Advances in Image And Video Segmentation by Yu-jin Zhang

By Yu-jin Zhang

Picture and video segmentation is without doubt one of the most important initiatives of photograph and video research: extracting info from a picture or a series of pictures. within the final forty years, this box has skilled major development and improvement, and has ended in a digital explosion of released info. Advances in picture and Video Segmentation brings jointly the newest effects from researchers focused on cutting-edge paintings in picture and video segmentation, delivering a suite of contemporary works made through greater than 50 specialists world wide.

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This means, for instance, that we will select randomly between circles and lines when we want to choose a model. Furthermore, as the ratio P(W ' | I ) / P (W | I ) = e − ∆H may be close to zero, in these cases the jump proposals will be rejected. Under these conditions, the stochastic search progresses too slowly. This situation has motivated the development of a novel data-driven approach (Tu & Zhu, 2002; Han, Tu, & Zhu, 2004) for re-designing the proposal probabilities. Following this approach, let us first consider the three types of jumps that we may have: (1) Changing of model type; (2) Merging two adjacent intervals to build a new one with a given model; and (3) Splitting a given interval between two adjacent ones having new models.

000 steps (right). 05 step Source: copyright IEEE 2004 Figure 4. Jump-diffusion algorithm for region segmentation (1D problem). The variable “jump” registers the occurrence of a jumping event which follows a Poisson distribution (Equation 26). JUPM-DIFFUSION ALGORITHM: MCMCII-III INITIALIZE W randomly, temperature T ← T0 , t = 0 ; WHILE ( T > TFINAL ) OR (convergence) WHILE NOT (jump) Diffusion for all change points xi : Eq. (25) dxi (t ) 1 = [( I ( xi ) − I 0 ( xi ; l i +1 ,θi +1 ))2 − ( I ( xi ) − I 0 ( xi ; l i ,θi )) 2 ] + 2T N (0,1) dt 2σ 2 Update W with new change points.

We show an example of a template for “A” character in Figure 5 (left). For assigning probabilities to each configuration, we have a reference configura~ ~ ~ ~ ) of the template. , q N ~ , the higher the probability that it will have this configuration. A Markov configuration Q Random Field will be used as graphical model, as shown in Figure 5 (right), in order to ~ penalize the deviation between Q and Q . This model will be invariant to global rotation and translation, but it will have a fixed scale, which is supposed to be known beforehand.

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