Direct Recovery of Motion and Shape in the General Case by Fixation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
On combining graph-partitioning with non-parametric clustering for image segmentation
Computer Vision and Image Understanding
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Silhouette Coherence for Camera Calibration under Circular Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying foreground from multiple images
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Wide-baseline multi-view video segmentation for 3D reconstruction
Proceedings of the 1st international workshop on 3D video processing
Joint Multi-Layer Segmentation and Reconstruction for Free-Viewpoint Video Applications
International Journal of Computer Vision
Multiple view object cosegmentation using appearance and stereo cues
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
N-tuple color segmentation for multi-view silhouette extraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Interactive object segmentation from multi-view images
Journal of Visual Communication and Image Representation
Integrating multiple viewpoints for articulated scene model aquisition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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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's 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.