Abstract

We present a new method to extract multiple segmentations of an ob ject viewed by multiple cameras, given only the camera cal- ibration. We introduce the n-tuple color model to express inter-view consistency when inferring in each view the foreground and background color models permitting the final segmentation. A color n-tuple is a set of pixel colors associated to the n pro jections of a 3D point. The first goal is set as finding the MAP estimate of background/foreground color models based on an arbitrary sample set of such n-tuples, such that sam- ples are consistently classified, in a soft way, as ”empty” if they pro ject in the background of at least one view, or ”occupied” if they pro ject to foreground pixels in all views. An Expectation Maximization framework is then used to alternate between color models and soft classifications. In a final step, all views are segmented based on their attached color models. The approach is significantly simpler and faster than previous multi-view segmentation methods, while providing results of equivalent or better quality.