Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting.

A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.

On color texture normalization for active appearance models.

The extension of the standard grayscale active appearance model (AAM) techniques to color images is investigated. Prior work in this field has mainly focused on RGB color models which did not demonstrate noticeable benefits over grayscale models from the point of view of convergence accuracy. We improve on previous work by normalizing the color texture vector separately for intensity and chromaticity components. Where an appropriate color space is chosen, we demonstrate improvements in convergence accuracy as well as image synthesization quality for AAMs. Optimal results are achieved when a color space in which the image channels are strongly decorrelated is chosen. Our best results are achieved using the I1I2I3 color space, originally proposed by Ohta.