Real-Time Object Recognition Using Deep-Learning
Keywords:Keywords: Deep Learning, Convolutional Neural Network (CNN), Feature Extraction, Classification
The great interest at the moment is focused on the field of technology, especially artificial intelligence, also do not devoid of our daily life of the use of phone applications and computer programs, that increasing of phone and computer usage demands more programs and applications to satisfy the needs of users. However, this approach starts with acquirement an image of a particular medical or engineering tool, is displayed on the computer through a webcam, whether this image is a photo or digital through a display screen, or even the device itself, the computer will identify the tool and a simplified explanation of the way it works with a video demonstration throughout MATLAB IDE for implementing this project as well as easy to use by anyone even the user doesn’t have any experience software.
Finally, this approach has been created this project to save time and effort for the users instead of searching on a specific tool that they need about its name, how to use so we tried to facility this matter. The proposed algorithm got accurate result, for the doctor’s tools the accuracy was approximately between 95.8 %, for the engineer’s tools was approximately between 98.3 % and for mix of them was approximately between 94.1 %.
2. Kwon, H., Kim, Y., Park, K. W., Yoon, H., & Choi, D. (2018). Friend-safe evasion attack: An adversarial example that is correctly recognized by a friendly classifier. computers & security, 78, 380-397.
3. Mehre, S. A., Dhara, A. K., Garg, M., Kalra, N., Khandelwal, N., & Mukhopadhyay, S. (2019). Content-based image retrieval system for pulmonary nodules using optimal feature sets and class membership-based retrieval. Journal of digital imaging, 32(3), 362-385.
4. Çetin, A., & Gökhan, T. (2018). Differential Diagnosis of Erythematous Squamous Diseases With Feature Selection and Classification Algorithms. In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 103-129). IGI Global.
5. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee.
6. Liu, D., Tan, Y., Khoram, E., & Yu, Z. (2018). Training deep neural networks for the inverse design of nanophotonic structures. Acs Photonics, 5(4), 1365-1369.
7. Cohen, G., Afshar, S., Tapson, J., & Van Schaik, A. (2017, May). EMNIST: Extending MNIST to handwritten letters. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2921-2926). IEEE.
8. Kornblith, S., Shlens, J., & Le, Q. V. (2019). Do better imagenet models transfer better?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2661-2671).
9. Nixon, M., & Aguado, A. (2019). Feature extraction and image processing for computer vision. Academic press.
10. Liu, D., Tan, Y., Khoram, E., & Yu, Z. (2018). Training deep neural networks for the inverse design of nanophotonic structures. Acs Photonics, 5(4), 1365-1369.
11. Gallo, I., Calefati, A., & Nawaz, S. (2017, November). Multimodal classification fusion in real-world scenarios. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (Vol. 5, pp. 36-41). IEEE.
12. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
13. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
14. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., ... & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.
15. Wigington, C., Stewart, S., Davis, B., Barrett, B., Price, B., & Cohen, S. (2017, November). Data augmentation for recognition of handwritten words and lines using a CNN-LSTM network. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (Vol. 1, pp. 639-645). IEEE.
16. Desoli, G., Chawla, N., Boesch, T., Singh, S. P., Guidetti, E., De Ambroggi, F., ... & Aggarwal, N. (2017, February). 14.1 A 2.9 TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems. In 2017 IEEE International Solid-State Circuits Conference (ISSCC) (pp. 238-239). IEEE.
18. Jaiswal, A. K., & Banka, H. (2017). Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomedical Signal Processing and Control, 34, 81-92.
19. Chaki, J., & Dey, N. (2019). A beginner’s guide to image shape feature extraction techniques. CRC Press.
20. Passalis, N., & Tefas, A. (2017). Neural bag-of-features learning. Pattern Recognition, 64, 277-294.
21. Sun, M., Liu, K., Wu, Q., Hong, Q., Wang, B., & Zhang, H. (2019). A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis. Pattern Recognition, 90, 346-362.
22. Radovic, M., Adarkwa, O., & Wang, Q. (2017). Object recognition in aerial images using convolutional neural networks. Journal of Imaging, 3(2), 21.
23. Liu, H., Wu, Y., Sun, F., Fang, B., & Guo, D. (2017). Weakly paired multimodal fusion for object recognition. IEEE Transactions on Automation Science and Engineering, 15(2), 784-795.
24. Weinstein, B. G. (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87(3), 533-545.
25. Milletari, F., Ahmadi, S. A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., ... & Navab, N. (2017). Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding, 164, 92-102.
26. Dong, L., Yan, J., Yuan, X., He, H., & Sun, C. (2018). Functional nonlinear model predictive control based on adaptive dynamic programming. IEEE transactions on cybernetics, 49(12), 4206-4218.
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