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深度卷积神经网络在计算机视觉中的应用 被引量:6

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摘要 随着科学技术水平的发展,大数据时代随之而来,使深度卷积神经网络具备更加丰富的网络结构,与传统的机器学习相比,在特征表达与特征学习方面更具优势。以深度学习算法深度卷积神经网络模型为基础所提出的计算机视觉领域在识别能力上取得了显著成绩。本文主要对深度卷积神经网络在计算机视觉中的应用进行探讨。
作者 孔峻
机构地区 西南林业大学
出处 《电子技术与软件工程》 2018年第21期130-130,131,共2页 ELECTRONIC TECHNOLOGY & SOFTWARE ENGINEERING
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  • 1Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60 (2) 91 110.
  • 2Dalai N, Triggs B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society Conference on. San Diego, USA: IEEE, 2005, 1 886-893.
  • 3Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786) : 504-507.
  • 4Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the catrs visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154.
  • 5Fukushima K, Miyake S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in posi- tion[J]. Pattern Recognition, 1982, 15(6): 455-469.
  • 6Ruck D W, Rogers S K, Kabrisky M. Feature selection using a multilayer perceptron[J]. Journal of Neural Network Com- puting, 1990, 2(2): 40-48.
  • 7Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986,3231 533 538.
  • 8LeCun Y, Denker J S, Henderson D, et al. Handwritten digit recognition with a back-propagation network[C]//Advances in Neural Information Processing Systems. Colorado, USA Is. n. ], 1990: 396-404.
  • 9LeCun Y, Cortes C. MNIST handwritten digit database[EB/OL], http//yann, lecun, com/exdb/mnist, 2010.
  • 10Waibe[ A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks[J]. Acoustics, Speech and Signal Processing, IEEF. Transactions on, 1989, 37(3): 328-339.

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