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Volumn , Issue , 2014, Pages

Generic object detection with dense neural patterns and regionlets

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; DEEP NEURAL NETWORKS; FEATURE EXTRACTION; NEURAL NETWORKS; OBJECT RECOGNITION;

EID: 85064926099     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/c.28.72     Document Type: Conference Paper
Times cited : (22)

References (23)
  • 1
    • 33947167478 scopus 로고    scopus 로고
    • Face description with local binary patterns: Application to face recognition
    • Timo Ahonen, Abdenour Hadid, and Matti Pietikäinen. Face description with local binary patterns: Application to face recognition. T-PAMI, 2006.
    • (2006) T-PAMI
    • Ahonen, T.1    Hadid, A.2    Pietikäinen, M.3
  • 2
    • 84866688216 scopus 로고    scopus 로고
    • Measuring the objectness of image windows
    • Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari. Measuring the objectness of image windows. T-PAMI, 2012.
    • (2012) T-PAMI
    • Alexe, B.1    Deselaers, T.2    Ferrari, V.3
  • 3
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 4
    • 80052876786 scopus 로고    scopus 로고
    • What does classifying more than 10,000 image categories tell us?
    • Jia Deng, Alexander C. Berg, Kai. Li, and Li Fei-Fei. What does classifying more than 10,000 image categories tell us? In ECCV, 2010.
    • (2010) ECCV
    • Deng, J.1    Berg, A.C.2    Li, K.3    Fei-Fei, L.4
  • 6
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • Federico Girosi,Michael Jones, and Tomaso Poggio. Regularization theory and neural networks architectures. Neural Computation, 1995.
    • (1995) Neural Computation
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 7
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 2014.
    • (2014) CVPR
    • Girshick, R.B.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 9
    • 0014266913 scopus 로고
    • Receptive fields and functional architecture of monkey striate cortex
    • David H Hubel and Torsten N Wiesel. Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology, 1968.
    • (1968) The Journal of Physiology
    • Hubel, D.H.1    Wiesel, T.N.2
  • 10
    • 77953183471 scopus 로고    scopus 로고
    • What is the best multi-stage architecture for object recognition?
    • Kevin Jarrett, Koray Kavukcuoglu, Marc Aurelio Ranzato, and Yann LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.
    • (2009) ICCV
    • Jarrett, K.1    Kavukcuoglu, K.2    Ranzato, M.A.3    Le Yann, C.4
  • 11
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 15
    • 5044231640 scopus 로고    scopus 로고
    • Learning methods for generic object recognition with invariance to pose and lighting
    • Yann LeCun, Fu Jie Huang, and Leon Bottou. Learning methods for generic object recognition with invariance to pose and lighting. In CVPR, 2004.
    • (2004) CVPR
    • Le Yann, C.1    Huang, F.J.2    Bottou, L.3
  • 16
    • 51149113745 scopus 로고    scopus 로고
    • A sparse and locally shift invariant feature extractor applied to document images
    • MRanzato and Yann LeCun. A sparse and locally shift invariant feature extractor applied to document images. In ICDAR, 2007.
    • (2007) ICDAR
    • Ranzato, M.1    Le Yann, C.2
  • 17
    • 84906907367 scopus 로고    scopus 로고
    • Pedestrian detection with unsupervised multistage feature learning
    • Pierre Sermanet, Koray Kavukcuoglu, and Soumith Chintala. Pedestrian detection with unsupervised multistage feature learning. In CVPR, 2012.
    • (2012) CVPR
    • Sermanet, P.1    Kavukcuoglu, K.2    Chintala, S.3
  • 18
    • 84906486689 scopus 로고    scopus 로고
    • Overfeat: Integrated recognition, localization and detection using convolutional networks
    • abs/1312.6229
    • Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229, 2013.
    • (2013) CoRR
    • Sermanet, P.1    Eigen, D.2    Zhang, X.3    Mathieu, M.4    Fergus, R.5    Le Yann, C.6
  • 19
    • 50249124717 scopus 로고    scopus 로고
    • Pedestrian detection via classification on riemannian manifolds
    • Oncel Tuzel, Fatih Porikli, and Peter Meer. Pedestrian detection via classification on riemannian manifolds. T-PAMI, 2008.
    • (2008) T-PAMI
    • Tuzel, O.1    Porikli, F.2    Meer, P.3
  • 22
    • 0037562189 scopus 로고    scopus 로고
    • Robust real-time object detection
    • P. Viola and M. J. Jones. Robust real-time object detection. IJCV, 2001.
    • (2001) IJCV
    • Viola, P.1    Jones, M.J.2
  • 23
    • 84898769710 scopus 로고    scopus 로고
    • Regionlets for generic object detection
    • Xiaoyu Wang, Ming Yang, Shenghuo Zhu, and Yuanqing Lin. Regionlets for generic object detection. In ICCV, 2013.
    • (2013) ICCV
    • Wang, X.1    Yang, M.2    Zhu, S.3    Lin, Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.