Semantic Line Combination Detector
Jinwon Ko, Dongkwon Jin, Chang-Su Kim
摘要
A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. More-over, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- Computational Scaffolding of Composition, Value, and Color for Disciplined DrawingJiaju Ma, Chau Vu, Asya Lyubavina, Catherine Liu 等UIST 2025 · 被引用 7 次
- ProCrop: Learning Aesthetic Image Cropping from Professional CompositionsKe Zhang, Tianyu Ding, Jiachen Jiang, Tianyi Chen 等AAAI 2026 · 被引用 3 次
相关 Paper
- Harmonious Semantic Line Detection via Maximal Weight Clique SelectionDongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Chang-Su KimCVPR 2021
- Transformer Based Line Segment Classifier with Image Context for Real-Time Vanishing Point Detection in Manhattan WorldXin Tong, Xianghua Ying, Yongjie Shi, Ruibin Wang 等CVPR 2022 · 被引用 17 次
- ELSD: Efficient Line Segment Detector and DescriptorHaotian Zhang, Yicheng Luo, Fangbo Qin, Yijia He 等ICCV 2021 · 被引用 32 次
- DeepLSD: Line Segment Detection and Refinement with Deep Image GradientsRémi Pautrat, Daniel Barath, Viktor Larsson, Martin R. Oswald 等CVPR 2023
- VaPiD: A Rapid Vanishing Point Detector via Learned OptimizersShichen Liu, Yichao Zhou, Yajie ZhaoICCV 2021 · 被引用 19 次
