Pengenalan Gerakan Tangan Manusia Menggunakan Deep Neural Network
57
DAFTAR PUSTAKA
Alex, D.S. & Wahi, A. 2014. BFSD: Background subtraction frame difference
algorithm for moving object detection and extraction. Journal of Theoretical &
Applied Information Technology 60(3): 623-628.
Amirani, M.C., Toorani, M. & Beheshti, A.A. 2008. A new approach to content-based
file type detection. Proceedings of the 13th IEEE Symposium on Computers
and Communications (ISCC’08), pp. 1103-1108.
Bengio, Y. 2009. Learning Deep Architectures for AI. Now: Netherland.
Bradski, G. & Kaehler, A. 2008. Learning OpenCV. O’Relly Media, Inc: Sebastopol.
Chairunnisa, T. 2015. Pengenalan gerakan tangan manusia untuk interaksi manusiakomputer. Skripsi. Universitas Sumatera Utara.
Deng, L. & Yu, D. 2014. Deep Learning Methods and Applications. 978-1-60198814-0. Now: Netherland.
Dunteman, G.H. 1989. Principal Components Analysis. SAGE: Thousand Oaks.
Erhan, D., Szegedy, C., Toshev, A. & Anguelov, D. 2014. Scalable object detection
using deep neural networks. IEEE Conference on Computer Vision and
Pattern Recognition, pp. 2155-2162.
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. 2nd Edition.
Prentice Hall: Upper Saddle River.
Heaton, J. 2015. Deep Learning and Neural Networks. Artificial Intelligence for
Humans. Volume 3. Heaton Research, Inc.: Chesterfield.
Ivakhnenko, A. G. 1971. Polynomial theory of complex system. IEEE Transactions
on Systems, Man, and Cybernetics 1(4): 364-378.
Jolliffe, I.T. 2002. Principal Component Analysis. Springer: London.
Kang, J. & Hayes, M.H. 2015. Face recognition for vehicle personalization with nearIR frame differencing and pose clustering. IEEE International Conference on
Consumer Electronics (ICCE), pp. 455-456.
Malepati, H. 2010. Digital Media Processing. 978-1-85617-678-1. Elsevier:
Burlington.
Mohri, M., Rostamizadeh, A. & Talwalkar, A. 2012. Foundations of Machine
Learning. The MIT Press: Cambridge.
Universitas Sumatera Utara
58
Molchanov, P., Gupta, S., Kim, K. & Kautz, J. 2015. Hand gesture recognition with
3D convolutional neural networks. IEEE Conference on Computer Vision and
Pattern Recognition Workshops, pp. 1-7.
Negnevitsky, M. 2005. Artificial Intelligence: A Guide to Intelligent Systems. 2nd
Edition. Pearson Education Limited: Upper Saddle River.
Neto, P., Pereira, D., Pires, J.N. & Moreira, A.P. 2013. Real-time and continuous hand
gesture spotting: an approach based on artificial neural networks. IEEE
International Conference on Robotic and Automation (ICRA), pp. 178-183.
Ramjan, M.R., Sandip, R.M., Uttam, P.S. & Srimant, W.S. 2014. Dynamic hand
gesture recognition and detection for real time using human computer
interaction. International Journal of Advance Research in Computer Science
and Management Studies (IJARCSMS) 2(3): 425-430.
Safinaz, S. 2014. An efficient algorithm for image scaling with high boost filtering.
International Journal of Scientific and Research Publications 4(5): 1-9.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Driessche, G., Schrittwieser,
J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D.,
Nham, J., Kalchbrenner, N., Sutskever, I., Lilicrap, T., Leach, M.,
Kavukcuoglu, K., Graepel, T. & Hassabis, D. 2016. Mastering the game of go
with deep neural networks and tree search. Nature, January 529: 484-489.
Tang, A., Lu, K., Wang, Y., Huang, J. & Li, H. 2013. A real-time hand posture
recognition system using deep neural networks. ACM Transactions on
Intelligent Systems and Technology 9(4): 39:1-21.
Vision
For
Intelligent
Vehicles
and
Appilications
Dataset.
http://cvrr.ucsd.edu/vivachallenge/index.php/hands/hand-gestures/. (diakses 14
Mei 2016)
Universitas Sumatera Utara
DAFTAR PUSTAKA
Alex, D.S. & Wahi, A. 2014. BFSD: Background subtraction frame difference
algorithm for moving object detection and extraction. Journal of Theoretical &
Applied Information Technology 60(3): 623-628.
Amirani, M.C., Toorani, M. & Beheshti, A.A. 2008. A new approach to content-based
file type detection. Proceedings of the 13th IEEE Symposium on Computers
and Communications (ISCC’08), pp. 1103-1108.
Bengio, Y. 2009. Learning Deep Architectures for AI. Now: Netherland.
Bradski, G. & Kaehler, A. 2008. Learning OpenCV. O’Relly Media, Inc: Sebastopol.
Chairunnisa, T. 2015. Pengenalan gerakan tangan manusia untuk interaksi manusiakomputer. Skripsi. Universitas Sumatera Utara.
Deng, L. & Yu, D. 2014. Deep Learning Methods and Applications. 978-1-60198814-0. Now: Netherland.
Dunteman, G.H. 1989. Principal Components Analysis. SAGE: Thousand Oaks.
Erhan, D., Szegedy, C., Toshev, A. & Anguelov, D. 2014. Scalable object detection
using deep neural networks. IEEE Conference on Computer Vision and
Pattern Recognition, pp. 2155-2162.
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. 2nd Edition.
Prentice Hall: Upper Saddle River.
Heaton, J. 2015. Deep Learning and Neural Networks. Artificial Intelligence for
Humans. Volume 3. Heaton Research, Inc.: Chesterfield.
Ivakhnenko, A. G. 1971. Polynomial theory of complex system. IEEE Transactions
on Systems, Man, and Cybernetics 1(4): 364-378.
Jolliffe, I.T. 2002. Principal Component Analysis. Springer: London.
Kang, J. & Hayes, M.H. 2015. Face recognition for vehicle personalization with nearIR frame differencing and pose clustering. IEEE International Conference on
Consumer Electronics (ICCE), pp. 455-456.
Malepati, H. 2010. Digital Media Processing. 978-1-85617-678-1. Elsevier:
Burlington.
Mohri, M., Rostamizadeh, A. & Talwalkar, A. 2012. Foundations of Machine
Learning. The MIT Press: Cambridge.
Universitas Sumatera Utara
58
Molchanov, P., Gupta, S., Kim, K. & Kautz, J. 2015. Hand gesture recognition with
3D convolutional neural networks. IEEE Conference on Computer Vision and
Pattern Recognition Workshops, pp. 1-7.
Negnevitsky, M. 2005. Artificial Intelligence: A Guide to Intelligent Systems. 2nd
Edition. Pearson Education Limited: Upper Saddle River.
Neto, P., Pereira, D., Pires, J.N. & Moreira, A.P. 2013. Real-time and continuous hand
gesture spotting: an approach based on artificial neural networks. IEEE
International Conference on Robotic and Automation (ICRA), pp. 178-183.
Ramjan, M.R., Sandip, R.M., Uttam, P.S. & Srimant, W.S. 2014. Dynamic hand
gesture recognition and detection for real time using human computer
interaction. International Journal of Advance Research in Computer Science
and Management Studies (IJARCSMS) 2(3): 425-430.
Safinaz, S. 2014. An efficient algorithm for image scaling with high boost filtering.
International Journal of Scientific and Research Publications 4(5): 1-9.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Driessche, G., Schrittwieser,
J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D.,
Nham, J., Kalchbrenner, N., Sutskever, I., Lilicrap, T., Leach, M.,
Kavukcuoglu, K., Graepel, T. & Hassabis, D. 2016. Mastering the game of go
with deep neural networks and tree search. Nature, January 529: 484-489.
Tang, A., Lu, K., Wang, Y., Huang, J. & Li, H. 2013. A real-time hand posture
recognition system using deep neural networks. ACM Transactions on
Intelligent Systems and Technology 9(4): 39:1-21.
Vision
For
Intelligent
Vehicles
and
Appilications
Dataset.
http://cvrr.ucsd.edu/vivachallenge/index.php/hands/hand-gestures/. (diakses 14
Mei 2016)
Universitas Sumatera Utara