Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Shape Guided Object Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A biologically motivated multiresolution approach to contour detection
EURASIP Journal on Applied Signal Processing
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Learning to combine bottom-up and top-down segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
Hi-index | 0.01 |
Object segmentation is a well-known difficult problem in pattern recognition. Until now, most of the existing object segmentation methods need to go through a time-consuming training phase prior to segmentation. Both robustness and efficiency of the existing methods have room for improvement. In this work, we propose a new methodology, called POSIT, for object segmentation without intensive training process. We construct a part-based shape model to substitute the training process. In the part-based framework, we sequentially register object parts in the prior model to an image so that the searching space is largely reduced. Another advantage of the sequential matching is that, instead of predefining the weighting parameters for the terms in the matching evaluation function, we can estimate the parameters in our model on the fly. Finally, we fine-tune the previous coarse segmentation by localized graph cuts. In the experiments, POSIT has been tested on numerous natural horse and cow images and the obtained results show the accuracy, robustness and efficiency of the proposed object segmentation method.