SHARPNESS-AWARE DOCUMENT IMAGE MOSAICING USING GRAPHCUTS

被引:0
|
作者
Eum, Sungmin [1 ]
Doermann, David [1 ]
机构
[1] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA
基金
美国国家科学基金会;
关键词
document; image mosaicing; panorama; Graphcuts; ENERGY MINIMIZATION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
There are numerous types of documents which are difficult to scan or capture in a single pass due to their physical size or the size of their content. One possible solution that has been proposed is mosaicing multiple overlapping images to capture the complete document. In this paper, we present a novel Graphcut-based document image mosaicing method which seeks to overcome the known limitations of the previous approaches. First, our method does not require any prior knowledge of the content of the given document images, making it more widely applicable and robust. Second, information regarding the geometrical disposition between the overlapping images is exploited to minimize the errors at the boundary regions. Third, our method incorporates a sharpness measure which induces cut generation in a way that results in the mosaic including the sharpest pixels. Our method is shown to outperform previous methods, both quantitatively and qualitatively.
引用
收藏
页码:2575 / 2579
页数:5
相关论文
共 50 条
  • [31] SALR: Sharpness-Aware Learning Rate Scheduler for Improved Generalization
    Yue, Xubo
    Nouiehed, Maher
    Al Kontar, Raed
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (09) : 12518 - 12527
  • [32] Noise-resistant sharpness-aware minimization in deep learning
    Su, Dan
    Jin, Long
    Wang, Jun
    NEURAL NETWORKS, 2025, 181
  • [33] Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
    Chen, Zixiang
    Zhang, Junkai
    Kou, Yiwen
    Chen, Xiangning
    Hsieh, Cho-Jui
    Gu, Quanquan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [34] TOWARDS BOOSTING BLACK-BOX ATTACK VIA SHARPNESS-AWARE
    Zhang, Yukun
    Yuan, Shengming
    Song, Jingkuan
    Zhou, Yixuan
    Zhang, Lin
    He, Yulan
    2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME, 2023, : 294 - 299
  • [35] Sharpness-Aware Minimization Leads to Better Robustness in Meta-learning
    Xu, Mengke
    Wang, Huiwei
    2023 15TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE, ICACI, 2023,
  • [36] Generalizable Prompt Learning via Gradient Constrained Sharpness-Aware Minimization
    Liu, Liangchen
    Wang, Nannan
    Zhou, Dawei
    Liu, Decheng
    Yang, Xi
    Gao, Xinbo
    Liu, Tongliang
    IEEE TRANSACTIONS ON MULTIMEDIA, 2025, 27 : 1100 - 1113
  • [37] Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
    Si, Dongkuk
    Yun, Chulhee
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [38] Fast sharpness-aware training for periodic time series classification and forecasting
    Park, Jinseong
    Kim, Hoki
    Choi, Yujin
    Lee, Woojin
    Lee, Jaewook
    APPLIED SOFT COMPUTING, 2023, 144
  • [39] A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model
    Mariam, Iqra
    Xue, Xiaorong
    Gadson, Kaleb
    SENSORS, 2024, 24 (13)
  • [40] Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network
    Yi, Xin
    Babyn, Paul
    JOURNAL OF DIGITAL IMAGING, 2018, 31 (05) : 655 - 669