科学网—深度学习在目标视觉检测中的应用进展与展望(6)

6 Uijlings J R R, van de Sande K E A, Gevers T, Smeulders A W M. Selective search for object recognition. International Journal of Computer Vision, 2013, 104(2): 154-171.

title:faster r-cnn: towards real-time object detection with region proposal networksabstract: state-of-the-art object detection networks depend on region proposalalgorithms to hypothesize object locations。6.zheng wm, zhou xy, zou cr, facial expression recognition using kernel canonical correlation analysis ,ieee transactions on neural networks ,vol.17,pp.233-238,2005。[10] ahonen t, hadid a. and pietikäinen m. “face description with local binary patterns: application to face recognition.” ieee transactions on pattern analysis and machine intelligence, 28(12):2037-2041.。

[2] n. dalal, b. triggs,histograms of oriented gradients for human detection, proc. ieee conf. computer vision and pattern recognition, 2005.。[26] krizhevsky, a. parallelizing convolutional neural networks. in tutorial of ieee conference on computer vision and pattern recognition (cvpr 2014). (columbus, ohio, usa, june 23-28, 2014). 2014.。[1] dalal, n. and b. triggs. "histograms oforiented gradients for human detection",ieee computer society conference on computer vision and pattern recognition,vol. 1 (june 2005), pp. 886–893.。

9 Lampert C H, Blaschko M B, Hofmann T. Beyond sliding windows: object localization by efficient subwindow search. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Anchorage, Alaska, USA: IEEE, 2008. 1-8

10 An S J, Peursum P, Liu W Q, Venkatesh S. Efficient algorithms for subwindow search in object detection and localization. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami, Florida, USA: IEEE, 2009. 264-271

[1]shi, wenzhe, et al. “real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.” proceedings of the ieee conference on computer vision and pattern recognition. 2016.。[26] krizhevsky, a. parallelizing convolutional neural networks. in tutorial of ieee conference on computer vision and pattern recognition (cvpr 2014). (columbus, ohio, usa, june 23-28, 2014). 2014.。【3】j. redmon, s. divvala, r. girshick, and a. farhadi, “you only look once: unified, real-time object detection,” in proceedings of the ieee conference on computer vision and pattern recognition, 2016, pp. 779–788.。

12 Van de Sande K E A, Uijlings J R R, Gevers T, Smeulders A W M. Segmentation as selective search for object recognition. In: Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV). Barcelona, Spain: IEEE, 2011. 1879-1886

13 Shotton J, Blake A, Cipolla R. Multiscale categorical object recognition using contour fragments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(7): 1270-1281.

14 Leibe B, Leonardis A, Schiele B. Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision, 2008, 77(1-3): 259-289.

09: 1072-1085. [10]yu jianxing, li zhibo, yang yuan, et al. collapse analysis of imperfect subsea pipelines based on 2d high-order nonlinear model [j]. transactions of tianjin university. 2014。[6]chen zhihua图像处理 目标识别，qiao wentao，wang xiaodun. seismic response analysis of long-span suspen-dome under multi-support excitations[j].transactions of tianjin university，2010，16(6):424-432. （ei: **072）。transactions onpattern analysis andmachine intelligence。

16 Shotton J, Winn J, Rother C, Criminisi A. TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Proceedings of the 9th European Conference on Computer Vision (ECCV). Berlin, Heidelberg, Germany: Springer, 2006. 1-15

17 Verbeek J, Triggs B. Region classification with Markov field aspect models. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis, Minnesota, USA: IEEE, 2007. 1-8

18 Cheng M M, Zhang Z M, Lin W Y, Torr P. BING: binarized normed gradients for objectness estimation at 300fps. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, USA: IEEE, 2014. 3286-3293

harvey i., and bossomaier t.r.j, (1997). time out of joint: attractors in asynchronous boolean networks. in husbands and harvey (eds.), proceedings of the fourth european conference on artificial life, 67-75, mit press.。1. ying xu, biqin dong, yi jiang，“3d delamination patch detection in laminate composites with an novel nde technique”，in proceedings of 15th european conference on composite materials (eccm15), venice, italy, 24-28 june 2012。chunxiang zhu, shengdong zhang, sin jko, mok pkt，”ultra-thin elevated channel low temperature poly-si tfts for fully-integrated amlcd systems on glass,” proceedings of the 29th european solid-state device research conference.essderc’99. editions frontieres. 1999, pp. 708-11. neuilly sur seine, france.。

20 Hosang J, Benenson R, Schiele B. How good are detection proposals, really? arXiv:1406.6962, 2014.