Abstract

To detect fruits within the scope of monitoring and improve the accuracy and stability of the test results. Considering the deficiencies of the traditional method which detects slowly and has poor adaptability to environment, a detection algorithm based on improved SSIM is proposed herein. Using the characteristics of SSIM that combines three comprehensive factors: image brightness, contrast and structural similarity to calculate the similarity of the image to improve the impact of environmental factors on the monitor screen of test results. And it gives full consideration to the efficiency of algorithm and improves the shortcomings of the traditional method. The experiments proved that the improved method can detect the fruit accurately and efficiently.

Keywords:

Detection of fruit, improved method, SSIM algorithm,

References

1. Charrier, C., K. Knoblauch, L.T. Maloney, A.C. Bovik and A.K. Moorthy, 2012. Optimizing multiscale SSIM for compression via MLDS. IEEE T. Image Process., 21(12):4682-94.
   CrossRef    PMid:22868568 PMCid:PMC4678964    
2. Duan, Y., J. Ma, W. Chen and Q. Feng, 2010. Improved SSIM medical image quality assessment. Comput. Eng. Appl., 46(2): 145-149.
   
3. Duan, Y., W. Chen, Q. Feng and J. Ma, 2011. Gradient-weighted SSIM based medical image quality assessment. Comput. Eng. Appl., 47(24): 205-210.
   
4. Gao, Y., 2006. The image capturing and disposing methods for interlinks meters. Electr. Meas. Instrum., 43(484).
   
5. Hu, Q., J. Du, M. Fang, L. Zi and P. Han, 2013. Multi-sensor image fusion algorithm based on SSIM. J. Southeast Univ., Nat. Sci. Edn., 2013.
   
6. Huang, L.F., X.N. Cui, J.A. Lin and Z.Y. Shi, 2011. A new reduced-reference image quality assessment method based on SSIM. Appl. Mech. Mater., 55-57: 31-36.
   CrossRef    
7. Mi, Z., 2014. Image quality assessment in multiband DCT domain based on SSIM. Optik-Int. J. Light Electron. Optics, 125(21): 6470-6473.
   CrossRef    
8. Premaratne, P. and M. Premaratne, 2014. Image matching using moment invariants. Neurocomputing, 137: 65-70.
   CrossRef    
9. Shiqi, W., A. Rehman, W. Zhou, M. Siwei and G. Wen, 2011. SSIM-inspired divisive normalization for perceptual fruit coding. Proceeding of the 18th IEEE International Conference on Image Processing (ICIP, 2011).
   
10. Wang, G.H., J. Ming and H. Wu, 2009. Video quality assessment based on SSIM and ROI. Chinese J. Sci. Instrum., 30(9): 1906-1911.
    
11. Wang, L., 2013. Improvements for AVS inter mode selection based on SSIM and SAD. Fruit Eng., 37(1).
    
12. Yang, W., 2008. Method of image quality assessment based on human visual system and structural similarity. J. Beijing Univ., Aeronaut. Astronaut., 34(1).
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.