Powered by OpenAIRE graph
Found an issue? Give us feedback
CyberSystem Journalarrow_drop_down
CyberSystem Journal
Article . 2024 . Peer-reviewed
Data sources: Crossref
addClaim

Deep Learning for Computer Vision: Innovations in Image Recognition and Processing Techniques

Authors: Akeel Mahmoud; Sahar Ahmed;

Deep Learning for Computer Vision: Innovations in Image Recognition and Processing Techniques

Abstract

Deep learning is a key area of research in the field of computer vision, image processing and bioinformatics. The techniques of deep learning generally are divided into three categories namely Convolutional Neural Networks (CNN), Restricted Boltzmann Machines (RBM), Stacked RBM and HOG (Histograms of oriented Gradient) feature extraction, Convolutional Neural Networks as a Database (CNN as D). Additionally, one in few deep learning architectures which is gaining popularity and is frequently used in the field of computer vision and image processing is Extreme Learning Machine and ensemble of Extreme Learning Machine and CNN. It attempts to survey the recent advances in deep learning researchers and the application of these algorithm in the field of computer vision. Mainly focusing on the deep learning methods and algorithms rather than image processing and computer vision methods, this work inspects deep learning techniques which are widely and commonly used in the field of computer vision image detection and processing like CNN, DBN, RBM and HMM as well as various applications of these techniques. Applications of deep learning techniques in computer vision are image classification, object recognition and detection. Along with the recent works and the future scope for deep learning methods in the field of computer vision and image processing is presented.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!