{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:30:49Z","timestamp":1725946249306},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319747262"},{"type":"electronic","value":"9783319747279"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-74727-9_32","type":"book-chapter","created":{"date-parts":[[2018,1,25]],"date-time":"2018-01-25T08:12:11Z","timestamp":1516867931000},"page":"273-280","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DetectionEvaluationJ: A Tool to Evaluate Object Detection Algorithms"],"prefix":"10.1007","author":[{"given":"C.","family":"Dom\u00ednguez","sequence":"first","affiliation":[]},{"given":"M.","family":"Garc\u00eda","sequence":"additional","affiliation":[]},{"given":"J.","family":"Heras","sequence":"additional","affiliation":[]},{"given":"A.","family":"In\u00e9s","sequence":"additional","affiliation":[]},{"given":"E.","family":"Mata","sequence":"additional","affiliation":[]},{"given":"V.","family":"Pascual","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,1,26]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cmpb.2017.03.010","volume":"143","author":"A Alonso","year":"2017","unstructured":"Alonso, A., et al.: AntibiogramJ: a tool for analysing images from disk diffusion tests. Comput. Methods Program. Biomed. 143, 159\u2013169 (2017)","journal-title":"Comput. Methods Program. Biomed."},{"issue":"4","key":"32_CR2","doi-asserted-by":"crossref","first-page":"5:1","DOI":"10.1147\/JRD.2017.2708299","volume":"61","author":"N Codella","year":"2017","unstructured":"Codella, N., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4), 5:1\u20135:15 (2017)","journal-title":"IBM J. Res. Dev."},{"key":"32_CR3","unstructured":"Everingham, M., et al.: The PASCAL visual object classes challenge 2012 (VOC2012) results (2012). http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html"},{"key":"32_CR4","volume-title":"Professional XML","author":"B Evjen","year":"2007","unstructured":"Evjen, B., et al.: Professional XML. Wiley Publishing Inc., Hoboken (2007)"},{"issue":"3","key":"32_CR5","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.cmpb.2015.08.013","volume":"122","author":"F Ghasemian","year":"2015","unstructured":"Ghasemian, F., et al.: An efficient method for automatic morphological abnormality detection from human sperm images. Comput. Methods Program. Biomed. 122(3), 409\u2013420 (2015)","journal-title":"Comput. Methods Program. Biomed."},{"key":"32_CR6","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","volume":"56","author":"AS Glas","year":"2003","unstructured":"Glas, A.S., et al.: The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56, 1129\u20131135 (2003)","journal-title":"J. Clin. Epidemiol."},{"key":"32_CR7","unstructured":"Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016. arXiv preprint arXiv:1605.01397 (2016)"},{"key":"32_CR8","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-015-0703-0","volume":"16","author":"J Heras","year":"2015","unstructured":"Heras, J., et al.: GelJ - a tool for analyzing DNA fingerprint gel images. BMC Bioinf. 16, 270 (2015)","journal-title":"BMC Bioinf."},{"issue":"6","key":"32_CR9","first-page":"912","volume":"17","author":"J Heras","year":"2015","unstructured":"Heras, J., et al.: Surveying and benchmarking techniques to analyse DNA gel fingerprint images. Brief. Bioinf. 17(6), 912\u2013925 (2015)","journal-title":"Brief. Bioinf."},{"issue":"12","key":"32_CR10","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/TNNLS.2013.2270314","volume":"24","author":"SC Huang","year":"2013","unstructured":"Huang, S.C., Chen, B.H.: Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems. IEEE Trans. Neural Netw. Learn. Syst. 24(12), 1920\u20131931 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"32_CR11","volume-title":"Learning OpenCV 3","author":"A Kaehler","year":"2015","unstructured":"Kaehler, A., Bradski, G.: Learning OpenCV 3. O\u2019Reilly Media, Sebastopol (2015)"},{"issue":"5","key":"32_CR12","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jbi.2005.02.008","volume":"38","author":"TA Lasko","year":"2005","unstructured":"Lasko, T.A., et al.: The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inf. 38(5), 404\u2013415 (2005)","journal-title":"J. Biomed. Inf."},{"key":"32_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"32_CR14","first-page":"41","volume":"690","author":"G Mata","year":"2017","unstructured":"Mata, G., et al.: SynapCountJ: a validated tool for analyzing synaptic densities in neurons. Commun. Comput. Inf. Sci. 690, 41\u201355 (2017)","journal-title":"Commun. Comput. Inf. Sci."},{"key":"32_CR15","unstructured":"MathWorks: Matlab version 9.0.0.341360 (R2016a). The MathWorks Inc., Natick, Massachusetts (2016)"},{"issue":"1","key":"32_CR16","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TBME.2016.2535311","volume":"64","author":"JI Orlando","year":"2015","unstructured":"Orlando, J.I., et al.: A discriminatively trained fully connected conditional random field model for blood blessed segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16\u201327 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"32_CR17","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s13042-011-0012-5","volume":"2","author":"DMW Powers","year":"2011","unstructured":"Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2(1), 37\u201363 (2011)","journal-title":"Int. J. Mach. Learn. Technol."},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint arXiv:1612.08242 (2016)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"3","key":"32_CR19","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"issue":"7","key":"32_CR20","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/nmeth.2089","volume":"9","author":"CA Schneider","year":"2012","unstructured":"Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9(7), 671\u2013675 (2012)","journal-title":"Nature Methods"},{"issue":"6","key":"32_CR21","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"R Shaoqing","year":"2017","unstructured":"Shaoqing, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"32_CR22","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1109\/TITB.2012.2201949","volume":"16","author":"JS Silva","year":"2012","unstructured":"Silva, J.S., et al.: Algorithm versus physicians variability evaluation in the cardiac chambers extraction. IEEE Trans. Inf. Technol. Biomed. 16(5), 835\u2013841 (2012)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"32_CR23","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s10032-006-0014-0","volume":"8","author":"C Wolf","year":"2006","unstructured":"Wolf, C., Jolion, J.M.: Object count\/area graphs for the evaluation of object detection and segmentation algorithms. Int. J. Doc. Anal. Recogn. 8, 280\u2013296 (2006)","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"32_CR24","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/mice.12042","volume":"29","author":"E Zalama","year":"2014","unstructured":"Zalama, E., et al.: Road crack detection using visual features extracted by gabor filters. Comput. Aided Civil Infrastruct. Eng. 29, 342\u2013358 (2014)","journal-title":"Comput. Aided Civil Infrastruct. Eng."},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Zhai, M., et al.: Object detection in surveillance video from dense trajectories. In: 14th IAPR International Conference on Machine Vision Applications. IEEE (2015)","DOI":"10.1109\/MVA.2015.7153248"}],"container-title":["Lecture Notes in Computer Science","Computer Aided Systems Theory \u2013 EUROCAST 2017"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-74727-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T16:28:37Z","timestamp":1570638517000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-74727-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319747262","9783319747279"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-74727-9_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]}}}