We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional pipelines that conduct detection and description separately, ELSD utilizes a shared feature extractor for both detection and description, to provide the essential line features to the higher-level tasks like SLAM and image matching in real time. First, we design the one-stage compact model, and propose to use the mid-point, angle and length as the minimal representation of line segment, which also guarantees the center-symmetry. The non-centerness suppression is proposed to filter out the fragmented line segments caused by lines' intersections. The fine offset prediction is designed to refine the mid-point localization. Second, the line descriptor branch is integrated with the detector branch, and the two branches are jointly trained in an end-to-end manner. In the experiments, the proposed ELSD achieves the state-of-the-art performance on the Wireframe dataset and YorkUrban dataset, in both accuracy and efficiency. The line description ability of ELSD also outperforms the previous works on the line matching task.
Convolutional neural networks have achieved great success in analyzing potential features inside tropical cyclones (TCs) using satellite images for intensity estimation. However, due to the high similarity of visual features in TC images, it is still a challenge to learn the accurate mapping between TC images and numerical intensity. Existing works mainly focus on the visual features of a single TC, ignoring the impact of intensity continuity and time evolution among TCs on decision making. Therefore, we propose a DR-transformer framework for temporal TC intensity estimation. Inside DR-transformers, a novel DR-extractor can extract Distance-consistency(DC) and Rotation-invariance (RI) features between TC images, and therefore can better learn the contours, structures, and other visual features of each TC image. DC features can reduce the estimation error between adjacent intensities, and RI features can eliminate feature deviation caused by shooting angles and TC rotation. Additionally, a transformer with a DR-extractor as the backbone is applied to aggregate the temporal correlation in a series of TC images, which can learn the evolution from intensity to the visual features of TC. Experiments show that the final result, an RMSE of 7.76 knots, outperforms the baseline, and is better than any previously reported method trained on the TCIR dataset.
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