Computer Vision – ECCV 2012: 12th European Conference on by Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei (auth.),

By Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed court cases of the twelfth ecu convention on machine imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers provided have been rigorously reviewed and chosen from 1437 submissions. The papers are equipped in topical sections on geometry, second and 3D shapes, 3D reconstruction, visible reputation and class, visible beneficial properties and picture matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and picture registration, photometry: lights and color, and photograph segmentation.

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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II

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This means that the majority of the used offsets have been limited in the coarser scales. If the upsampled shift is s = (u, v), then the other 4 shifts are (u± 2l , v) and (u, v± 2l ). 26 K. He and J. Sun Input Ours Content-Aware Fill Fig. 13. Comparisons with Content-Aware Fill. Left: input. Middle: our results. Right: results of Content-Aware Fill. The artifacts are highlighted by the arrows. 6Mp, 2Mp, 4Mp, 10Mp. The running time is in Fig. 14. Fig. 6, door (300x400) Fig. 12, lion (500x400) Fig.

3(c)). , Fig. , Fig. 4(d)); it is sufficient if the structures have a “trend” along one or a few directions. Textures can yield prominent patterns in the offset statistics. Ideally, a regular texture should generate a regular pattern of dominant offsets describing how the textures are repeated (Fig. 5). This is observed in real images (Fig. 6). We can complete the texture by shifting the image using these offsets. On the other hand, an irregular texture generates random patterns (Fig. 7). In this case our method behaves just like the Graphcut texture algorithm [17].

9(f)), mainly because they are unaware of how the objects are repeated. Our method has the advantage that it need not consider the above cases separately. It can handle all of them or a mixture of them in the same framework. 3 Offsets Sparsity and Optimized Energy Our method has an energy function (3) similar to the Shift-map method [15]. The main difference is that Shift-map allows all possible offsets. Thus our solution space is a very small subset of the one of Shift-map. Theoretically, Shift-map can achieve a smaller energy than our method (this is observed in experiments).

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