By Dhruv Batra, Adarsh Kowdle, Devi Parikh, Jiebo Luo, Tsuhan Chen (auth.)
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Extra info for Interactive Co-segmentation of Objects in Image Collections
For example, gamers want to be able to “scan” and use their own gear (like skateboards) in a skateboarding game. The computer-vision task then is to create a 3D model of this object of interest from a collection of images of this object. One approach to achieve this, would be to haul an expensive laser scanner to get precise depth estimates in a controlled setup, and reconstruct the object . However, this might not be a feasible solution for average users. Another typical approach is to capture images of the object in a controlled environment like a multicamera studio with mono-color screen [1, 3, 6, 8, 22, 24] or structured lighting , and then use something like a shape-from-silhouette algorithm [2, 5, 7, 23] to construct the 3D model.
We extract the following features to describe an image. • Illumination histogram: One of the observations we made was that the presence of strong shadows across the image (Fig. 10(a)) often results in poor co-segmentation accuracies. To capture this intuition, we compute a 50-dim histogram of the gray scale image. • Hue, Saturation and Value entropy: The variety of colours in an image typically corresponds to the amount of useful information in the image, as seen in Fig. 10(b). We quantify this via a 3-dim vector holding the entropies of the hue, saturation and value marginal histograms.
Fig. 18: Common Failure Case: (a) shows the group of images; (c) shows the segmentations achieved by scribbling on a single image, shown in (b). Cyan indicates foreground. (f) shows the segmentations achieved by scribbles on multiple images, shown in (e). When foreground and background have a lot of overlap in colour distributions, our interactive segmentation method faces difﬁculty in producing accurate segmentations (compare segmentations in (c),(f) with ground-truth in (d)). g. colour-pallet of ), which should result in better performance.