Reduction of blocking artifacts in both spatial domain and transformed domain

In this paper, we propose a bi-domain technique to reduce the blocking artifacts commonly incurred in image processing. Some pixels are sampled in the shifted image block and some high frequency components of the corresponding transformed block are discarded. By solving for the remaining unknown pixel values and the transformed coefficients, a less blocky image is obtained. Simulation results using the Discrete Cosine Transform and the Slant Transform show that the proposed algorithm gives a better quantitative result and image quality than that of the existing methods

Sensor fusion for semantic segmentation of urban scenes

Abstract—Semantic understanding of environments is an important problem in robotics in general and intelligent au-tonomous systems in particular. In this paper, we propose a semantic segmentation algorithm which effectively fuses infor-mation from images and 3D point clouds. The proposed method incorporates information from multiple scales in an intuitive and effective manner. A late-fusion architecture is proposed to maximally leverage the training data in each modality. Finally, a pairwise Conditional Random Field (CRF) is used as a post-processing step to enforce spatial consistency in the structured prediction. The proposed algorithm is evaluated on the publicly available KITTI dataset [1] [2], augmented with additional pixel and point-wise semantic labels for building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence regions. A per-pixel accuracy of 89.3 % and average class accuracy of 65.4 % is achieved, well above current state-of-the-art [3]. I