ARCHIVES

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[2] G. Flitton, T. P. Breckon, and N. Megherbi, “A comparison of 3D interest point descriptors with application to airport baggage object
detection in complex CT imagery,” Pattern Recognit., vol. 46, no. 9, pp. 2420–2436, 2013.
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Intelligence and Pattern Recognition (AIPR) (pp. 1-5). IEEE.
[4] Jung, H., Lee, S., Park, S., Kim, B., Kim, J., Lee, I., & Ahn, C. (2015, January). Development of deep learning-based facial expression
recognition system. In 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) (pp. 1-4). IEEE.
[5] Tenguria, R., Parkhedkar, S., Modak, N., Madan, R., & Tondwalkar, A. (2017, April). Design framework for general purpose object
recognition on a robotic platform. In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 2157 -
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[6] Etemad, E., & Gao, Q. (2017, September). Object localization by optimizing convolutional neural network detection score using generic
edge features. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 675-679). IEEE. [7] Mazumdar, M., Sarasvathi, V., & Kumar, A. (2017, August). Object recognition in videos by sequential frame extraction using
convolutional neural networks and fully connected neural networks. In 2017 International Conference on Energy, Communication,
Data Analytics and Soft Computing (ICECDS) (pp. 1485-1488). IEEE.
[8] Sujana, S. R., Abisheck, S. S., Ahmed, A. T., & Chandran, K. S. (2017, April). Real time object identification using deep convolutional
neural networks. In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 1801-1805). IEEE.
[9] Cheng, S. C. (2005). Visual pattern matching in motion estimation for object -based very low bit-rate coding using moment-preserving
edge detection. IEEE transactions on multimedia, 7(2), 189-200.
[10] Alexeev, A., Matveev, Y., and Kukharev, G. (2018, October). Using a Fully Connected Convolutional Network to Detect Objects in
Images. In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 141-146). IEEE.
[11] Wong, S. C., Stamatescu, V., Gatt, A., Kearney, D., Lee, I., and McDonnell, M. D. (2017). Track everything: Limiting pri or knowledge
in online multi-object recognition. IEEE Transactions on Image Processing, 26(10), 4669-4683.
[12] Yang, L., Wang, L., & Wu, S. (2018, April). Real-time object recognition algorithm based on deep convolutional neural network. In
2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 331-335). IEEE.
[13] https://www.javatpoint.com/supervised-machine-learning
[14] https://techvidvan.com/tutorials/unsupervised-learning/
[15] https://www.kdnuggets.com/2018/03/5-things-reinforcement-learning.html.