Abstract
The manual postmortem inspection of broilers and their viscera is becoming a bottleneck as the slaughter rate increases. Computer vision can assist veterinarians during the inspection, but specular highlights can hide crucial details when inspecting for diseases on the viscera set. This study aims to restore details behind these specular highlights by capturing two images of the same viscera using shifting light positions. The dataset consists of images captured in-line at a poultry processing plant. The method achieves an average SSIM score of 0.96 over a test set of 100 image sets. The result is visually pleasing images with correct textural information instead of specular highlights.
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Altwaijry, H., Veit, A., Belongie, S.: Learning to detect and match keypoints with deep architectures. In: British Machine Vision Conference (BMVC), New York, UK (2016)
Angelopoulou, E.: Specular highlight detection based on the fresnel reflection coefficient. In: Proceedings of the IEEE International Conference on Computer Vision (2007)
Arumugaperumal, S., Sivagami, B., PazhaniKumar, K.: An advanced scratch removal method for Fingerprint biometrics. In: 2011 3rd International Conference on Electronics Computer Technology, pp. 196–200. IEEE, April 2011. https://doi.org/10.1109/ICECTECH.2011.5941886
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014
Buades, A., Haro, G., Meinhardt-Llopis, E.: Obtaining high quality photographs of paintings by image fusion. Image Process. Line 5, 159–175 (2015). https://doi.org/10.5201/ipol.2015.49
Chao, K., Chen, Y.R., Hruschka, W.R., Park, B.: Chicken heart disease characterization by multi-spectral imaging. Appl. Eng. Agric. Am. Soc. Agric. Eng. 99(171), 99–106 (2001)
Feris, R., Raskar, R., Tan, K.H., Turk, M.: Specular highlights detection and reduction with multi-flash photography. J. Braz. Comput. Soc. 12(1), 35–42 (2006). https://doi.org/10.1007/BF03192386
Hsieh, C., Chen, Y.R., Dey, B.P., Chan, D.E.: Separating septicemic and normal chicken livers by visible/near-infrared spectroscopy and back-propagation neural networks. Trans. ASAE 45(2), 459–469 (2002)
Jørgensen, A., Fagertun, J., Moeslund, T.B.: Diagnosis of broiler livers by classifying image patches. In: Sharma, P., Bianchi, F.M. (eds.) SCIA 2017. LNCS, vol. 10269, pp. 374–385. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59126-1_31
Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999). https://doi.org/10.1109/ICCV.1999.790410
Marel Poultry: The world of Poultry Processing (2018). https://marel.com/files/pdf/world-of-stork-poultry-en.pdf
Philipsen, M.P., Jørgensen, A., Escalera, S., Moeslund, T.B.: RGB-D segmentation of poultry entrails. In: Perales, F.J.J., Kittler, J. (eds.) AMDO 2016. LNCS, vol. 9756, pp. 168–174. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41778-3_17
Ritchie, H., Roser, M.: Meat and seafood production & consumption. In: Our World is Changing (2018). OurWorldInData.org
Saxena, S., Singh, R.K.: A survey of recent and classical image registration methods. Int. J. Signal Process. Image Process. Pattern Recogn. 7(4), 167–176 (2014). https://doi.org/10.14257/ijsip.2014.7.4.16
Shah, S.M.Z.A., Marshall, S., Murray, P.: Removal of specular reflections from image sequences using feature correspondences. Mach. Vis. Appl. 28(3), 409–420 (2017). https://doi.org/10.1007/s00138-017-0826-6
Suo, J., An, D., Ji, X., Wang, H., Dai, Q.: Fast and high quality highlight removal from a single image. IEEE Trans. Image Process. 25(11), 5441–5454 (2016). https://doi.org/10.1109/TIP.2016.2605002
Tao, Y., Shao, J., Skeeles, K., Chen, Y.R.: Detection of splenomegaly in poultry carcasses by UV and color imaging. Trans. ASAE 43(2), 469–474 (2000)
Trambadia, S., Mayatra, H.: Image in painting based on Discrete Wavelet Transform (DWT) technique. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–6. IEEE, November 2016. https://doi.org/10.1109/GET.2016.7916794
Vestergaard, M., Bengtson, S., Pedersen, M., Rankl, C., Moeslund, T.B.: Stitching grid-wise atomic force microscope images. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 3 (VISIGRAPP), pp. 110–117 (2016). https://doi.org/10.5220/0005716501100117
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Zagorchev, L., Goshtasby, A.: A comparative study of transformation functions for nonrigid image registration. IEEE Trans. Image Process. 15(3), 529–538 (2006). https://doi.org/10.1109/TIP.2005.863114
Zhang, S., Jiao, L., Liu, F., Wang, S.: Global low-rank image restoration with Gaussian mixture model. IEEE Trans. Cybern. 1–12 (2017). https://doi.org/10.1109/TCYB.2017.2715846
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Jørgensen, A., Pedersen, M., Gade, R., Fagertun, J., Moeslund, T.B. (2019). Reaching Behind Specular Highlights by Registration of Two Images of Broiler Viscera. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_30
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