| Author | Tejasri, Nampally |
| Note | A thesis submitted in partial fulfillment of the requirements for the
degree of Master of Engineering in
Microelectronics and Embedded Systems
|
| Publisher | Asian Institute of Technology |
| Abstract | The classification and recognition of variety of materials that are present in our surroundings become an important visual competition have been focused by computer vision systems in the recent years. Understanding the recognition of the materials in different images that involve a deep learning process made use of the recent development in the field of Artificial Neural Networks brought the ability to train various neural network architectures for the extraction of features for this challenging task. In this work, state-of-the-art Convolutional Neural Network (CNN) techniques are used to classify materials and also compare the results obtained by them.The results are gathered over two material data sets applying the two popular approaches of Transfer Learning. The results showcase that fine-tuning approach achieves very good results compared to the case of approach when the information derived from the layer which is just before the fully connected layer is limited. The results of the comparison indicates the fact that there is an improvement in the performance and the accuracy of the system particularly in the data set that contains large number of images. |
| Year | 2018 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Microelectronics (ME) |
| Chairperson(s) | Mongkol Ekpanyapong; |
| Examination Committee(s) | Manukid Parnichkun;Dailey, Matthew N. ; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Thesis (M. Eng.) -- Asian Institute of Technology, 2018 |