- -

Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Chaves;Saikia;Fer ...
Tamaño: 16.77Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Chaves, Deisy es_ES
dc.contributor.author Saikia, Surajit es_ES
dc.contributor.author Fernández-Robles, Laura es_ES
dc.contributor.author Alegre, Enrique es_ES
dc.contributor.author Trujillo, Maria es_ES
dc.date.accessioned 2020-05-13T19:49:34Z
dc.date.available 2020-05-13T19:49:34Z
dc.date.issued 2018-06-22
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/143094
dc.description.abstract [EN] Currently, many applications require a precise localization of the objects that appear in an image, to later process them. This is the case of visual inspection in the industry, computer-aided clinical diagnostic systems, the obstacle detection in vehicles or in robots, among others. However, several factors such as the quality of the image and the appearance of the objects to be detected make this automatic location difficult. In this article, we carry out a systematic revision of the main methods used to locate objects by considering since the methods based on sliding windows, as the detector proposed by Viola and Jones, until the current methods that use deep learning networks, such as Faster-RCNN or Mask-RCNN. For each proposal, we describe the relevant details, considering their advantages and disadvantages, as well as the main applications of these methods in various areas. This paper aims to provide a clean and condensed review of the state of the art of these techniques, their usefulness and their implementations in order to facilitate their knowledge and use by any researcher that requires locating objects in digital images. We conclude this work by summarizing the main ideas presented and discussing the future trends of these methods. es_ES
dc.description.abstract [ES] Actualmente, muchas aplicaciones requieren localizar de forma precisa los objetos que aparecen en una imagen, para su posterior procesamiento. Este es el caso de la inspección visual en la industria, los sistemas de diagnóstico clínico asistido por computador, la detección de obstáculos en vehículos o en robots, entre otros. Sin embargo, diversos factores como la calidad de la imagen y la apariencia de los objetos a detectar, dificultan la localización automática. En este artículo realizamos una revisión sistemática de los principales métodos utilizados para localizar objetos, considerando desde los métodos basados en ventanas deslizantes, como el detector propuesto por Viola y Jones, hasta los métodos actuales que usan redes de aprendizaje profundo, tales como Faster-RCNNo Mask-RCNN. Para cada propuesta, describimos los detalles relevantes, considerando sus ventajas y desventajas, así como sus aplicaciones en diversas áreas. El artículo pretende proporcionar una revisión ordenada y condensada del estado del arte de estas técnicas, su utilidad y sus implementaciones a fin de facilitar su conocimiento y uso por cualquier investigador que requiera localizar objetos en imágenes digitales. Concluimos este trabajo resumiendo las ideas presentadas y discutiendo líneas de trabajo futuro. es_ES
dc.description.sponsorship Este trabajo ha sido financiado parcialmente por diferentes instituciones. Deisy Chaves cuenta con una beca “Estudios de Doctorado en Colombia 2013” de COLCIENCIAS. Surajit Saikia cuenta con una beca de la Junta de Castilla y León con referencia EDU/529/2017. También queremos agradecer el apoyo de INCIBE (Instituto Nacional de Ciberseguridad) mediante la Adenda 22 al convenio con la Universidad de León. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Detection algorithms es_ES
dc.subject Image processing es_ES
dc.subject Machine learning es_ES
dc.subject Object recognition es_ES
dc.subject Pattern recognition es_ES
dc.subject Algoritmos de detección es_ES
dc.subject Aprendizaje máquina es_ES
dc.subject Procesamiento de imágenes es_ES
dc.subject Reconocimiento de objetos es_ES
dc.subject Reconocimiento de patrones es_ES
dc.title Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes es_ES
dc.title.alternative A Systematic Review on Object Localisation Methods in Images es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2018.10229
dc.relation.projectID info:eu-repo/grantAgreement/Junta de Castilla y León//EDU%2F529%2F2017 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Chaves, D.; Saikia, S.; Fernández-Robles, L.; Alegre, E.; Trujillo, M. (2018). Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes. Revista Iberoamericana de Automática e Informática industrial. 15(3):231-242. https://doi.org/10.4995/riai.2018.10229 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2018.10229 es_ES
dc.description.upvformatpinicio 231 es_ES
dc.description.upvformatpfin 242 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 15 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\10229 es_ES
dc.contributor.funder Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia es_ES
dc.contributor.funder Junta de Castilla y León es_ES
dc.description.references 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. es_ES
dc.description.references Alexe, B., Deselaers, T., Ferrari, V., 2010. What is an object? In: CVPR. pp.73-80. es_ES
dc.description.references 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 es_ES
dc.description.references Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A training algorithm for opti-mal margin classifiers. In: COLT. pp. 144-152. es_ES
dc.description.references Brazil, G., Yin, X., Liu, X., 2017. Illuminating pedestrians via simultaneous detection & segmentation. CoRR abs/1706.08564. es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references Dai, J., Li, Y., He, K., Sun, J., 2016. R-FCN: object detection via region-based fully convolutional networks. CoRR abs/1605.06409. es_ES
dc.description.references 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 es_ES
dc.description.references Deng, L., 2014. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing 3, e2. es_ES
dc.description.references Deng, L., Yu, D., 2014. Deep learning: Methods and applications. Foundations and Trends in Signal Processing 7 (3-4), 197-387. es_ES
dc.description.references Dollár, P., Tu, Z., Perona, P., Belongie, S. J., 2009. Integral channel features. In: BMVC. pp. 1-11. es_ES
dc.description.references Dollar, P., Zitnick, L., 2013. Structured forests for fast edge detection. In: ICCV. pp. 1841-1848. es_ES
dc.description.references Donoser, M., Bischof, H., 2006. Efficient maximally stable extremal region (mser) tracking. In: CVPR. pp. 553-560. DOI:10.1109/CVPR.2006.107 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Freund, Y., Schapire, R. E., 1999. A short introduction to boosting. In: IJCAI. pp. 1401-1406. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Girshick, R. B., 2015. Fast R-CNN. CoRR abs/1504.08083. es_ES
dc.description.references Girshick, R. B., Donahue, J., Darrell, T., Malik, J., 2013. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRRabs/1311.2524. es_ES
dc.description.references 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. es_ES
dc.description.references He, K., Gkioxari, G., Doll ́ar, P., Girshick, R. B., 2017. Mask R-CNN. CoRRabs/1703.06870. es_ES
dc.description.references He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: CVPR. pp. 770-778. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In: Adv Neural Inf Process Syst. pp. 1097-1105. es_ES
dc.description.references 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 es_ES
dc.description.references Lecun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436-444. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references Luo, S., Lu, H., Xiao, J., Yu, Q., Zheng, Z., 2017. Robot detection and localization based on deep learning. In: CAC. pp. 7091-7095. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Redmon, J., Divvala, S. K., Girshick, R. B., Farhadi, A., 2015. You only look once: Unified, real-time object detection. CoRR abs/1506.02640. es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556. es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references Wang, S., Cheng, J., Liu, H., Tang, M., 2018. Pcn: Part and context information for pedestrian detection with cnns. CoRR abs/1804.04483. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Zeiler, M. D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: ECCV. pp. 818-833. es_ES
dc.description.references Zhang, L., Lin, L., Liang, X., He, K., 2016. Is faster r-cnn doing well for pedestrian detection? In: ECCV. pp. 443-457. es_ES
dc.description.references 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 es_ES
dc.description.references Zitnick, L., Dollar, P., 2014. Edge boxes: Locating object proposals from edges. In: ECCV. pp. 391-405. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem