[1] |
郭峰. 基于机器视觉的水果品质无损检测与分析方法研究[D]. 上海: 上海交通大学, 2007.
|
|
GUO F. Study of machine vision based no-contact inspect and analysis method for fruit quality[D]. Shanghai: Shanghai Jiao Tong University, 2007. (in Chinese)
|
[2] |
ZHANG B, HUANG W, GONG L, LI J B, ZHAO C J, LIU C L. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. Journal of Food Engineering, 2015,146:143-151.
doi: 10.1016/j.jfoodeng.2014.08.024
|
[3] |
IQBAL S M, GOPAL A, SANKARANARAYANAN P E, NAIR A B. Classification of selected citrus fruits based on color using machine vision system. International Journal of Food Properties, 2016,19(2):272-288.
doi: 10.1080/10942912.2015.1020439
|
[4] |
LÓPEZ-GARCÍA F, ANDREU-GARCÍA G, BLASCO J, ALEIXOS N, VALIENTE J M. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 2010,71(2):189-197.
doi: 10.1016/j.compag.2010.02.001
|
[5] |
白雪冰, 宋恩来, 李润佳, 许景涛. 柑橘表面缺陷图像快速准确分割方法. 沈阳农业大学学报, 2018,49(2):242-249.
|
|
BAI X B, SONG E L, LI R J, XU J T. Fast and accurate segmentation method for surface defects of citrus. Journal of Shenyang Agricultural University, 2018,49(2):242-249. (in Chinese)
|
[6] |
GÓMEZ-SANCHIS J, MOLTÓ E, CAMPS-VALLS G, GÓMEZ- CHOVA L, ALEIXOS N, BLASCO J. Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering, 2008,85(2):191-200.
doi: 10.1016/j.jfoodeng.2007.06.036
|
[7] |
NIPHADKAR N P, BURKS T F, JIAN W Q, RITENOUR M. Edge effect compensation for citrus canker lesion detection due to light source variation - a hyperspectral imaging application. Agricultural Engineering International Cigr Journal, 2013,15(4):314-327.
|
[8] |
LI J B, RAO X Q, WANG F, WU W, YING Y B. Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 2013,82:59-69.
doi: 10.1016/j.postharvbio.2013.02.016
|
[9] |
RONG D, RAO X Q, YING Y B. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Computers & Electronics in Agriculture, 2017,137:59-68.
|
[10] |
RONG D, YING Y B, RAO X Q. Embedded vision detection of defective orange by fast adaptive lightness correction algorithm. Computers & Electronics in Agriculture, 2017,138:48-59.
|
[11] |
TAO Y, WEN Z. An adaptive spherical image transform for high- speed fruit defect detection. Transactions of the ASME, 1999,42(1):241-246.
|
[12] |
冯斌, 汪懋华. 计算机视觉技术识别水果缺陷的一种新方法. 中国农业大学学报, 2002,7(4):73-76.
|
|
FENG B, WANG M H. Study on identifying measurement about default of fruit in computer vision. Journal of China Agricultural University, 2002,7(4):73-76. (in Chinese)
|
[13] |
RAJAVEl P. Image dependent brightness preserving histogram equalization. IEEE Transactions on Consumer Electronics, 2010,56(2):756-763.
doi: 10.1109/TCE.2010.5505998
|
[14] |
TAN T L, SIM K S, TSO C P. Image enhancement using background brightness preserving histogram equalisation. Electronics Letters, 2012,48(3):155-157.
doi: 10.1049/el.2011.3740
|
[15] |
JIA L. Application of image enhancement method for digital images based on Retinex theory. Optik - International Journal for Light and Electron Optics, 2013,124(23):5986-5988.
doi: 10.1016/j.ijleo.2013.04.115
|
[16] |
SHUKRI D S, ASMUNI H, OTHMAN R M, HASSAN R. An improved multiscale retinex algorithm for motion-blurred iris images to minimize the intra-individual variations. Pattern Recognition Letters, 2013,34(9):1071-1077.
doi: 10.1016/j.patrec.2013.02.017
|
[17] |
李江波, 饶秀勤, 应义斌. 基于照度-反射模型的脐橙表面缺陷检测. 农业工程学报, 2011,27(7):338-342.
|
|
LI J B, RAO X Q, YING Y B. Detection of navel surface defects based on illumination-reflectance model. Transactions of the Chinese Society of Agricultural Engineering, 2011,27(7):338-342. (in Chinese)
|
[18] |
LEE P H, WU S W, HUNG Y P. Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Transactions on Image Processing, 2012,21(9):4280-4289.
doi: 10.1109/TIP.2012.2202670
|
[19] |
CHEOLKON J, TIAN S, LICHENG J. Eye detection under varying illumination using the retinex theory. Neurocomputing, 2013,113(596):130-137.
doi: 10.1016/j.neucom.2013.01.038
|
[20] |
WANG S H, ZHANG J, HU H M, LI B. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 2013,22(9):3538-3548.
doi: 10.1109/TIP.2013.2261309
|
[21] |
王殿伟, 王晶, 许志杰, 刘颖. 一种光照不均匀图像的自适应校正算法. 系统工程与电子技术, 2017,39(6):1383-1390.
|
|
WANG D W, WANG J, XU Z J, LIU Y. Adaptive correction algorithm for non-uniform illumination images. Systems Engineering and Electronics, 2017,39(6):1383-1390. (in Chinese)
|
[22] |
刘志成, 王殿伟, 刘颖, 刘学杰. 基于二维伽马函数的光照不均匀图像自适应校正算法. 北京理工大学学报, 2016,36(2):191-196.
|
|
LIU Z C, WANG D W, LIU Y, LIU X J. Adaptive adjustment algorithm for non-uniform illumination images based on 2D gamma function. Transaction of Beijing Institute of Technology, 2016,36(2):191-196. (in Chinese)
|
[23] |
李江波. 脐橙表面缺陷的快速检测方法研究[D]. 杭州: 浙江大学, 2012.
|
|
LI J B. Study on rapid detection methods of defects on navel orange surface[D]. Hangzhou: Zhejiang University, 2012. (in Chinese)
|
[24] |
王干, 孙力, 李雪梅, 张明, 吕强, 蔡建荣. 基于机器视觉的脐橙采后田间分级系统设计. 江苏大学学报(自然科学版)>, 2017(6):57-61.
|
|
WANG G, SUN L, LI X L, ZHANG M, LÜ Q, CAI J R. Design of postharvest in-field grading system for navel orange based on machine vision. Journal of Jiangsu University (Natural Science Edition), 2017(6):57-61. (in Chinese)
|
[25] |
庞江伟. 基于计算机视觉的脐橙表面常见缺陷种类识别的研究[D]. 杭州: 浙江大学, 2006.
|
|
PANG J W. Study on external defects classification of navel orange based on machine vision[D]. Hangzhou: Zhejiang University, 2006. (in Chinese)
|
[26] |
饶秀勤. 基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[D]. 杭州: 浙江大学, 2007.
|
|
RAO X Q. Real-time inspection technology of fruit quality using machine vision[D]. Hangzhou: Zhejiang University, 2007. (in Chinese)
|
[27] |
CHACON-MURGUIA M I, RAMIREZ-QUINTANA J, URIAS-ZAVALA D. Segmentation of video background regions based on a DTCNN- clustering approach. Signal, Image and Video Processing, 2015,9(1):135-144.
doi: 10.1007/s11760-014-0718-4
|
[28] |
应义斌. 水果图像的背景分割和边缘检测技术研究. 浙江大学学报, 2000,26(1):35-38.
|
|
YING Y B. Study on background segment and edge detection of fruit image using machine vision. Journal of Zhejiang University, 2000,26(1):35-38. (in Chinese)
|
[29] |
李江波, 黄文倩, 张保华, 彭彦昆, 赵春江. 类球形水果表皮颜色变化校正方法研究. 农业机械学报, 2014,45(4):226-230.
|
|
LI J B, HUANG W Q, ZHANG B H, PENG Y K, ZHAO C J. Correction algorithm of lighting non-uniformity on spherical fruit. Transactions of the Chinese Society for Agricultural Machinery, 2014,45(4):226-230. (in Chinese)
|
[30] |
张明, 李鹏, 邓烈, 何绍兰, 易时来, 郑永强, 谢让金, 马岩岩, 吕强. 基于掩模及亮度校正算法的脐橙表面缺陷分割. 中国农业科学, 2019,52(2):327-338.
doi: 10.3864/j.issn.0578-1752.2019.02.011
|
|
ZHANG M, LI P, DENG L, HE S L, YI S L, ZHENG Y Q, XIE R J, MA Y Y, LÜ Q. Segmentation of navel orange surface defects based on mask and brightness correction algorithm. Scientia Agricultura Sinica, 2019,52(2):327-338. (in Chinese)
doi: 10.3864/j.issn.0578-1752.2019.02.011
|
[31] |
李江波, 饶秀勤, 应义斌, 马本学, 郭俊先. 基于掩模及边缘灰度补偿算法的脐橙背景及表面缺陷分割. 农业工程学报, 2009,25(12):133-137.
|
|
LI J B, RAO X Q, YING Y B, MA B X, GUO J X. Background and external defects segmentation of navel orange based on mask and edge gray value compensation algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(12):133-137. (in Chinese)
|