Ling Xiao

Xiao Ling, Rongjun Qin

In this paper, based on the assumption that the object boundaries (e.g., buildings) from the over-view data should coincide with footprints of façade 3D points generated from street-view photogrammetric images, we aim to address this problem by proposing a fully automated geo-registration method for cross-view data, which utilizes semantically segmented object boundaries as view-invariant features under a global optimization framework through graph-matching: taking the over-view point clouds generated from stereo/multi-stereo satellite images and the street-view point clouds generated from monocular video images as the inputs, the proposed method models segments of buildings as nodes of graphs, both detected from the satellite-based and street-view based point clouds, thus to form the registration as a graph-matching problem to allow non-rigid matches; to enable a robust solution and fully utilize the topological relations between these segments, we propose to address the graph-matching problem on its conjugate graph solved through a belief-propagation algorithm. The matched nodes will be subject to a further optimization to allow precise-registration, followed by a constrained bundle adjustment on the street-view image to keep 2D29 3D consistencies, which yields well-registered street-view images and point clouds to the satellite point clouds.

Xiao Ling, Tim Menzies

For simulation-based systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. Domain-specific optimization goals (e.g. maximize output variance) are useful for guiding the rapid selection of test cases via mutation. But evaluating the selected test cases via mutation (that can distinguish the current program from the mutated systems) is a different goal to domain-specific optimizations. While the optimization goals can be used to guide the mutation analysis, that guidance should be viewed as a weak indicator since it can hurt the mutation effectiveness goals by focusing too much on the optimization goals. Based on the above, this paper proposes DoLesS (Domination with Least Squares Approximation) that selects the minimal and effective test cases by averaging over a coarse-grained grid of the information gained from multiple optimizations goals. DoLesS applies an inverted least squares approximation approach to find a minimal set of tests that can distinguish better from worse parts of the optimization goals. When tested on multiple simulation-based systems, DoLesS performs as well or even better as the prior state-of-the-art, while running 80-360 times faster on average (seconds instead of hours).

Xiao Ling, Xu Huang, Rongjun Qin

Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature match-ing often contains high uncertainties in weak/repeat textures, while BA results are helpful in reducing these uncertainties. To compute more accurate orientations, this article incorpo-rates BA and feature matching in a unified framework and formulates the union as the optimization of a global energy function so that the solutions of the BA and feature matching are constrained with each other. To avoid a degeneracy in the optimization, we propose a comprised solution by breaking the optimization of the global energy function into two-step suboptimizations and compute the local minimums of each suboptimization in an incremental manner. Experiments on multi-view high-resolution satellite images show that our proposed method outperforms state-of-the-art orientation techniques with or without accurate least-squares matching.

Rongjun Qin, Shuang Song, Xiao Ling et al.

3D recovery from multi-stereo and stereo images, as an important application of the image-based perspective geometry, serves many applications in computer vision, remote sensing and Geomatics. In this chapter, the authors utilize the imaging geometry and present approaches that perform 3D reconstruction from cross-view images that are drastically different in their viewpoints. We introduce our framework that takes ground-view images and satellite images for full 3D recovery, which includes necessary methods in satellite and ground-based point cloud generation from images, 3D data co-registration, fusion and mesh generation. We demonstrate our proposed framework on a dataset consisting of twelve satellite images and 150k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.