Fusion of Multi-view Multi-exposure Images with Delaunay Triangulation

被引:0
作者
Yu, Hanyi [1 ]
Zhou, Yue [1 ]
机构
[1] Shanghai Jiao Tong Univ, Image Proc & Pattern Recognit, Shanghai, Peoples R China
来源
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II | 2016年 / 9948卷
关键词
Multi-view; Multi-exposure; Delaunay triangulation; Image registration; Image fusion;
D O I
10.1007/978-3-319-46672-9_76
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present a completely automatic method for multi-view multi-exposure image fusion. The technique adopts the normalized cross-correlation (NCC) as the measurement of the similarity of interest points. With the matched feature points, we divide images into a set of triangles by Delaunay triangulation. Then we apply affine transformation to each matched triangle pairs respectively to get the registration of multi-view images. After images aligned, we partition the image domain into uniformed regions and select the images that provides the most information with certain blocks. The selected images are fused together under monotonically blending functions.
引用
收藏
页码:682 / 689
页数:8
相关论文
共 50 条
  • [21] DISCRIMINATIVE MULTI-VIEW FEATURE SELECTION AND FUSION
    Liu, Yanbin
    Liao, Binbing
    Han, Yahong
    2015 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO (ICME), 2015,
  • [22] An Improved Clustering Method for Multi-view Images
    Dong, Yang
    Fan, Dazhao
    Ma, Qiuhe
    Ji, Song
    IMAGE AND GRAPHICS, ICIG 2019, PT III, 2019, 11903 : 134 - 144
  • [23] ALIGNMENT OF UNCALIBRATED IMAGES FOR MULTI-VIEW CLASSIFICATION
    Arik, Sercan Omer
    Vural, Elif
    Frossard, Pascal
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011,
  • [24] STOCHASTIC FUSION OF MULTI-VIEW GRADIENT FIELDS
    Sankaranarayanan, Aswin C.
    Chellappa, Rama
    2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5, 2008, : 1324 - 1327
  • [25] CurveMEF: Multi-exposure fusion via curve embedding network
    Peng, Pai
    Jing, Zhongliang
    Pan, Han
    Liu, Yang
    Song, Buer
    NEUROCOMPUTING, 2024, 596
  • [26] Dense SIFT for ghost-free multi-exposure fusion
    Liu, Yu
    Wang, Zengfu
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2015, 31 : 208 - 224
  • [27] A Multi-Exposure Fusion Method for Reflection Suppression of Curved Workpieces
    Sun, Chongyan
    Song, Kechen
    Su, Jianyu
    Yan, Yunhui
    Zhang, Tianle
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [28] Variable augmented neural network for decolorization and multi-exposure fusion
    Liu, Qiegen
    Leung, Henry
    INFORMATION FUSION, 2019, 46 : 114 - 127
  • [29] Assessment for multi-exposure image fusion based on fuzzy theory
    Fu Zheng-Fang
    Zhu Hong
    Yu Shun-Yuan
    ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW, 2015, 82 (04): : 197 - 204
  • [30] An improved algorithm of multi-exposure image fusion by detail enhancement
    Qu, Zhong
    Huang, Xu
    Liu, Ling
    MULTIMEDIA SYSTEMS, 2021, 27 (01) : 33 - 44