Computing Visual Correspondence with Occlusions via Graph Cuts

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value for a pixel p. The energy minimized in [8] is the Potts energy 1 of [20] E(f) = Dp(fp)+ p∈L p,q∈N Vp,q · T (fp = fq). (1) Here Dp(fp) is a penalty for the pixel p to have the disparity fp, N is a neighborhood system for the pixels of the left image and T (·) is 1 if its argument is true and 0 otherwise. Minimizing this energy is NP-hard, so [8] gives two approximation algorithms. They involve the notion of moves. Consider a particular disparity (or label) α. A configuration f ′ is said to be within a single α-expansion move of f if for all pixels p ∈Leither f ′ p or f ′ p = fp = α. Now consider a pair of disparities α, β, α = β. A configuration f ′ is said to be within a single αβ-swap move of f if for all pixels p ∈L, fp ∈ {α, β} implies f ′ p = fp. The crucial fact about these moves is that for a given configuration f it is possible to efficiently find a strong local minimum of the energy; more precisely, the lowest energy configuration within a single α-expansion or αβ- swap move of f, respectively. These local improvement operations rely on graph cuts. The expansion algorithm consists entirely of a sequence of α- expansion local improvement operations for different disparities α, untilno α-expansion can reduce the energy. Similarly, the swap algorithm consists entirely of a sequence of αβ-swap local improvement operations for pairs of disparities α, β, untilnoαβ-swap can reduce the energy. This formulation, unfortunately, does not handle occlusions properly. First, two pixels in the left image can easily be mapped into the same pixel 1 In fact, they consider a more general energy but this is the simplest case that works very well in practice. 4
in the right image. Furthermore, this assumes that each pixel in the left image is mapped into some pixel in the right image. In reality, pixels in the left image can be occluded and thus not correspond to any pixel in the right image. 3 Overview of the new algorithm 3.1 Problem representation Let A be the set of (unordered) pairs of pixels that may potentially correspond. For stereo with aligned cameras, for example, we have A = {〈p, q〉 |py = qy and 0 ≤ qx − px
Page 1 and 2: Computing Visual Correspondence wit
Page 3: The new algorithms proposed in this
Page 7 and 8: • a smoothness term Esmooth, whic
Page 9 and 10: not permit occlusions to be natural
Page 11 and 12: The most closely related work consi
Page 13 and 14: Figure 3: The graph corresponding t
Page 15 and 16: • the last edge that we add is (v
Page 17 and 18: 2. Occlusion term. ˜Eocc(x) = Cp
Page 19 and 20: The corresponding graph is shown in
Page 21 and 22: First image Second image Horizontal
Page 23 and 24: Left image Right image Horizontal m
Page 25 and 26: distance. Occlusions were computed
Page 27 and 28: 7 Conclusions We have presented an
Page 29 and 30: negative function D : S×V↦→
Page 31 and 32: [7] Yuri Boykov, Olga Veksler, and
Page 33: [23] Rick Szeliski and Ramin Zabih.

Computing Visual Correspondence with Occlusions via Graph Cuts

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