By Yu-jin Zhang
Snapshot and video segmentation is without doubt one of the most important initiatives of photograph and video research: extracting info from a picture or a chain of pictures. within the final forty years, this box has skilled major development and improvement, and has led to a digital explosion of released details. Advances in photograph and Video Segmentation brings jointly the newest effects from researchers curious about cutting-edge paintings in photo and video segmentation, offering a suite of recent works made by means of greater than 50 specialists all over the world.
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Additional info for Advances in Image And Video Segmentation
Optimal Image Segmentation Methods Based on Energy Minimization 25 Figure 2. Hough transforms for lines (left) and circles (right). In the case of lines, the crosses indicate line candidates; in the case of circles the size of the balls represents the number of votes received by the center of the ball representing a potential arc. In both cases, the axes represent each of the parameters of the corresponding 2D (for lines) and 3D (for circles) parameter space. 19 200 250 300 350 400 450 500 550 600 650 20 40 50 60 70 80 90 100 110 120 130 Source: copyright IEEE 2004 where G (θ − θ l(i ) ) denotes the density of a Gaussian kernel centered at a candidate line or circle θ l(i ) and ϖ i denotes the weights (number of votes).
Given an image, this Bayesian model assigns a posterior probability to each possible configuration. We will see later how to use an inference algorithm based on BP for finding the most likely configuration. The shape prior models the variability of the template; it assigns a probability to each possible configuration. ,θ N corresponding to each point of the contour. , qN), where each node is defined by qi = (xi ,θ i ) and xi = (xi, y i). We show an example of a template for “A” character in Figure 5 (left).
Once the cost function is defined in Equations 53-55 its maximization is addressed by the Bayesian A* algorithm (Coughlan & Yuille, 1999). Given an initial junction center ( xc0 , yc0 ) and an orientation φ 0, the algorithm explores a tree in which each segment pj may expand Q successors. Although there are QN paths for path lengths of N = L, the Bayesian A* exploits the fact that we want to detect one target path against clutter, instead of taking the best choice from a population of paths. Then, the complexity of the search may be reduced by pruning partial paths with “too low” rewards.