Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (09): 82-88.doi: 10.13475/j.fzxb.20210601707

• Fiber Materials • Previous Articles     Next Articles

Cocoon image fusion method based on ellipse overlapping area

SUN Weihong1,2(), LI Yu1,2, LIANG Man1,2, SHAO Tiefeng1,2, GAO Minghui3   

  1. 1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
    2. Cocoon and Silk Quality Inspection Technology Institute, China Jiliang University, Hangzhou, Zhejiang 310018, China
    3. Taian Textile Fiber Inspection Institute, Taian, Shandong 217000, China
  • Received:2021-06-07 Revised:2022-04-25 Online:2022-09-15 Published:2022-09-26

RichHTML

5

PDF (PC)

40

Abstract:

Aiming at the appearance of boundary lines of the cocoon in the high latitude area when using the existing linear weight assignment method to fuse cocoon images, a cocoon image fusion method based on elliptical overlapping area was proposed. The equivalent ellipse of the cocoon surface in the image to be spliced was obtained by using the least squares fitting algorithm, and its position equation in the image coordinates was output. A mathematical model of cocoon image fusion was then established according to the displacement along the horizontal and vertical axes obtained in the image registration process, and the point set of the overlapping area of the equivalent ellipse was obtained. An improved trigonometric function weight algorithm was established through the maximum width of the ellipse overlapping area to fuse the ellipse overlapping area. The experimental results show that the fusion effect of this method is better than the fading in and out algorithms and trigonometric function weight algorithms, which can effectively eliminate the boundary line of the cocoon, and obtain a cocoon fusion image with good visual effect and more information.

Key words: image registration, equivalent ellipse, overlapping area, trigonometric function weight, fusion processing, cocoon image, cocoon quality

CLC Number: 

  • TP391

Fig.1

Schematic diagram of acquisition device"

Fig.2

Ellipse fitting of cocoon image. (a) Cocoon image;(b) Ellipse fitting results"

Fig.3

Mathematical model of cocoon image fusion"

Fig.4

Weight change diagram of modified trigonometric function"

Fig.5

Comparison of fusion results of trigonometric function weight algorithm before (a) and after (b) improved"

Fig.6

Cocoon unfolding image before fusion. (a) First group; (b) Second group"

Fig.7

Fusion results of first group of cocoon images. (a) Fading in and out algorithm; (b) Trigonometric function weight algorithm;(c) Algorithm in this paper"

Fig.8

Fusion results of second group of cocoon images. (a) Fading in and out algorithm; (b) Trigonometric function weight algorithm;(c) Algorithm in this paper"

Tab.1

Comparison of objective indexes mean value of cocoon fusion images under three methods"

蚕茧图像组别 算法名称 信息熵 平均梯度 空间频率 标准差 互信息
1 渐入渐出算法 6.07 3.97 18.94 72.82 4.400 6
三角函数权重算法 6.10 4.12 20.37 74.11 4.454 3
本文方法 6.12 3.99 19.58 75.30 4.482 8
2 渐入渐出算法 5.95 4.05 19.30 71.11 3.883 4
三角函数权重算法 5.93 4.16 20.01 72.63 3.887 6
本文方法 5.96 4.21 19.72 74.25 3.894 5
相比渐入渐出算法提高百分比/% 0.50 2.23 2.78 3.92 2.15
相比三角函数权重算法提高百分比/% 0.42 -0.98 -2.67 1.92 0.82
[1] 刘莫尘, 许荣浩, 闫筱, 等. 基于FCM及HSV模型的方格蔟黄斑茧检测与剔除技术[J]. 农业机械学报, 2018, 49(7): 31-38.
LIU Mochen, XU Ronghao, YAN Xiao, et al. Detection and removal technology of colloid-mounted cocoons based on FCM and HSV models[J]. Transactions of The Chinese Society for Agricultural Machinery, 2018, 49(7): 31-38.
[2] 宫妍. 全景图像拼接关键技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2016:10-20.
GONG Yan. Research on key technologies of panoramic image mosaic[D]. Harbin: Harbin Engineering University, 2016:10-20.
[3] QUAN Y, BU L, WU W. Improved weighted average of threshold value of HSV and wavelet transform image fusion[J]. Journal of Liaoning Technical University, 2016. DOI: 10.11956/j.issn.1008-0562.2016.01013.
doi: 10.11956/j.issn.1008-0562.2016.01013
[4] 郭晶. 基于特征点的多幅图像拼接技术研究[D]. 西安: 西安科技大学, 2012:34-36.
GUO Jing. Research on multi-image mosaic technology based on feature points[D]. Xi'an: Xi'an University of Science and Technology, 2012:34-36.
[5] YAN W Q, HOU C P, LEI J J, et al. Stereoscopic image stitching based on a hybrid warping model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(9):1934-1946.
doi: 10.1109/TCSVT.2016.2564838
[6] WU H, MIAO Z J. Image compostion based on details preserved[C]//Proceedings of the 25th International Congress on Image and Signal Processing. Chongqing: IEEE, 2012: 597-601.
[7] 李海超, 赫胜勇, 朱琦. 多片遥感图像的快速无缝拼接方法[J]. 红外与激光工程, 2011, 40(7): 1381-1386.
LI Haichao, HE Shengyong, ZHU Qi. A fast seamless mosaic method for multiple remote sensing images[J]. Infrared and Laser Engineering, 2011, 40(7): 1381-1386.
[8] 瞿中, 乔高元, 林嗣鹏. 一种消除图像拼接缝和鬼影的快速拼接算法[J]. 计算机科学, 2015, 42(3): 280-283.
doi: 10.11896/j.issn.1002-137X.2015.03.058
QU Zhong, QIAO Gaoyuan, LIN Sipeng. A fast mosaic algorithm for eliminating split seams and ghastly images[J]. Computer Science, 2015, 42(3): 280-283.
doi: 10.11896/j.issn.1002-137X.2015.03.058
[9] 汪丹, 刘辉, 李可, 等. 一种三角函数权重的图像拼接算法[J]. 红外技术, 2017, 35(1): 53-57.
WANG Dan, LIU Hui, LI Ke, et al. An image mosaic algorithm based on trigonometric function weight[J]. Infrared Technology, 2017, 35(1): 53-57.
[10] 刘素一, 夏蕾. 基于元胞自动机的织物图像拼接[J]. 纺织学报, 2011, 32(1): 29-33.
LIU Suyi, XIA Lei. Textile image matching based on cellular automata[J]. Journal of Textile Research, 2011, 31(1): 29-33.
[11] 孙卫红, 廖艺真, 梁曼, 等. 蚕茧表面图像等效阶梯柱面展开[J]. 中国图像图形学报, 2020, 25(3):498-506.
SUN Weihong, LIAO Yizhen, LIANG Man, et al. Cocoon surface image equivalent step cylinder expansion[J]. Journal of Image and Graphics, 2020, 25(3):498-506.
[12] 姚桂国, 梁金祥, 左保齐. 基于网格的织物图像拼接技术[J]. 纺织学报, 2012, 33(2): 46-49.
YAO Guiguo, LIANG Jinxiang, ZUO Baoqi. Fabric image mosaic technology based on mesh[J]. Journal of Textile Research, 2012, 33(2): 46-49.
[13] BAY H, TUYTELAARS T, GOOL L V. SURF: speeded up robust features[C]//9th European Conference on Computer Vision. Austria: Springer, 2006: 404-417.
[14] FIZGIBBON A, PILU M, FISHER R B. Direct least square fitting of ellipses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 476-480.
doi: 10.1109/34.765658
[15] 张小利, 李雄飞, 李军. 融合图像质量评价指标的相关性分析及性能评估[J]. 自动化学报, 2014, 40(2): 306-315.
ZHANG Xiaoli, LI Xiongfei, LI Jun. Correlation analysis and performance evaluation of fusion image quality evaluation index[J]. Acta Automatica Sinica, 2014, 40(2): 306-315.
[16] 陈立伟, 蒋勇. 图像融合算法的综合性能评价指标[J]. 计算机工程, 2015, 41(2): 219-223.
CHEN Liwei, JIANG Yong. Comprehensive performance evaluation index of image fusion algorithm[J]. Computer Engineering, 2015, 41(2): 219-223.
[17] ZHANG X L, LI X F, LI J. Validation and correlation analysis of metrics for evaluation performance of image fusion[J]. Acta Automatica Sinica, 2014, 40(2): 306-315.
[18] 沈英, 黄春红, 黄峰, 等. 红外与可见光图像融合技术的研究进展[J]. 红外与激光工程, 2021, 50(9): 152-169.
SHEN Ying, HUANG Chunhong, HUANG Feng, et al. Research progress of infrared and visible light image fusion technology[J]. Infrared and Laser Engineering, 2021, 50(9): 152-169.
[1] WU Le, ZHANG Qian, YANG Wanran, XU Zhaoyue, WANG Weiguan, HOU Xi. Research on operation-maintenance-patrol-inspection system of yarn package dyeing latch locking robot based on augmented reality technology [J]. Journal of Textile Research, 2022, 43(09): 34-40.
[2] YANG Xiaobo. Research on 3-D clothing style based on interactive genetic algorithm [J]. Journal of Textile Research, 2022, 43(06): 145-150.
[3] SUN Chunhong, DING Guangtai, FANG Kun. Cashmere and wool classification based on sparse dictionary learning [J]. Journal of Textile Research, 2022, 43(04): 28-32.
[4] ZHANG Ronggen, FENG Pei, LIU Dashuang, ZHANG Junping, YANG Chongchang. Research on on-line detection system of broken filaments in industrial polyester filament [J]. Journal of Textile Research, 2022, 43(04): 153-159.
[5] JIN Guiyang, CHEN Gang, SUN Pingfan. Information model of sweater production workshops based on OPC unified architecture and its application [J]. Journal of Textile Research, 2022, 43(03): 185-192.
[6] JIANG Hui, MA Biao. Style similarity algorithm based on clothing style [J]. Journal of Textile Research, 2021, 42(11): 129-136.
[7] XU Minghui, XU Pinghua, WEI Qiuju, DING Xuemei, MAO Hailin. Color parsing of female brand clothing based on nexus network modeling [J]. Journal of Textile Research, 2021, 42(11): 137-142.
[8] ZHANG Yinhui, YANG Hongkuan, LIU Qiang, HE Zifen. Real-time detection of inferior cocoons through model compression and receptive field enhancement [J]. Journal of Textile Research, 2021, 42(11): 29-38.
[9] YANG Zhengyan, XUE Wenliang, ZHANG Chuanxiong, DING Yi, MA Yanxue. Recommendations for user's bottoms matching based on generative adversarial networks [J]. Journal of Textile Research, 2021, 42(07): 164-168.
[10] JIANG Hongxia, HUANG Zhiwei, LIU Jihong. Virtual display of cheongsam based on modularization [J]. Journal of Textile Research, 2021, 42(05): 138-142.
[11] FENG Wenqian, LI Xinrong, YANG Shuai. Research progress in machine vision algorithm for human contour detection [J]. Journal of Textile Research, 2021, 42(03): 190-196.
[12] ZHU Lei, REN Mengfan, PAN Yang, LI Botao. Fabric defect detection based on similarity location and superpixel segmentation [J]. Journal of Textile Research, 2020, 41(10): 58-66.
[13] LIU Yun, XIU Yi. NURBS surface model of digital mannequin based on B/S architecture [J]. Journal of Textile Research, 2020, 41(10): 137-143.
[14] ZHANG Jianxin, LI Qi. Online cheese package yarn density detection system based on machine vision [J]. Journal of Textile Research, 2020, 41(06): 141-146.
[15] YANG Enjun, LIAO Yihui, LIU Andong, YU Li. Detection for fabric defects based on low-rank decomposition [J]. Journal of Textile Research, 2020, 41(05): 72-78.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd