The detection of pavement cracks is essential for damage assessment and maintenance of pavement. Obtaining complete crack paths using traditional approaches is difficult due to the varied appearance of pavement cracks and complex texture noise. A robust graph network refining algorithm guided by multiscale curvilinear structure filtering (CFGNR) is proposed for pavement crack detection. A multiscale curvilinear structure filter consisting of curved linear templates and a local texture inhibition term is first utilized to enhance crack contours. The enhanced pavement image is then presented as a graph of overcomplete crack paths, and a graph network refining approach derived from path saliency and local contrast constraints is utilized to select the optimal subset of crack paths. Finally, an iterative path growing algorithm is employed to obtain pixel-level cracks. Experimental results on four public pavement datasets show that the proposed algorithm significantly improves the completeness of detected cracks and achieves a superior performance compared to six existing algorithms.

Region proposal algorithms are beneficial for enhancing the performance of object detection and recognition methods. We present a method for grouping region proposals based on perceptual grouping principles. The grouping principles are simulated to extract image features, and the region proposals are segmented by solving a sequence of parametric maxflow problems. In order to extract complex objects from natural images, the element connectedness cue is introduced in the parametric energy functions. This newly introduced cue is propitious to group objects with diversified patterns. To effectively fuse the grouping principles, a multiclassify-based learning algorithm is proposed to optimize an ensemble of binary segmentation models. The training samples are first divided into groups to pretrain each individual model, and the algorithm adaptively adjusts the sample groups in the iteration procedure to learn an optimal set of models. We conduct the experiments on the PASCAL Visual Object Classes Challenge 2012 segmentation dataset but also in the context of region proposals in optical remote sensing images, and the results show that the proposed method can achieve a favorable performance compared to the existing algorithms.