Deep Learning for Computer Vision: A Brief Review.

Athanasios Voulodimos,Eftychios Protopapadakis,Anastasios Doulamis,Nikolaos Doulamis

doi:10.1155/2018/7068349

Abstract

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Highlights

Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction mimicking how the brain perceives and understands multimodal information, implicitly capturing intricate structures of large-scale data
The surge of deep learning over the last years is to a great extent due to the strides it has enabled in the field of computer vision
The three key categories of deep learning for computer vision that have been reviewed in this paper, namely, Convolutional Neural Networks (CNNs), the “Boltzmann family” including Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs), and SdAs, have been employed to achieve significant performance rates in a variety of visual understanding tasks, such as object detection, face recognition, action and activity recognition, human pose estimation, image retrieval, and semantic segmentation

Summary

Introduction

Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction mimicking how the brain perceives and understands multimodal information, implicitly capturing intricate structures of large-scale data. Deep learning has fueled great strides in a variety of computer vision problems, such as object detection (e.g., [8, 9]), motion tracking (e.g., [10, 11]), action recognition (e.g., [12, 13]), human pose estimation (e.g., [14, 15]), and semantic segmentation (e.g., [16, 17]) In this overview, we will concisely review the main developments in deep learning architectures and algorithms for computer vision applications. We will concisely review the main developments in deep learning architectures and algorithms for computer vision applications In this context, we will focus on three of the most important types of deep learning models with respect to their applicability in visual understanding, that is, Convolutional Neural Networks (CNNs), the “Boltzmann family” including Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs) and Stacked (Denoising) Autoencoders.

Deep Learning Methods and Developments

Applications in Computer Vision

Conclusions