Image annotating tools for agricultural purpose : a requirements study

• Autorzy: Bator Marcin , Pankiewicz Maciej

Identyfikatory

DOI

10.22630/MGV.2019.28.1.7

• Języki publikacji: EN

Abstrakty

EN

Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.

Słowa kluczowe

EN

image annotation; agriculture; uncertainty

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2019
• Tom: Vol. 28, No. 1/4
• Strony: 69--77
• Opis fizyczny: Bibliogr. 21 poz., fot., tab.

Twórcy

autor

Bator Marcin

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-6881-3695

autor

Pankiewicz Maciej

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-6945-0523

Bibliografia

• [1] D. K. Iakovidis, C. Smailis, T. Goudas, and I. Maglogiannis. Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis. The Scientific World Journal, vol. 2014, Article ID 286856, 12 pages, 2014. doi: 10.1155/2014/286856.
• [2] N. Fiedler, M. Bestmann, N. Hendrich. ImageTagger: An Open Source Online Platform for Collaborative Image Labeling. In Holz et al., editors, RoboCup 2018: Robot World Cup XXII. vol 11374Lecture Notes in Computer Science, Springer 2019 doi: 10.1007/978-3-030-27544-013 https://github.com/bit-bots/imagetagger
• [3] Erdmann, M., Maedche, A., Schnurr, H. P., and Staab, S. From manual to semi-automatic semantic annotation: About ontology-based text annotation tools. In Proceedings of the COLING-2000Workshop on Semantic Annotation and Intelligent Content (pp. 79-85). 2000, August.
• [4] Sazedj, P., and Pinto, H. S. Time to evaluate: Targeting Annotation Tools. In SemAnnot@ ISWC. 2005, November.
• [5] Dasiopoulou, S., Giannakidou, E., Litos, G., Malasioti, P., and Kompatsiaris, Y. A survey of semantic image and video annotation tools. In Knowledge-driven multimedia information extraction and ontology evolution (pp. 196-239). Springer, Berlin, Heidelberg. 2011.
• [6] S. Seifert, M. Kelm, M. Moeller, S. Mukherjee, A. Cavallaro, M. Huber and D. Comaniciu. Semantic annotation of medical images. In Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications (Vol. 7628, p. 762808). International Society for Opticsand Photonics. 2010, March.
• [7] K. Chehab, A. Kalboussi, and A. H. Kacem. Study of Annotations in e-health Domain. In International Conference on Smart Homes and Health Telematics (pp. 189-199). Springer, Cham. 2018, July.
• [8] M. Neves, U. Leser. A survey on annotation tools for the biomedical literature. Briefings in Bioinformatics, 15(2): 327-340, March 2014. doi: 10.1093/bib/bbs084
• [9] T. Wirthgen, G. Lempe, S. Zipser and U. Grünhaupt. Level-set based infrared image segmentation for automatic veterinary health monitoring. In L. Bolc et al., editors, Computer Vision and Graphics: Proc. Int. Conf. ICCVG 2012, volume 7594 of Lecture Notes in Computer Science, pages 685–693, Warsaw, Poland, 24-26 Sep 2012. Springer.
• [10] L. Yang, D. Zhang, J. Luo, Z. Wang and C. Wu. Automatic Recognition for Cotton Spider Mites Damage Level Based on SVM and AdaBoost. Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery50 (2):14-20 2019.
• [11] A. Farooq, X. Jia, J. Hu and J. Zhou. Multi-resolution weed classification via convolutional neural network and superpixel based local binary pattern using remote sensing images. Remote Sensing 11, 2019. doi: doi:10.3390/rs11141692
• [12] R. Aravind, M. Daman, and B. S. Kariyappa. Design and development of automatic weed detection and smart herbicide sprayer robot. In 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015257–261, 2015.
• [13] Labelbox. [Online; accessed 10 Dec 2019]. https://www.labelbox.io/.
• [14] Labelimg. [Online; accessed 10 Dec 2019]. https://github.com/tzutalin/labelImg.
• [15] M. Bauml. Sloth documentation. [Online; accessed 10 Dec 2019]. http://sloth.readthedocs.io/en/latesthttps://github.com/cvhciKIT/sloth.
• [16] C. Zhang, K. Loken, Z. Chen, Z. Xiao and G. Kunkel. Mask editor: an image annotation tool for image segmentation tasks. (2018). arXiv preprint arXiv: 1809.06461. https://github.com/Chuanhai/Mask-Editor.
• [17] Computer Vision Annotation Tool (CVAT). [Online; accessed 10 Dec 2019]. https://github.com/opencv/cvat.
• [18] B. C. Russell, A. Torralba, K. P. Murphy and W. T. Freeman. Labelme: a database and web-based tool for image annotation. Int. J. Comput. Vis., 77 (1–3): 157-173 2008. doi: 10.1007/s11263-007-0090-8. https://github.com/wkentaro/labelme.
• [19] imglab. [Online; accessed 10 Dec 2019]. https://github.com/NaturalIntelligence/imglab
• [20] A. Dutta ad A. Zisserman. The VIA Annotation Software for Images, Audio and Video In Proceedings of the 27th ACM International Conference on Multimedia (MM’19), October 21–25, 2019, Nice, France. ACM, New York, NY, USA, 4 pages. doi:10.1145/3343031.3350535
• [21] Rhoban. [Online; accessed 10 Dec 2019]. http://rhoban.com/tagger/index.php https://github.com/Rhoban/tagger.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).