Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E., 2016. A region based convolutional network for tumor detection and classification in breast mammography. In: Deep Learning and Data Labe-ling for Medical Applications. pp. 197-205.
Alexe, B., Deselaers, T., Ferrari, V., 2010. What is an object? In: CVPR. pp.73-80.
Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., Zuair, M., 2017.Deep learning approach for car detection in uav imagery. Remote Sens. 9 (4). DOI:10.3390/rs9040312
[+]
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E., 2016. A region based convolutional network for tumor detection and classification in breast mammography. In: Deep Learning and Data Labe-ling for Medical Applications. pp. 197-205.
Alexe, B., Deselaers, T., Ferrari, V., 2010. What is an object? In: CVPR. pp.73-80.
Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., Zuair, M., 2017.Deep learning approach for car detection in uav imagery. Remote Sens. 9 (4). DOI:10.3390/rs9040312
Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A training algorithm for opti-mal margin classifiers. In: COLT. pp. 144-152.
Brazil, G., Yin, X., Liu, X., 2017. Illuminating pedestrians via simultaneous detection & segmentation. CoRR abs/1706.08564.
Cai, Z., Fan, Q., Feris, R. S., Vasconcelos, N., 2016. A unified multi-scale deep convolutional neural network for fast object detection. CoRRabs/1607.07155.
Cao, X., Gong, G., Liu, M.,Qi, J., 2016. Foreign object debris detection on air-field pavement using region based convolution neural network. In: DICTA. pp. 1-6. DOI:10.1109/DICTA.2016.7797045
Cao, X., Wang, P., Meng, C., Bai, X., Gong, G., Liu, M., Qi, J., 2018. Region based cnn for foreign object debris detection on airfield pavement. Sensors18 (3). DOI:10.3390/s18030737
Chen, J., Liu, Z., Wang, H., Núñez, A., Han, Z., 2018. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE T Instrum Meas 67 (2), 257-269. DOI:10.1109/TIM.2017.2775345
Cireʂan, D. C., Giusti, A., Gambardella, L. M., Schmidhuber, J., 2013. Mitosis detection in breast cancer histology images with deep neural networks. In: MICCAI. pp. 411-418.
Coifman, B., McCord, M., Mishalani, R. G., Iswalt, M., Ji, Y., 2006. Roadway traffic monitoring from an unmanned aerial vehicle. IEE Proceedings - Intelligent Transport Systems 153 (1),11-20. DOI:10.1049/ip-its:20055014
Dai, J., Li, Y., He, K., Sun, J., 2016. R-FCN: object detection via region-based fully convolutional networks. CoRR abs/1605.06409.
Dalal, N., Triggs, B., June2005. Histograms of oriented gradients for human detection. In: CVPR. Vol. 1. pp. 886-893 vol. 1. DOI:10.1109/CVPR.2005.177
Deng, L., 2014. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing 3, e2.
Deng, L., Yu, D., 2014. Deep learning: Methods and applications. Foundations and Trends in Signal Processing 7 (3-4), 197-387.
Dollár, P., Tu, Z., Perona, P., Belongie, S. J., 2009. Integral channel features. In: BMVC. pp. 1-11.
Dollar, P., Zitnick, L., 2013. Structured forests for fast edge detection. In: ICCV. pp. 1841-1848.
Donoser, M., Bischof, H., 2006. Efficient maximally stable extremal region (mser) tracking. In: CVPR. pp. 553-560. DOI:10.1109/CVPR.2006.107
Du, X., El-Khamy, M., Lee, J., Davis, L., 2017. Fused dnn: A deep neural net-work fusion approach to fast and robust pedestrian detection. In: WACV. pp.953-961. DOI:10.1109/WACV.2017.111
Dženan, Z., Aleš, V., Jan, E., Daniel, H., Christopher, N., Andreas, K., 2014. Robust detection and segmentation for diagnosis of vertebral diseases using routine mr images. Computer Graphics Forum 33 (6), 190-204. DOI:10.1111/cgf.12343
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., Ramanan, D., 2010. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32 (9), 1627-1645. DOI:10.1109/TPAMI.2009.167
Felzenszwalb, P. F., Huttenlocher, D. P., 2004. Efficient graph-based image segmentation. IJCV 59 (2), 167-181. DOI:10.1023/B:VISI.0000022288.19776.77
Ferguson, M., Ak, R., Lee, Y. T. T., Law, K. H., 2017. Automatic localization of casting defects with convolutional neural networks. In: IEEE International Conference on Big Data. pp. 1726-1735. DOI:10.1109/BigData.2017.8258115
Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N., 2017a. Machine-vision-based identification of broken inserts in edge profile milling heads. Robot Comput Integr Manuf 44, 276 - 283. DOI:https://doi.org/10.1016/j.rcim.2016.10.004
Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N., Castejón-Limas ,M., 2017b. Identification of milling inserts in situ based on a versatile machine vision system. JMSY 45, 48 - 57. DOI: https://doi.org/10.1016/j.jmsy.2017.08.002
Freund, Y., Schapire, R. E., 1999. A short introduction to boosting. In: IJCAI. pp. 1401-1406.
García-Ordás, M. T., Alegre, E., González-Castro, V., Alaiz-Rodríguez, R.,2017. A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int J Adv Manuf Technol 90 (5), 1947-1961. DOI:10.1007/s00170-016-9541-0
García-Olalla, O., Alegre, E., Fernández-Robles, L., Fidalgo, E., Saikia, S., 2018. Textile retrieval based on image content from cdc and webcam cameras in indoor environments. Sensors 18 (5). DOI:10.3390/s18051329
Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., Goldner,V., Cohen, R., Horn, K., Levi, D., 2017. Real-time category-based and general obstacle detection for autonomous driving. In: ICCVW. pp. 198-205. DOI:10.1109/ICCVW.2017.32
Girshick, R. B., 2015. Fast R-CNN. CoRR abs/1504.08083.
Girshick, R. B., Donahue, J., Darrell, T., Malik, J., 2013. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRRabs/1311.2524.
He, B., Xiao, D., Hu, Q., Jia, F., 2018. Automatic magnetic resonance image prostate segmentation based on adaptive feature learning probability boos-ting tree initialization and cnn-asm refinement. IEEE Access 6, 2005-2015.
He, K., Gkioxari, G., Doll ́ar, P., Girshick, R. B., 2017. Mask R-CNN. CoRRabs/1703.06870.
He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: CVPR. pp. 770-778.
Heo, Y. J., Lee, D., Kang, J., Lee, K., Chung, W. K., 2017. Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip. Scientific Reports 7 (1), 11651. DOI:10.1038/s41598-017-11534-0
Hosang, J., Benenson, R., Doll ́ar, P., Schiele, B., 2016. What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38 (4),814-830. DOI:10.1109/TPAMI.2015.2465908
Jiamin, L., David, W., Le, L., Zhuoshi, W., Lauren, K., B., T. E., Berkman,S., A., P. N., M., S. R., 2017. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks. Medical Physics44 (9), 4630-4642. DOI:10.1002/mp.12399
Jung, F., Kirschner, M., Wesarg, S., 2013. A generic approach to organ detection using 3d haar-like features. In: Bildverarbeitung für die Medizin 2013.pp. 320-325.
Kisilev, P., Sason, E., Barkan, E., Hashoul, S., 2016. Medical image description nusing multi-task-loss cnn. In: Deep Learning and Data Labeling for Medical Applications. pp. 121-129.
Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In: Adv Neural Inf Process Syst. pp. 1097-1105.
Lampert, C. H., Blaschko, M. B., Hofmann, T., 2008. Beyond sliding windows: Object localization by efficient subwindow search. In: CVPR. pp. 1-8. DOI:10.1109/CVPR.2008.4587586
Lecun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436-444.
Lee, C. J., Tseng, T. H., Huang, B. J., Jun-Weihsieh, Tsai, C. M., 2015. Obstacle detection and avoidance via cascade classifier for wheeled mobile robot. In: ICMLC. Vol. 1. pp. 403-407. DOI:10.1109/ICMLC.2015.7340955
Lee, J., Wang, J., Crandall, D., Šabanovic, S., Fox, G., 2017. Real-time, cloud-based object detection for unmanned aerial vehicles. In: IRC. pp. 36-43. DOI:10.1109/IRC.2017.77
Levi, D., Garnett, N., Fetaya, E., September 2015a. Stixelnet: A deep convolutional network for obstacle detection and road segmentation. In: BMVC. pp. 109.1-109.12. DOI:10.5244/C.29.109
Levi, D., Garnett, N., Fetaya, E., 2015b. Stixelnet: A deep convolutional network for obstacle detection and road segmentation. In: BMVC. pp. 109.1-109.12. DOI:10.5244/C.29.109
Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S., 2018. Scale-aware fast r-cnn for pedestrian detection. IEEE Trans Multimedia 20 (4), 985-996. DOI:10.1109/TMM.2017.2759508
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.,2016. Ssd: Single shot multibox detector. In: ECCV. pp. 21-37.
Luo, S., Lu, H., Xiao, J., Yu, Q., Zheng, Z., 2017. Robot detection and localization based on deep learning. In: CAC. pp. 7091-7095.
Ma, Y., Jiang, Z., Zhang, H., Xie, F., Zheng, Y., Shi, H., 2017. Proposing regions from histopathological whole slide image for retrieval using selective search. In: ISBI. pp. 156-159. DOI:10.1109/ISBI.2017.7950491
Mery, D., Rio, V., Zscherpel, U., Mondrag ́on, G., Lillo, I., Zuccar, I., Lobel,H., Carrasco, M., 2015. Gdxray: The database of x-ray images for nondestructive testing. Journal of Nondestructive Evaluation 34 (4), 42. DOI:10.1007/s10921-015-0315-7
Park, J.-K., Kwon, B.-K., Park, J.-H., Kang, D.-J., 2016. Machine learning-based imaging system for surface defect inspection. IJPEM-GT 3 (3), 303-310. DOI:10.1007/s40684-016-0039-x
Redmon, J., Divvala, S. K., Girshick, R. B., Farhadi, A., 2015. You only look once: Unified, real-time object detection. CoRR abs/1506.02640.
Ren, S., He, K., Girshick, R. B., Sun, J., 2015. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497.
Říha, K., Mašek, J., Burget, R., Beneš, R., Závodná, E., 2013. Novel method for localization of common carotid artery transverse section in ultrasound images using modified viola-jones detector. Ultrasound Med Biol 39 (10),1887 - 1902. DOI:10.1016/j.ultrasmedbio.2013.04.013
Sa, R., Owens, W., Wiegand, R., Studin, M., Capoferri, D., Barooha, K.,Greaux, A., Rattray, R., Hutton, A., Cintineo, J., Chaudhary, V., 2017. Intervertebral disc detection in x-ray images using faster r-cnn. In: EMBC. pp. 564-567. DOI:10.1109/EMBC.2017.8036887
Saikia, S., Fidalgo, E., Alegre, E., Fernández-Robles, L., 2017. Object detection for crime scene evidence analysis using deep learning. In: ICIAP. pp.14-24.
Sepúlveda, G. V., Torriti, M. T.,Calero, M. F., 2017. Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado. Revista Iberoamericana de Automática e Informática Industrial14 (2), 152-162. DOI:10.1016/j.riai.2016.09.010
Shah, V. R., Maru, S. V., Jhaveri, R. H., 2018. An obstacle detection scheme for vehicles in an intelligent transportation system. IJCNIS 8 (10), 23-28. DOI:10.5815/ijcnis.2016.10.03
Shi, Y., Li, Y., Wei, X., Zhou, Y., 2017. A faster-rcnn based chemical fiber paper tube defect detection method. In: International Conference on Enterprise Systems. pp. 173-177. DOI:10.1109/ES.2017.35
Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556.
Szegedy, C., Ioe, S., Vanhoucke, V., Alemi, A. A., 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI. pp. 4278-4284.
Tang, T., Zhou, S., Deng, Z., Zou, H., Lei, L., 2017. Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors 17 (2). DOI:10.3390/s17020336
Tek, F., 2013. Mitosis detection using generic features and an ensemble of cascade adaboosts. J Pathol Inform 4 (1), 12. DOI:10.4103/2153-3539.112697
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., Smeulders, A. W. M. ,2013. Selective search for object recognition. IJCV 104 (2), 154-171.
Viola, P., Jones, M. J., May 2004. Robust real-time face detection. IJCV 57 (2), 137-154 .DOI:10.1023/B:VISI.0000013087.49260.fb
Wang, S., Cheng, J., Liu, H., Tang, M., 2018. Pcn: Part and context information for pedestrian detection with cnns. CoRR abs/1804.04483.
Xu, Y., Yu, G., Wang, Y., Ma, Y., 2017a. Car detection from low-altitude uav imagery with the faster r-cnn. JAT 2017. DOI:https://doi.org/10.1155/2017/2823617
Xu, Y., Yu, G., Wang, Y., Wu, X., Ma, Y., 2016. A hybrid vehicle detection method based on viola-jones and hog+svm from uav images. Sensors 16 (8). DOI:10.3390/s16081325
Xu, Y., Yu, G., Wu, X., Wang, Y., Ma, Y., 2017b. An enhanced viola-jones vehicle detection method from unmanned aerial vehicles imagery. IEEE trans Intell Transp Syst 18 (7), 1845-1856. DOI:10.1109/TITS.2016.2617202
Yang, S., Fang, B., Tang, W., Wu, X., Qian, J., Yang, W., 2017. Faster r-cnn based microscopic cell detection. In: SPAC. pp. 345-350. DOI:10.1109/SPAC.2017.8304302
Yi, X., Song, G., Derong, T., Dong, G., Liang, S., Yuqiong, W., 2018. Fast road obstacle detection method based on maximally stable extremal regions. IJARS 15 (1), 1-10. DOI:10.1177/1729881418759118
Zeiler, M. D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: ECCV. pp. 818-833.
Zhang, L., Lin, L., Liang, X., He, K., 2016. Is faster r-cnn doing well for pedestrian detection? In: ECCV. pp. 443-457.
Zhong, J., Lei, T., Yao, G., 2017. Robust vehicle detection in aerial images based on cascaded convolutional neural networks. Sensors 17 (12). DOI:10.3390/s17122720
Zitnick, L., Dollar, P., 2014. Edge boxes: Locating object proposals from edges. In: ECCV. pp. 391-405.
[-]
Chaves, Deisy
Saikia, Surajit
Fernández-Robles, Laura
Alegre, Enrique
Trujillo, Maria
