Computing Visual Correspondence with Occlusions via Graph Cuts

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Left image Our results Ground truth L1 correlation Results from [8] Results from [25] Figure 4: Stereo results on Tsukuba dataset. Occluded pixels are shown in red. Disparities <strong>Occlusions</strong> Method Errors Gross errors False negatives False positives Our results 6.7% 1.9% 42.6% 1.1% Boykov, Veksler & Zabih [8] 6.7% 2.0% 82.8% 0.3% Zitnick & Kanade [25] 12.0% 2.6% 52.4% 0.8% Correlation 28.5% 12.8% 87.3% 6.1% Figure 5: Error statistics on Tsukuba dataset. 20
First image Second image Horizontal motion (our method) Horizontal motion (method of [8]) Vertical motion (our method) Vertical motion (method of [8]) Figure 6: Motion results on the flower garden sequence. Occluded pixels are shown in red. 21
Page 1 and 2: Computing Visual Correspondence wit
Page 3 and 4: The new algorithms proposed in this
Page 5 and 6: in the right image. Furthermore, th
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: The corresponding graph is shown in
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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