Segmenting Highly Articulated Video Objects with Weak-Prior Random Forests.
Journal
Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006, Proceedings, Part IV
Pages
373-385
Date Issued
2006
Author(s)
Abstract
We address the problem of segmenting highly articulated video objects in a wide variety of poses. The main idea of our approach is to model the prior information of object appearance via random forests. To automatically extract an object from a video sequence, we first build a random forest based on image patches sampled from the initial template. Owing to the nature of using a randomized technique and simple features, the modeled prior information is considered weak, but on the other hand appropriate for our application. Furthermore, the random forest can be dynamically updated to generate prior probabilities about the configurations of the object in subsequent image frames. The algorithm then combines the prior probabilities with low-level region information to produce a sequence of figure-ground segmentations. Overall, the proposed segmentation technique is useful and flexible in that one can easily integrate different cues and efficiently select discriminating features to model object appearance and handle various articulations. © Springer-Verlag Berlin Heidelberg 2006.
Subjects
Algorithms; Feature extraction; Image analysis; Image segmentation; Information retrieval; Problem solving; Video signal processing; Image frames; Image patches; Segmentation technique; Video objects; Object recognition
SDGs
Type
conference paper