Journal Article10.1109/TIP.2003.821445
Active contours for tracking distributions
Daniel Freedman,Tao Zhang +1 more
TL;DR: A new approach to tracking using active contours is presented, which aims to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution.
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Abstract: A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments.
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Citations
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Variational Networks: Connecting Variational Methods and Deep Learning
Erich Kobler,Teresa Klatzer,Kerstin Hammernik,Thomas Pock,Thomas Pock +4 more
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TL;DR: Surprisingly, in numerical experiments on image reconstruction problems it turns out that giving up exact minimization leads to a consistent performance increase, in particular in the case of convex models.
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Image segmentation and selective smoothing by using Mumford-Shah model
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