pixels that are representative of a foreground object and of the background. These labeled pixels are used to learn intensity...
Image matting deals with finding the probability that each pixel in an image belongs to a user specified dasiaobjectpsila or to the remaining dasiabackgroundpsila. Most existing methods estimate the mattes for two groups only. Moreover, most of these methods estimate the mattes with a particular bias towards the object and hence the resulting mattes do not sum up to 1 across the different groups....
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