As an aim for the future in order to improve performance more might be considered processing of bigger amount of images for learning. Such action may be extremely helpful if these extra images will be difficult to segment, as an example, images with grass or those where a seal was segmented not accurately. This aim is achievable because the Saimaa ringed seal dataset contains more images than what was used for the current work.
The main drawback is that this approach demands a lot of manual work, which is time-consuming.
Besides, this thesis does not incorporate any investigation on modification CNN layers.
Undoubtedly, better adoption of the network structure to the current need may have a positive effect on the overall accuracy. Possibly, there are or will be soon introduced more suitable state-of-the-art algorithms and solutions for image processing, which could help to improve the result achieved described in this work.
One more aspect to be solved in the future is an integration of the proposed solution into the re-identification framework. It is expected to bring better accuracy in matching indi-viduals as well as cutting down the time spent on computations performing. In addition, as Saimaa ringed seals are not the only animals with such fur structure it seems reasonable to try to apply the proposed method onto other species. It first can be tested on some other kinds of seal and if it works in the same way then it probably might work with something like leopards or giraffes.
7 CONCLUSION
In this thesis, the fur pattern extraction task in the area of animal biometrics was consid-ered. Also, the review of connected topics and approaches was made to find the ideas and direction for the development of the pattern extractor. Comparison, combination, and im-provement of state-of-the-art approaches such as UNet and DeepLab were made in order to perform the task.
The proposed algorithm includes three stages: preprocessing, segmentation, postprocess-ing. Preprocessing incorporates two main ideas: removing of undesired regions and high-lighting the target object. These ideas were realized by the application of scaling and rearranging of intensity. Segmentation is served by the CNN based on the UNet architec-ture. It takes in an image of a seal segmented from the background and produces a binary mask of the pattern is represented in black and the rest is white. Finally, color inversion and one more cropping stage represent a postprocessing stage.
It has been shown that the proposed method outperforms previously used the Sato filter-based method. The accuracy that was estimated by the Sørensen–Dice coefficient in-creased by 139% and by 192% for the Jaccard index. Estimation was held on ten difficult to segment images that contain either grass, complicated luminance distribution, or low quality of the image. Finally, this method speeds up the extraction phase by 37% due to the neural network-based architecture.
REFERENCES
[1] Saimaa ringed seal. https://wwf.fi/en/saimaa-ringed-seal/. Ac-cessed: 2020-01-27.
[2] Tero Sipilä. Pusa hispida ssp. saimensis, saimaa seal, 2015.
[3] William Paterson, Paula Redman, Lex Hiby, Simon Moss, Ailsa Hall, and Patrick Pomeroy. Pup to adult photo-id: Evidence of pelage stability in gray seals. Marine Mammal Science, 29:537–541, 2013.
[4] Coexist project. http://www2.it.lut.fi/project/coexist/index.
shtml. Accessed: 2020-05-08.
[5] Patrick Clemins, Michael Johnson, Kirsten Leong, and Anne Savage. Automatic classification and speaker idenfication of african elephant (loxodonta africana) vo-calizations. The Journal of the Acoustical Society of America, 117:956–63, 2005.
[6] Emiliano Mori, Mattia Menchetti, Sandro Bertolino, Giuseppe Mazza, and Leonardo Ancillotto. Reappraisal of an old cheap method for marking the european hedgehog. Mammal Research, 2015.
[7] Andrew William, Hannuna Sion, Campbell Neill, and Burghardt Tilo. Automatic in-dividual holstein friesian cattle identification via selective local coat pattern match-ing in rgb-d imagery. InIEEE International Conference on Image Processing (ICIP), pages 484–488, 2016.
[8] Luca Bergamini, Angelo Porrello, Andrea Dondona, Ercole Negro, Mauro Matti-oli, Nicola D’Alterio, and Simone Calderara. Multi-views embedding for cattle re-identification. In 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pages 184–191, 2018.
[9] Clemens Brust, Tilo Burghardt, Milou Groenenberg, Christoph Kading, Hjalmar S. Kühl, Marie L. Manguette, and Joachim Denzler. Towards automated visual monitoring of individual gorillas in the wild. InIEEE International Conference on Computer Vision Workshops (ICCVW), pages 2820–2830, 2017.
[10] Santosh Kumar, Sanjay Singh, Ravi Singh, Amit Singh, and Dr. Shrikant Tiwari.
Real-time recognition of cattle using animal biometrics.Journal of Real-Time Image Processing, 13:1–22, 2016.
[11] Artem Zhelezniakov, Tuomas Eerola, Meeri Koivuniemi, Miina Auttila, Riikka Lev-änen, Marja Niemi, Mervi Kunnasranta, and Heikki Kälviäinen. Segmentation of
saimaa ringed seals for identification purposes. InAdvances in Visual Computing, pages 227–236, Cham, 2015. Springer International Publishing.
[12] Tina Chehrsimin, Tuomas Eerola, Meeri Koivuniemi, Miina Auttila, Riikka Levä-nen, Marja Niemi, Mervi Kunnasranta, and Heikki Kälviäinen. Automatic individual identification of saimaa ringed seals. IET Computer Vision, 12(2):146–152, 2018.
[13] Ekaterina Nepovinnykh, Tuomas Eerola, Heikki Kälviäinen, and Gleb Radchenko.
Identification of saimaa ringed seal individuals using transfer learning. InAdvanced Concepts for Intelligent Vision Systems, pages 211–222. Springer International Pub-lishing, 2018.
[14] Ekaterina Nepovinnykh, Tuomas Eerola, and Heikki Kälviäinen. Siamese network based pelage pattern matching for ringed seal re-identification. InWinter Conference on Applications of Computer Vision Workshop, 2020.
[15] Er. Anjna Anjna and Er.Rajandeep Kaur. Review of image segmentation technique.
In International Journal of Advanced Research in Computer Science, volume 8, pages 36–39, 2017.
[16] Xiaolong Liu, Zhidong Deng, and Yuhan Yang. Recent progress in semantic image segmentation. Artificial Intelligence Review, 52(2):1089–1106, 2019.
[17] Vishwanath A. Sindagi and Vishal M. Patel. A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recognition Letters, 107:3 – 16, 2018.
[18] V. Badrinarayanan, A. Kendall, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pat-tern Analysis and Machine Intelligence, 39(12):2481–2495, 2017.
[19] L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Se-mantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 40(4):834–848, 2018.
[20] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional net-works for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 234–241, Cham, 2015. Springer International Publishing.
[21] Data augmentation for deep learning. https://towardsdatascience.
com/data-augmentation-for-deep-learning-4fe21d1a4eb9. Ac-cessed: 2020-05-10.
[22] Simon Jegou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Ben-gio. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. InConference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017.
[23] K. Hu, Z. Zhang, X. Niu, Y. Zhang, C. Cao, F. Xiao, and X. Gao. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing, 309(2):179–191, 2018.
[24] Rafal Mantiuk, Karol Myszkowski, and Hans-Peter Seidel. A perceptual frame-work for contrast processing of high dynamic range images. ACM Transactions on Applied Perception, 3(3):286–308, 2006.
[25] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image seg-mentation. InEuropean Conference on Computer Vision (ECCV), pages 833–851, Cham, 2018. Springer International Publishing.