@INPROCEEDINGS{CampbellBMVC07,
  author       = {Neill D.F. Campbell and George Vogiatzis and Carlos Hern\'{a}ndez and Roberto Cipolla},
  title        = {Automatic {3D} Object Segmentation in Multiple Views using Volumetric Graph-Cuts},
  booktitle    = {18th British Machine Vision Conference},
  year         = {2007},
  volume       = {1},
  pages        = {530-539},
  abstract     = {We propose an algorithm for automatically obtaining a segmentation of 
a rigid object in a sequence of images that are calibrated for camera pose 
and intrinsic parameters. Until recently, the best segmentation results have 
been obtained by interactive methods that require manual labelling of image 
regions. Our method requires no user input but instead relies on the camera 
fixating on the object of interest during the sequence. We begin by learning 
a model of the object is colour, from the image pixels around the fixation 
points. We then extract image edges and combine these with the object colour 
information in a volumetric binary MRF model. The globally optimal segmentation 
of 3D space is obtained by a graph-cut optimisation. From this 
segmentation an improved colour model is extracted and the whole process 
is iterated until convergence. 

Our first finding is that the fixation constraint, which requires that the object 
of interest is more or less central in the image, is enough to determine 
what to segment and initialise an automatic segmentation process. Second, 
we find that by performing a single segmentation in 3D, we implicitly exploit 
a 3D rigidity constraint, expressed as silhouette coherency, which significantly
improves silhouette quality over independent 2D segmentations. We 
demonstrate the validity of our approach by providing segmentation results 
on real sequences.}
}