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.