Object segmentation and object tracking are fundamental research areas in the computer
vision community. These two topics are difficult to handle some common challenges, such …

The medical image analysis field has traditionally been focused on the development of
organ-, and disease-specific methods. Recently, the interest in the development of more …

Decision forests (also known as random forests) are an indispensable tool for automatic
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …

Existing optical flow methods make generic, spatially homogeneous, assumptions about the
spatial structure of the flow. In reality, optical flow varies across an image depending on …

Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This
paper addresses semantic segmentation by incorporating high-order relations and mixture …

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …

A novel model-based approach to 3D hand tracking from monocular video is presented. The
3D hand pose, the hand texture, and the illuminant are dynamically estimated through …

Object appearance modeling is crucial for tracking objects, especially in videos captured by
nonstationary cameras and for reasoning about occlusions between multiple moving …

Layered models provide a compelling approach for estimating image motion and
segmenting moving scenes. Previous methods, however, have failed to capture the structure …

The quality of any tracking-by-assignment hinges on the accuracy of the foregoing target
detection/segmentation step. In many kinds of images, errors in this first stage are …