Deep Learning for Computer Vision Applications: A Basic Review
Asiya Jan, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
Javed Iqbal Bangash, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
Nouman Atta, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
Maliha Tahir, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
Kanwal Lodhi, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
Corresponding Author:
Asiya Jan (asiya.jan423@gmail.com)
Abstract:
Deep learning models have revolutionized the field of Computer Vision by offering advanced techniques for interpreting visual information. Throughout the past years deep learning approaches and applications and have been displayed to beat past cutting edge machine learning methods in countless fields with computer vision being considered one of the conspicuous cases. The specified review paper gives a concise outline of Convolutional Neural Networks (CNNs), Deep Boltzmann Machines (DBMs) and Deep Belief Networks (DBNs), Autoencoders and Extreme Learning, the major deep learning methods exploited in computer vision issues. A brief record of their set of experiences, structures, benefits, and constraints is given, trailed by a depiction of their applications in different computer vision errands, object detection, face recognition, action and activity recognition. Finally, a compact overview is given of future heading in designing deep learning schemes and techniques for the issues included in computer vision.
Keywords:
Deep Learning; Artificial Intelligence; Convolutional Neural Networks (CNNs); Deep Boltzmann Machines (DBMs), Deep Belief Networks (DBNs); Autoencoders.