물체 검출과 얼굴 인식을 위한 딥러닝 기법 = Deep learning method for object detection and human face recognition

안영신 2020년
논문상세정보
' 물체 검출과 얼굴 인식을 위한 딥러닝 기법 = Deep learning method for object detection and human face recognition' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • fr
  • hog
  • roi
  • ssd
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
185 0

0.0%

' 물체 검출과 얼굴 인식을 위한 딥러닝 기법 = Deep learning method for object detection and human face recognition' 의 참고문헌

  • 딥러닝 기반 고성능 얼굴인식 기술 동향
    김형일 문진영 박종열 한국전자통신연구원 [2018]
  • 「패턴인식」
    오일석 교보문고 [2008]
  • 「파이썬으로 만드는 OpenCV 프로젝트」
    이세우 프로그래밍 인사 이트 [2019]
  • 「컴퓨터 비전」
    오일석 한빛아카데미 [2014]
  • 「밑바닥부터 시작하는 딥러닝(Deep Learning from scratch)」
    고키 사이토 한빛미디어(O’REILLY) [2017]
  • 「Template Matching Techniques inComputer Vision : Theory and Practice」
    [2009]
  • 「OpenCV 4로 배우는 컴퓨터 비전과 머신 러닝」
    황선규 길벗 출 판사 [2019]
  • 「OpenCV 3Computer Vision with PythonCookbook」
    [2018]
  • 「Handbook of Face Recognition」
    [2005]
  • the free encyclopedia
    https : //en.wikipedia.org/wiki/Haar-like_feature , last edited on 6
  • darkpgmr
    ‘영상 feature 비교’ https://darkpgmr.tistory.com/116 [2014]
  • [8] Convolutional neural network, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Convolutional_neural_network, last edited on 12 June 2020.
  • [88] Created by Yangqing Jia, Lead Developer Evan Shelhamer, ‘Deep learning framework by BAIR’, http://caffe.berkeleyvision.org/, last accessed on 15 June 2020.
  • [85] Application programming interface, From Wikipedia, https://en.wikipedia.org/wiki/Application_programming_interface, last edited on 9 June 2020.
  • [84] Sobel Operator, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Sobel_operator, last edited on 3 June 2020.
  • [82] scikit-image development team homepage, ‘scikit-image’, https://scikit-image.org/docs/dev/install.html, last accessed on 15 June 2020.
  • [81] OpenCV team, ‘OpcnCV’, https://opencv.org/, 2020.
  • [80] Python Software Foundation, https://www.python.org/, last accessed on 15 June 2020.
  • [79] Project Jupyter, ‘Jupyter Notebook’, https://jupyter.org/, last updated on 16 May 2020.
  • [77] Labeled Faces in the Wild homepage, http://vis-www.cs.umass.edu/lfw/, last accessed on 10 June 2020.
  • [75] Region of interest, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Region_of_interest, last edited on 25 January 2020.
  • [73] OpenCV 2.4.13.7 documentation, ‘Affine Transformations’, https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/warp_affine/w arp_affine.html, last accessed on 14 June 2020.
  • [70] OpenFace, https://cmusatyalab.github.io/openface, last accessed on 2 March 2020.
  • [66] Jaccard index, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Jaccard_index, last edited on 7 June 2020.
  • [62] Ground truth, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Ground_truth, last edited on 13 May 2020.
  • [61]Confusion matrix, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Confusion_matrix, last edited on 9 June 2020.
  • [59] Frame rate, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Frame_rate, last edited on 16 May 2020.
  • [55] Satya Mallick homepage, ‘Learn OpenCV’, https://www.learnopencv.com/histogram-of-oriented-gradients/, last edited on 6 December 2016.
    https : //www.learnopencv.com/histogram-of-oriented-gradients/ , last edited on 6
  • [54] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," CVPR 2005, IEEE Computer Society Conference, Vol. 1, pp. 886–893, 2015.
  • [50] Difference of Gaussians, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Difference_of_Gaussians, last edited on 15 May 2020.
  • [46] Dilution (neural networks), From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Dilution_(neural_networks), last edited on 12 June 2020.
  • [43] Visual Geometry Group, https://www.robots.ox.ac.uk/~vgg/, last accessed on 15 June 2020.
  • [3] Face Detection & Recognition, https://facedetection.com, last edited on 2020.
  • [38] Sigmoid function, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Sigmoid_function, last edited on 10 June 2020.
  • [37] Diederik P. Kingma, Jimmy Lei Ba, "Adam: A Method for Stochastic Optimization", https://arxiv.org/pdf/1412.6980.pdf, 2014.
  • [34] Affine transformation, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Affine_transformation, last edited on 17 May 2020.
  • [32] Andrej Karpathy's blog, ‘Hacker's guide to Neural Networks’, http://karpathy.github.io/neuralnets/, last accessed on 16 June 2020.
  • [30] Stochastic gradient descent, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Stochastic_gradient_descent, last edited on 7 June 2020.
  • [27] Gradient descent, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Gradient_descent, last edited on 11 June 2020.
  • [26] Random search, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Random_search, last edited on 19 April 2020.
  • [25] Dankmar Böhning, "Multinomial logistic regression algorithm", Annals of the Institute of Statistical Mathematics, Vol. 44, pp. 197– 200, 1992.
  • [22] Training, validation, and test sets, From Wikipedia, https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets, last edited on 2 June 2020.
  • [21] Hinge loss, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Hinge_loss, last edited on 9 April 2020.
  • [20] Support vector machine, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Support_vector_machine, last edited on 18 May 2020.
  • [1] Facial recognition system, From Wikipedia, the free encyclopedia, https://en.wikipedia.org/wiki/Facial_recognition_system, last edited on 15 May 2020.
  • [18] Christopher J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167, 1998.
  • [16] CS231n: Stanford University, ‘CS231n: Convolutional Neural Networks for visual recognition’, http://cs231n.stanford.edu/, last accessed on 15 June 2020.
  • [13] Adam Geitgey’s FACE_RECOGNITION Library homepage, https://github.com/ageitgey/face_recognition, last accessed on 2 March 2020.
  • [12] Dlib C++ Library homepage, http://dlib.net/, last modified on 6 June 2020.
  • [10] Stanford Vision Lab, Large Scale Visual Recognition Challenge (ILSVRC), http://www.image-net.org/challenges/LSVRC/, 2015.
  • You only look once : Unified , real-time object detection
    pp . 779-788 [2016]
  • Very Deep Convolutional Networks for Large-scale Image Recognition
    https : //arxiv.org/abs/1409.1556 [2014]
  • VGG16 – Convolutional Network for Classification and Detection
    https : //neurohive.io/en/popular-networks/vgg16/ [2018]
  • Understanding the difficulty training deep feedforward neural networks . , In Proceedings of the International Conference on Artificial Intelligence and Statistics ( AISTATS2010 )
    [2010]
  • Understanding Learning Rates and How It Improves Performance in Deep Learning
    [2018]
  • Unconstrained Minimization , Convex Optimization
    pp . 457–520 [2004]
  • Ssd : Single shot multibox detector
    Volume 9905 LNCS Vol . 9905 , pp . 21–37 [2016]
  • SSD object detection : Single Shot MultiBox Detector for real-time processing ’
  • Rapid object detection using a boostedCascade of simple features
    [2001]
  • Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks
    [2001]
  • Openface : A general-purpose face recognition library with mobile applications
    [2016]
  • One Millisecond Face Alignment with an Ensemble of Regression Trees
    pp . 1867-1874 [2014]
  • Neocognitron : A self-organizing neural network model For a mechanism of pattern recognition unaffected by shift in position .
    36 , pp . 193-202 , [1980]
  • Machine Learning is Fun ! Part 4 : Modern Face Recognition with Deep Learning ’
  • ImagenetClassification with deepConvolutional neural networks
    pp . 1106-1114 [2012]
  • Gradient-Based Learning Applied to Document Recognition
    Vol . 86 , pp . 2278-2324 [1998]
  • Going Deeper with Convolutions
    [2015]
  • Generating Anchor boxes for Yolo-like network for vehicle detection using KITTI dataset .
    7 , last edited on 10
  • From Wikipedia , the free encyclopedia
  • Feature Engineering for Images : A Valuable Introduction to the HOG Feature Descriptor ’
  • Faster r-cnn : Towards real-time object detection with region proposal networks
    28 , [2015]
  • Fast R-CNN
    pp . 1440-1448 [2015]
  • Facenet : A unified embedding for face recognition and clustering
    [2015]
  • Face detection vs. Face Recognition ’
    https : //www.facefirst.com/blog/face-detection-vs-face-recognition , last edited on
  • Face Recognition using OpenFace ’
  • E. Labeled faces in the wild : A database for studying face recognition in unconstrained environments , Technical report 07–49
    [2007]
  • Dropout : a simple way to prevent neural networks from overfitting.
    [2014]
  • Dlib-ml : A Machine Learning Toolkit
    10 , pp . 1755-1758 [2009]
  • Distinctive image features from scale-invariant keypoints
    [2004]
  • Delving deep into rectifiers : Surpassing human-level performance on imagenet classification
    [2015]
  • BetterToday homepage
    ‘mAP(mean average precision)의 개념’, https://better-today.tistory.com/3, last edited on 16 August [2017]
  • Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift
    arXiv:1502.03167 [2015]
  • Applications of support vector machines in chemistry
    Vol . 23 , pp . 291-400 [2007]
  • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
    [2011]
  • 6.5 Back-Propagation and Other Differentiation Algorithms .
    pp . 200–220 [2016]