Automatic correction of the adverse effects of light on fruit surfaces using the shape-from-shading method

https://doi.org/10.17221/105/2017-CJFSCitation:Zhang S., Lu J., Shi L ., Wang X., Hou D. (2018): Automatic correction of the adverse effects of light on fruit surfaces using the shape-from-shading method. Czech J. Food Sci., 36: 37-43.
download PDF
In this study, we propose a method for correcting the adverse effects produced by the cur vature of fruit objects in images acquired by cameras in machine vision systems. The areas near the edge are darker in acquired images than in the centre, which results in many difficulties for subsequent analyses. In this paper, the fruit object was considered as a Lambertian surface. The light intensity was analysed and the height and normal on fruit surface was deduced based on the shape-from-shading (SFS) algorithm. The geometric correction factors were calculated and the adverse effects of light intensity were corrected on the fruit surface. The proposed method was evaluated on a test set of four types of fruit. The results show that the non-uniformity of the greyscale value on the fruit surface fell by 35.5% after correction, and the ref lectance in the central area of fruit is similar to that of the peripheral areas when using the proposed method. The experiments prove that our method allowed homogenisation of the greyscale level of the pixels belonging to the same class, regardless of where they are on the fruit surface, which will facilitate subsequent classification tasks.
References:
Aleixos N., Blasco J., Navarron F., Molto E. (2002): Multi- spectral inspection of citrus in real-time using machine vision and digital signal processors. Computers & Elec- tronics in Agriculture, 33: 121–137.
 
Bajksy P., Kooper R . (2005): Prediction accuracy of color imager y from hy p ersp e ctral imager y. Pro ce e dings of SPIE - The International Society for Optical Engineering, 5806: 1325–1326.
 
Costa Corrado, Antonucci Francesca, Pallottino Federico, Aguzzi Jacopo, Sun Da-Wen, Menesatti Paolo (2011): Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision. Food and Bioprocess Technology, 4, 673-692 https://doi.org/10.1007/s11947-011-0556-0
 
Foley J.D., van Dam A., Feiner S.K., Hughes J.F. (1996): Com- puter Graphics: Principles and Practice. 2nd Ed. Boston, Addison-Wesley: 722–733.
 
Gómez-Sanchis J., Moltó E., Camps-Valls G., Gómez-Chova L., Aleixos N., Blasco J. (2008): 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, 85, 191-200 https://doi.org/10.1016/j.jfoodeng.2007.06.036
 
Katrašnik J., Pernuš F., Likar B. (2011): Illumination system characterization for hyperspectral imaging. Proceedings of SPIE, 7891: 141–152.
 
Lu J., Wu P., Xue J., Qiu M., Peng F. (2015): Detecting de- fects on citrus surface based on circularity threshold segmentation. In: Tang Z. (ed.): 12th International Conference on Fuzzy Systems and Knowledge Discovery, Aug 15–17, 2015, Zhangjiajie, China. IEEA, New York: 1543–1547. doi: 10.1109/FSKD.2015.7382174
 
Qin Jianwei, Lu Renfu (2008): Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology, 49, 355-365 https://doi.org/10.1016/j.postharvbio.2008.03.010
 
Riquelme M.T., Barreiro P., Ruiz-Altisent M., Valero C. (2008): Olive classification according to external damage using image analysis. Journal of Food Engineering, 87, 371-379 https://doi.org/10.1016/j.jfoodeng.2007.12.018
 
Teena M., Manickavasagan A., Mothershaw A., El Hadi S., Jayas D. S. (2013): Potential of Machine Vision Techniques for Detecting Fecal and Microbial Contamination of Food Products: A Review. Food and Bioprocess Technology, 6, 1621-1634 https://doi.org/10.1007/s11947-013-1079-7
 
Ping-Sing Tsai, Shah Mubarak (1994): Shape from shading using linear approximation. Image and Vision Computing, 12, 487-498 https://doi.org/10.1016/0262-8856(94)90002-7
 
Unay D., Gosselin B. (2007): Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. Journal of Food Engineering, 78, 597-605 https://doi.org/10.1016/j.jfoodeng.2005.10.038
 
Ruo Zhang , Ping-Sing Tsai , Cryer J.E., Shah M. (): Shape-from-shading: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 690-706 https://doi.org/10.1109/34.784284
 
Zhang Baohua, Huang Wenqian, Li Jiangbo, Zhao Chunjiang, Fan Shuxiang, Wu Jitao, Liu Chengliang (2014): Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326-343 https://doi.org/10.1016/j.foodres.2014.03.012
 
Zhang Chi, Zhao Chunjiang, Huang Wenqian, Wang Qingyan, Liu Shenggen, Li Jiangbo, Guo Zhiming (2017): Automatic detection of defective apples using NIR coded structured light and fast lightness correction. Journal of Food Engineering, 203, 69-82 https://doi.org/10.1016/j.jfoodeng.2017.02.008
 
download PDF

© 2018 Czech Academy of Agricultural Sciences