Open AccessRecognition of Real-World Texture Images Under Challenging Conditions With Deep Learning
Author(s) -
Özal Yıldırım,
Ayşegül Uçar,
Ulaş Baran Baloğlu
Publication year - 2018
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201812039
Subject(s) - artificial intelligence , convolutional neural network , computer science , computer vision , deep learning , layer (electronics) , texture (cosmology) , image (mathematics) , artificial neural network , object (grammar) , motion (physics) , pattern recognition (psychology) , chemistry , organic chemistry
Images obtained from the real world environments usually have various distortions in image quality. For example, when an object in motion is filmed, or when an environment is being filmed on the move, motion tracking effects occur on the image. Increasing the recognition performance of expert systems, which perform image recognition on data obtained under such conditions, is an important research area. In this study, we propose a Convolutional Neural Network (CNN) based Deep System Model (CNN-DSM) for accurate classification of images under challenging conditions. In the proposed model, a new layer is designed in addition to the classical CNN layers. This layer works as an enhancement layer. For the performance evaluations, various real world surface images were selected from the Curet database. Finally, results are presented and discussed.