Abstract. Polygonal objects are prevalent in man-made scenes. Early approaches to detecting them relied mainly on geometry while subsequent ones also incorporated appearance-based cues. It has recently been shown that this could be done fast by searching for cycles in graphs of line-fragments, provided that the cycle scoring function can be expressed as additive terms attached to individual fragments. In this paper, we propose an approach that eliminates this restriction. Given a weighted linefragment graph, we use its cyclomatic number to partition the graph into managebly-sized sub-graphs that preserve nodes and edges with a high weight and are most likely to contain object contours. Object contours are then detected as maximally scoring elementary circuits enumerated in each sub-graph. Our approach can be used with any cycle scoring function and multiple candidates that share line fragments can be found. This is unlike in other approaches that rely on a greedy approach to finding candidates. We demonstrate that our approach significantly outperforms the state-of-the-art for the detection of building rooftops in aerial images and polygonal object categories from ImageNet.
We propose a novel approach to real-time landing site detection and assessment in unconstrained man-made environments using passive sensors. Because this task must be performed in a few seconds or less, existing methods are often limited to simple local intensity and edge variation cues. By contrast, we show how to efficiently take into account the potential sites' global shape, which is a critical cue in man-made scenes. Our method relies on a new segmentation algorithm and shape regularity measure to look for polygonal regions in video sequences. In this way we enforce both temporal consistency and geometric regularity, resulting in very reliable and consistent detections. We demonstrate our approach for the detection of landable sites such as rural fields, building rooftops and runways from color and infrared monocular sequences significantly outperforming the state-of-the-art.
The OCR technology for Chinese historical documents is still an open problem. As these documents are hand-written or hand-carved in various styles, overlapped and touching characters bring great difficulty for character segmentation module. This paper presents an over-segmentation-based method to handle the overlapped and touching Chinese characters in historic documents. The whole segmentation process includes two parts: over-segmented and segmenting path optimization. In the former part, touching strokes will be found and segmented by analyzing the geometric information of the white and black connected components. The segmentation cost of the touching strokes is estimated with connected components' shape and location, as well as the touching stroke width. The latter part uses local optimization dynamic programming to find best segmenting path. HMM is used to express the multiple choices of segmenting paths, and Viterbi algorithm is used to search local optimal solution. Experimental results on practical Chinese documents show the proposed method is effective.
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