Robust Image Matching with Line Context

Some features are specially designed to cope with complex illumination variations [1, 2, 3, 4, 5]. These features are based on intensity orders rather than values so that their descriptors are invariant to non-linear intensity changes. However, one limitation of such features is that they are only able to handle monotonic illumination changes, as the examples shown in Figure 1. In this paper, we try to cope with this problem by proposing a new type of local feature, which describes the context information of neighboring line segments. Through extensive experiments and based on existing research, we found that the best pixel grouping level to handle large illumination variations is using curves or lines. Since curves require more sophisticated groupings and can be approximated by multiple line segments, we use line segments as the primitives of our feature descriptor. We call the proposed feature Line Context, since it is inspired by Shape Context. Line Context Detector. It is well known that edges are present at various scales. To detect edges at different scales we use multi-scale Canny edge detector with Gaussian derivatives at several pre-selected scales. Two phases are used to remove unstable edges. The first phase is to apply Laplacian operator. Those edges that do not attain a distinctive extremum over scales will be removed. The second phase is to remove those edges with homogeneous gradients, i.e. the points where the underlying curves have zero curvatures. We apply Harris matrix to achieve this purpose. Segments in Context. The edge pixels are linked to connected curves at different scales. These curves are then fitted by straight line segments. As shown in Figure 2-(a), several cases need to be considered for representing curves with line segments. One curve may be fitted by multiple segments like curve a. Two segments with small gap in between are merged into one larger segment no matter they are on the same scale (curve b and d) or different scales (curve f and g). In the latter case, the merged segment only exists in the lower scale (segment 8). Besides, all the segments in higher levels are also segments (segment 1, 2 and 3) or part of segments (segment 5 and 7) in lower levels. For each keypoint, we need to find line segments in its neighborhood, which is also called context of the feature. The line segments lying inside or partially inside the context are called context segments. The initial scale σ provides an estimate size of searching area. Let dpoint−set(v,S) be the shortest distance from point v to a set of points S. With keypoint k and all-segments {segi}, the segments set L in the context is defined as,