{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:02:39Z","timestamp":1785603759089,"version":"3.56.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031611391","type":"print"},{"value":"9783031611407","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-61140-7_47","type":"book-chapter","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:10:33Z","timestamp":1717053033000},"page":"496-506","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Framework for\u00a0Explanation-Aware Visualization and\u00a0Adjudication in\u00a0Object Detection: First Results and\u00a0Perspectives"],"prefix":"10.1007","author":[{"given":"Arnab Ghosh","family":"Chowdhury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Massan\u00e9s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Meinert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Atzmueller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jss.2016.02.010","volume":"117","author":"U Alegre","year":"2016","unstructured":"Alegre, U., Augusto, J.C., Clark, T.: Engineering context-aware systems and applications: a survey. J. Syst. Softw. 117, 55\u201383 (2016)","journal-title":"J. Syst. Softw."},{"key":"47_CR2","doi-asserted-by":"publisher","unstructured":"Atzmueller, M.: Declarative aspects in explicative data mining for computational sensemaking. In: Seipel, D., Hanus, M., Abreu, S. (eds.) Declarative Programming, pp. 97\u2013114. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-030-00801-7_7","DOI":"10.1007\/978-3-030-00801-7_7"},{"key":"47_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00449-3","volume":"2","author":"M Bany Muhammad","year":"2021","unstructured":"Bany Muhammad, M., Yeasin, M.: Eigen-cam: visual explanations for deep convolutional neural networks. SN Comput. Sci. 2, 1\u201314 (2021)","journal-title":"SN Comput. Sci."},{"key":"47_CR4","unstructured":"Chowdhury, A.G., Schut, N., Atzmueller, M.: A hybrid information extraction approach using transfer learning on richly-structured documents. In: Proceedings of LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR. CEUR Workshop Proceedings, vol.\u00a02993, pp. 13\u201325. CEUR-WS.org (2021)"},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"David, E., et\u00a0al.: Global wheat head detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics (2020)","DOI":"10.34133\/2020\/3521852"},{"key":"47_CR6","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s007790170019","volume":"5","author":"AK Dey","year":"2001","unstructured":"Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5, 4\u20137 (2001)","journal-title":"Pers. Ubiquit. Comput."},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5) (2018)","DOI":"10.1145\/3236009"},{"key":"47_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.robot.2018.08.016","volume":"110","author":"M G\u00fcnther","year":"2018","unstructured":"G\u00fcnther, M., Ruiz-Sarmiento, J., Galindo, C., Gonz\u00e1lez-Jim\u00e9nez, J., Hertzberg, J.: Context-aware 3D object anchoring for mobile robots. Robot. Auton. Syst. 110, 12\u201332 (2018)","journal-title":"Robot. Auton. Syst."},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Gwon, C., Howell, S.C.: Odsmoothgrad: generating saliency maps for object detectors. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 3685\u20133689 (2023)","DOI":"10.1109\/CVPRW59228.2023.00376"},{"key":"47_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Hong, J.Y., Suh, E.H., Kim, S.J.: Context-aware systems: a literature review and classification. Expert Syst. Appl. 36(4), 8509\u20138522 (2009)","DOI":"10.1016\/j.eswa.2008.10.071"},{"issue":"4\u20135","key":"47_CR12","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1159\/000515642","volume":"50","author":"W Krackov","year":"2021","unstructured":"Krackov, W., Sor, M., Razdan, R., Zheng, H., Kotanko, P.: Artificial intelligence methods for rapid vascular access aneurysm classification in remote or in-person settings. Blood Purif. 50(4\u20135), 636\u2013641 (2021)","journal-title":"Blood Purif."},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, Z., Shrivastava, A., Davis, L.S.: Rethinking pseudo labels for semi-supervised object detection. In: Proceedings of AAAI, vol.\u00a036, pp. 1314\u20131322 (2022)","DOI":"10.1609\/aaai.v36i2.20019"},{"key":"47_CR14","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/s11704-019-8452-2","volume":"13","author":"YF Li","year":"2019","unstructured":"Li, Y.F., Liang, D.M.: Safe semi-supervised learning: a brief introduction. Front. Comput. Sci. 13, 669\u2013676 (2019)","journal-title":"Front. Comput. Sci."},{"key":"47_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"47_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105760","volume":"178","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Young, S.: A survey of public datasets for computer vision tasks in precision agriculture. Comput. Electron. Agric. 178, 105760 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"47_CR17","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s10846-019-01136-5","volume":"99","author":"R Martins","year":"2020","unstructured":"Martins, R., Bersan, D., Campos, M.F., Nascimento, E.R.: Extending maps with semantic and contextual object information for robot navigation: a learning-based framework using visual and depth cues. J. Intell. Robot. Syst. 99, 555\u2013569 (2020)","journal-title":"J. Intell. Robot. Syst."},{"key":"47_CR18","unstructured":"Monarch, R.M.: Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI. Simon and Schuster (2021)"},{"key":"47_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1\u201315 (2018)","journal-title":"Digit. Signal Process."},{"issue":"4","key":"47_CR20","doi-asserted-by":"publisher","first-page":"3005","DOI":"10.1007\/s10462-022-10246-w","volume":"56","author":"E Mosqueira-Rey","year":"2023","unstructured":"Mosqueira-Rey, E., Hern\u00e1ndez-Pereira, E., Alonso-R\u00edos, D., Bobes-Bascar\u00e1n, J., Fern\u00e1ndez-Leal, \u00c1.: Human-in-the-loop machine learning: a state of the art. Artif. Intell. Rev. 56(4), 3005\u20133054 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Muhammad, M.B., Yeasin, M.: Eigen-cam: class activation map using principal components. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206626"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., Ferrari, V.: We don\u2019t need no bounding-boxes: training object class detectors using only human verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.99"},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Petsiuk, V., et al.: Black-box explanation of object detectors via saliency maps. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11443\u201311452 (2021)","DOI":"10.1109\/CVPR46437.2021.01128"},{"key":"47_CR24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"47_CR25","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Salazar-Gomez, A., Darbyshire, M., Gao, J., Sklar, E.I., Parsons, S.: Towards practical object detection for weed spraying in precision agriculture. arXiv preprint arXiv:2109.11048 (2021)","DOI":"10.1109\/IROS47612.2022.9982139"},{"key":"47_CR27","doi-asserted-by":"crossref","unstructured":"Sarkar, S., Majumder, S., Koehler, J.L., Landman, S.R.: An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors. Heart Rhythm O2 4(1), 51\u201358 (2023)","DOI":"10.1016\/j.hroo.2022.10.014"},{"key":"47_CR28","doi-asserted-by":"publisher","unstructured":"Sekachev, B., et al.: opencv\/cvat: v1.1.0 (2020). https:\/\/doi.org\/10.5281\/zenodo.4009388","DOI":"10.5281\/zenodo.4009388"},{"key":"47_CR29","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Shafti, A., Orlov, P., Faisal, A.A.: Gaze-based, context-aware robotic system for assisted reaching and grasping. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 863\u2013869. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793804"},{"key":"47_CR31","doi-asserted-by":"publisher","unstructured":"Shen, Z., Zhang, R., Dell, M., Lee, B.C.G., Carlson, J., Li, W.: Layoutparser: a unified toolkit for deep learning based document image analysis. In: Llados, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 131\u2013146. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86549-8_9","DOI":"10.1007\/978-3-030-86549-8_9"},{"key":"47_CR32","doi-asserted-by":"crossref","unstructured":"Sreeram, M., Nof, S.Y.: Human-in-the-loop: role in cyber physical agricultural systems. Int. J. Comput. Commun. Control 16(2) (2021)","DOI":"10.15837\/ijccc.2021.2.4166"},{"issue":"5","key":"47_CR33","doi-asserted-by":"publisher","first-page":"e193963","DOI":"10.1001\/jamanetworkopen.2019.3963","volume":"2","author":"RW Stidham","year":"2019","unstructured":"Stidham, R.W., et al.: Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw. Open 2(5), e193963\u2013e193963 (2019)","journal-title":"JAMA Netw. Open"},{"key":"47_CR34","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319\u20133328. PMLR (2017)"},{"key":"47_CR35","doi-asserted-by":"crossref","unstructured":"Tsiakas, K., Murray-Rust, D.: Using human-in-the-loop and explainable AI to envisage new future work practices. In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 588\u2013594 (2022)","DOI":"10.1145\/3529190.3534779"},{"key":"47_CR36","unstructured":"Tzutalin: Labelimg. Free Software: MIT License (2015). https:\/\/github.com\/tzutalin\/labelImg"},{"key":"47_CR37","unstructured":"Wada, K.: labelme: Image Polygonal Annotation with Python (2016). https:\/\/github.com\/wkentaro\/labelme"},{"issue":"1","key":"47_CR38","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/COMST.2014.2381246","volume":"18","author":"\u00d6 Y\u00fcr\u00fcr","year":"2014","unstructured":"Y\u00fcr\u00fcr, \u00d6., Liu, C.H., Sheng, Z., Leung, V.C., Moreno, W., Leung, K.K.: Context-awareness for mobile sensing: a survey and future directions. IEEE Commun. Surv. Tutor. 18(1), 68\u201393 (2014)","journal-title":"IEEE Commun. Surv. Tutor."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence for Neuroscience and Emotional Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61140-7_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:35:26Z","timestamp":1717054526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61140-7_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031611391","9783031611407"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61140-7_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olh\u00e2o","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwinac2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.eu\/iwinac.org\/iwinac2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}