Extracting road graphs from aerial images automatically is more efficient and costs less than from field acquisition. This can be done by a post-processing step that vectorizes road segmentation predicted by CNN, but imperfect predictions will result in road graphs with low connectivity. On the other hand, iterative next move exploration could construct road graphs with better road connectivity, but often focuses on local information and does not provide precise alignment with the real road. To enhance the road connectivity while maintaining the precise alignment between the graph and real road, we propose a point-based iterative graph exploration scheme with segmentation-cues guidance and flexible steps. In our approach, we represent the location of the next move as a 'point' that unifies the representation of multiple constraints such as the direction and step size in each moving step. Information cues such as road segmentation and road junctions are jointly detected and utilized to guide the next move and achieve better alignment of roads. We demonstrate that our proposed method has a considerable improvement over state-of-the-art road graph extraction methods in terms of F-measure and road connectivity metrics on common datasets.

The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.