3D Head models have many applications, such as virtual conference, 3D web game, and so on. The existing several web-based
face modeling solutions that can create a 3D face model from one or two user uploaded face images, are limited to
generating the 3D model of only face region. The accuracy of such reconstruction is very limited for side views, as well
as hair regions. The goal of our research is to develop a framework for reconstructing the realistic 3D human head based
on two approximate orthogonal views. Our framework takes two images, and goes through segmentation, feature points
detection, 3D bald head reconstruction, 3D hair reconstruction and texture mapping to create a 3D head model. The main
contribution of the paper is that the processing steps are applies to both the face region as well as the hair region.
This paper presents an online object tracking method, in which co-training and particle filters algorithms cooperate and
complement each other for robust and effective tracking. Under framework of particle filters, the semi-supervised cotraining
algorithm is adopted to construct, on-line update, and mutually boost two complementary object classifiers,
which consequently improves discriminant ability of particles and its adaptability to appearance variants caused by
illumination changing, pose verying, camera shaking, and occlusion. Meanwhile, to make sampling procedure more
efficient, knowledge from coarse confidence maps and spatial-temporal constraints are introduced by importance
sampling. It improves not only the accuracy and efficiency of sampling procedure, but also provides more reliable
training samples for co-training. Experimental results verify the effectiveness and robustness of our method.