CREATION OF IMAGE MODELS FOR INSPECTING DEFECTS ON COMMERCIAL DRIED FISH FLOSS

Hong-Dar Lin; Chang-Yi Lin; Ching-Hsiang Lin

doi:10.5937/jaes18-27185

Hong-Dar Lin Chaoyang University of Technology
Chang-Yi Lin Chaoyang University of Technology
Ching-Hsiang Lin National Kaohsiung University of Science and Technology

DOI: https://doi.org/10.5937/jaes18-27185

Keywords: Fish floss, Computer vision, Visual defect, Curvelet transform

Abstract

Both fish and fish related products are two common foods in our daily lives. Since fish products have highly economic value, the main form of direct consumption of raw fish, cooked fish, processed products (such as preserved fish, canned fish, fish floss, etc.) is fairly extensive. Fish floss is a chopped finely or mashed fish meat boiled in seasonings, then stir fry until the fish meat is arid and pulverous. When fish is usually eaten, fish bones are some of the most regularly gulped extraneous objects happened in meals. In the making of commercial fish flosses, fishbone inspection is conducted by expertise inspectors using their feeling of contact and sight which could cause misjudgments. When consumers eat fish floss with bones, it may cause harm to the health of consumers. Therefore, this study proposes an automated fishbone inspection method for fish floss products. The proposed method applies the curvelet transform with square-ring low-pass energy filtering to remove the random patterns of background and delete the angle direction of background texture. The approximated and partial detailed components regarding fish bones and uniform background are preserved in the low and medium frequency bands. Then the filtered image is inversely converted to spatial domain. In the reconstructed image, the background random texture is attenuated and the fishbone areas are enhanced. Finally, a threshold value is determined by statistical interval estimation and the restored image can be easily segmented to into two categories namely dark fish bones, and white background. Outcome results show that the suggested inspection system can effectively determine the presence and the locations of fish bones on the surfaces of fish floss.

References

1) Fitriya, W., Ekantari, N. (2019). Projective mapping and descriptive analysis of commercial fish floss in Yogyakarta Region. IOP Conference Series: Earth and Environmental Science, 370, 012069.

2) Poonsri, T., Jafarzadeh, S., Ariffin, F., Ismail, N., Barati, Z., Latif, S., Müller, J. (2019). Improving the physicochemical and antioxidant properties of fish floss incorporated with waste cassava leaves. Journal of Chemical Health Risks, 9(1), 27-34.

3) El-Mesery, H.S., Mao, H., Abomohra, A.E. (2019). Applications of non-destructive technologies for agricultural and food products quality inspection. Sensors, 19, 846.

4) Lin, H.D., Chen, H.L. (2018). Automated visual fault inspection of optical elements using machine vision technologies. Journal of Applied Engineering Science, 16(4), 447-453.

5) Chiu, Y.P., Lin, Y.K., Lin, H.D. (2020). Effective image models for inspecting profile flaws of car mirrors with applications. Journal of Applied Engineering Science, 18(1), 81-91.

6) Gowen, A.A., Taghizadeh, M., O’Donnell, C.P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. Journal of Food Engineering, 93, 7-12.

7) Huang, W., Li, J., Wang, Q., Chen, L. (2015). Development of a multispectral imaging system for online detection of bruises on apples. Journal of Food Engineering, 146, 62-71.

8) Khoje, S.A., Bodhe, S.K., Adsul, A. (2013). Automated skin defect identification system for fruit grading based on discrete curvelet transform. International Journal of Engineering and Technology, 5(4), 3251-3256.

9) Chen, K., Qin, C. (2008). Segmentation of beef marbling based on vision threshold. IEEE Xplore, Computers and Electronics in Agriculture, 62, 223-230.

10) Brosnan, T., Sun, D.W. (2004). Improving quality inspection of food products by computer vision - a review. Journal of Food Engineering, 61, 3-16.

11) Andersen, K. (2003). X-ray techniques for quality assessment. In: Luten, J.B., Oehlenschlaeger, J., Olafsdottir, G. (Eds.). Quality of Fish from Catch to Consumer: Labelling, modelling and traceability. Wageningen Academic Publishers, 283-286.

12) Han, Y., Shi, P. (2007). An efficient approach for fish bone detection based on image preprocessing and particle swarm clustering. Communications in Computer and Information Science, 2, 940-948.

13) Thielemann, J., Kirkhus, T., Kavli, T., Schumann-Olsen, Haugland, O., Westavik, H. (2007). System for estimation of pin bone positions in pre-rigor salmon. In: Lecture Notes in Computer Science. Advanced Concepts for Intelligent Vision Systems (ACIVS2007), 4678, 888-896.

14) Sivertsen, A.H., Chu, C.K., Wang, L.C., Godtliebsen, F., Heia, K., Nilsen, H. (2009). Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering, 90, 317-324.

15) Mery, D., Lillo, I., Loebel, H., Riffo, V., Soto, A., Cipriano, A., Aguilera, J.M. (2011). Automated fish bone detection using X-ray imaging. Journal of Food Engineering, 105, 485-492.

16) Rerkratn, A., Kaewpoonsuk, A. (2014). System development for quality evaluation of fish fillet using image processing. Proceedings of the 2014 International Conference on Computer, Communications and Information Technology, 196-199.

17) Boubchir, L., Fadili, J.M. (2005). Multivariate statistical modeling of images with the curvelet transform. IEEE Xplore, 1581046, 747-750.

18) Jiang, T., Zhao, X. (1999). Research and application of image denoising method based on curvelet transform. The International Archives of the Photogrammetry, 62, 363-368.

19) Zhang, Y., Li, T., Li, Q. (2013). Defect detection for tire laser shearography image using curvelet transform based edge detector. Optics & Laser Technology, 47, 64-71.

20) Li, L., Zhang, X., Zhang, H., He, X., Xu, M. (2015). Feature extraction of non-stochastic surfaces using curvelets. Precision Engineering, 39, 212-219.

21) Kalaivani, M., Jeyalakshmi, M.S., Aparna,V. (2012). Extraction of retinal blood vessels using curvelet transform and Kirsch’s templates. International Journal of Emerging Technology and Advanced Engineering, 2, 360-363.

22) Mandal, T., Wu, Q.M.J. (2008). Face recognition using curvelet based PCA. 2008 19th International Conference on Pattern Recognition, 1-4.

23) Candès, E.J., Donoho, D.L. (2000). Curvelets: A surprisingly effective non-adaptive representation for objects with edges. in Curve and Surface Fitting: Saint-Malo Proceedings, Cohen, A., Merrien, J.-L, Schumaker, L.L., Eds. Nashville, TN: Vanderbilt Univ. Press, 105-120.

24) Cand, E.J., Donoho, D.L. (2004). New tight frames of curvelets and optimal representations of objects with C2 singularities. Department of Statistics, Communications on Pure and Applied Mathematics, 57(2), 219-266.

25) Cand, E.J., Demanet, L., Donoho, D., Ying, L. (2006). Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3), 861-899.

26) Montgomery, D.C., Runger, G.C. (2007). Applied Statistics and Probability for Engineers. 4th Edition, John Wiley & Sons, New Jersey, USA.

27) Couprie, M., Bertrand, G. (1997). Topological Grayscale Watershed Transformation. SPIE Vision Geometry VI Proceedings, 3168, 136-146.

28) Lin, H.D., Tsai, H.H. (2012). Automated quality inspection of surface defects on touch panels. Journal of the Chinese Institute of Industrial Engineers, 29(5), 291-302.