This comprehensive book is primarily intended for researchers, computer vision specialists, and high-performance computing specialists who are interested in parallelizing computer vision techniques for the sake of accelerating the run-time of computer vision methods. This book covers different penalization methods on different parallel architectures such as multi-core CPUs and GPUs. It is also a valuable reference resource for researchers at all levels (e.g., undergraduate and postgraduate) who are seeking real-life examples of speeding up the computer vision methods’ run-time.
Author(s): Khalid M. Hosny, Ahmad Salah
Series: Studies in Computational Intelligence, 1073
Publisher: Springer
Year: 2023
Language: English
Pages: 125
City: Cham
Preface
About This Book
Contents
A Generic Multicore CPU Parallel Implementation for Fractional Order Digital Image Moments
1 Introduction
2 Background
2.1 Fractional Order Moments
2.2 Shared-Memory Parallel Programming for CPUs
3 Related Work
4 The Proposed Implementation
5 Results and Discussion
5.1 Setup
5.2 Results
6 Conclusions
References
Computer-Aided Road Inspection: Systems and Algorithms
1 Introduction
2 Road Damage Types
3 Road Data Acquisition
3.1 Sensors
3.2 Public Datasets
4 Road Damage Detection
4.1 2-D Image Analysis/Understanding-Based Approaches
4.2 3-D Road Surface Modeling-Based Approaches
4.3 Hybrid Approaches
5 Parallel Computing Architecture
6 Summary
References
Computer Stereo Vision for Autonomous Driving: Theory and Algorithms
1 Introduction
2 Autonomous Car System
2.1 Hardware
2.2 Software
3 Autonomous Car Perception
4 Computer Stereo Vision
4.1 Preliminaries
4.2 Multi-view Geometry
4.3 Stereopsis
5 Heterogeneous Computing
5.1 Multi-threading CPU
5.2 GPU
6 Summary
References
A Survey on GPU-Based Visual Trackers
1 Introduction
2 Parallel Computing
2.1 The Main Difference Between GPU and CPU
2.2 Strategy for Designing a Parallel Algorithm
2.3 Performance Evaluation Metrics
2.4 GPU Programming
3 Levels of Object Tracking Algorithms
3.1 Single Object Tracking
3.2 Multiple Object Tracking (MOT)
4 Conclusion
References
Accelerating the Process of Copy-Move Forgery Detection Using Multi-core CPUs Parallel Architecture
1 Introduction
2 Background
2.1 Types of Image Forgery
2.2 Copy-Move Forgery Detection Techniques
2.3 OpenMP
3 Related Work
3.1 Block-Based Methods
3.2 Key-Point Based Methods
3.3 Parallel Architectures in Copy-Move Forgery Detection
4 The Proposed Method
5 Results and Discussion
5.1 Setup
5.2 Results
6 Conclusion
References
Parallel Image Processing Applications Using Raspberry Pi
1 Introduction
2 Raspberry Pi: General Overview
2.1 Memory Bandwidth
2.2 Ethernet Throughput
2.3 WI-FI Throughput
2.4 Power Draw
3 Parallel Image Processing Applications Using RPI
3.1 Medical Applications
3.2 Recognition Applications
3.3 Monitoring Applications
3.4 Compression
3.5 Autonomous Car Driving
4 Conclusion
References
