A multi-layer coding algorithm is proposed for grey image lossless compression. We transform the original image by a set of bases (e.g., wavelets, DCT, and gradient spaces). Then, the transformed image is split into a sub-image set with a binary tree. The set include two parts: major sub-images and minor sub-images, which are coded separately. Experimental results over a common dataset show that the proposed algorithm performs close to JPEG-LS in terms of bitrate. However, we can get a scalable image quality, which is similar to JPEG2000. A suboptimal compressed image can be obtained when the bitstream is truncated by unexpected factors. Our algorithm is quit suitable for image transmission, on internet or on satellites.
Robust lane detection and tracking approach using improved Hough transform and Gaussian Mixture Model is proposed
in this paper. The approach consists of three parts: lane markings detection, lane parameters estimation and lane position
tracking. Firstly, lane marking pixels are extracted using edge and color features. Then, these pixels are used to estimate
the lane boundaries. After the vanishing point has been predicted by a RANSAC algorithm, we use an improved Hough
transform to detect the straight lane boundaries in the near field, and apply a parabolic model to represent curved lanes
probably existed in the far field. Finally, a novel lane parameters determination method, which uses Gaussian Mixture
Model to represent and update the parameters of lane boundaries, is proposed to ensure the stability of the lane tracking
system. The proposed approach is tested with some real videos captured on a highway with challenging road
environments, and the results demonstrate that our system is very reliable and can also be implemented in real-time.