Showing papers on "Generalised Hough transform published in 2010"

Hough forest-based facial expression recognition from video sequences

Abstract: Automatic recognition of facial expression is a necessary step toward the design of more natural human-computer interaction systems. This work presents a user-independent approach for the recognition of facial expressions from image sequences. The faces are normalized in scale and rotation based on the eye centers' locations into tracks from which we extract features representing shape and motion. Classification and localization of the center of the expression in the video sequences are performed using a Hough transform voting method based on randomized forests. We tested our approach on two publicly available databases and achieved encouraging results comparable to the state of the art.

Occluded Pedestrian Tracking Using Body-Part Tracklets

Abstract: Detection of pedestrians under occlusion has been addressed previously with body-part-based approaches, in particular using the generalised Hough transform. Tracking is usually addressed by first detecting pedestrians in each frame independently and then tracking the detections over time. This paper presents a novel variation on the generalised Hough approach: tracking is performed first, and detection second. Robust features on a pedestrian are tracked over short time-frames to form tracklets. Not only do tracklets reduce false alarms due to unstable features, but they provide temporal correspondence information in Hough space. Consequently tracking can be posed as optimal path finding in Hough space and efficiently solved using the Viterbi algorithm. The paper also presents an improvement to the random Hough forest training method by using multi-objective optimisation.

Tracking of brain tumors using vision and neurosonography

Abstract: We have developed a method to render brain tumours from endoneurosonography. We propose to track an ultrasound probe in successive endoscopic images without relying on an external optic or magnetic tracking system. The probe is tracked using two different methods: one of them based on a generalised Hough transform and the other one based on particle filters. By estimating the pose of the ultrasound probe in several contiguous images, we use conformal geometric algebra to compute the geometric transformations that yield the 3D position of the tumour, which was segmented in the ultrasound image using morphological operators. We use images from brain phantoms to evaluate the performance of the proposed methods, and our results show that they are robust.