Hybrid Deep Learning-Gaussian Process Network for Pedestrian Lane Detection in Unstructured Scenes.

Pedestrian lane detection is an important task in many assistive and autonomous navigation systems. This article presents a new approach for pedestrian lane detection in unstructured environments, where the pedestrian lanes can have arbitrary surfaces with no painted markers. In this approach, a hybrid deep learning-Gaussian process (DL-GP) network is proposed to segment a scene image into lane and background regions.

Robust interactive image segmentation using convex active contours.

The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model.