On fast and accurate block-based motion estimation algorithms using particle swarm optimization

2012 ◽  
Vol 197 ◽  
pp. 53-64 ◽  
Author(s):  
Jing Cai ◽  
W. David Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Estimation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Estimation Algorithms ◽  
Block Based

Improved Particle Swarm Optimization Approach for Vibration Vision Measurement

2020 ◽  
pp. 2159011
Author(s):  
Chunli Zhu ◽  
Yuan Shen ◽  
Xiujun Lei
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Estimation ◽  
Search Algorithm ◽  
Computational Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Vision Measurement ◽  
Improved Pso

Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.

A Secure Data-Hiding Approach Using Particle Swarm Optimization and Pixel Value Difference

2016 ◽  
pp. 363-381
Author(s):  
Tanmoy Halder ◽  
Sunil Karforma ◽  
Rupali Halder
Keyword(s):  
Particle Swarm Optimization ◽  
Data Hiding ◽  
Particle Swarm ◽  
Secret Message ◽  
Secret Data ◽  
Swarm Optimization ◽  
Pixel Value ◽  
The Difference ◽  
Block Based ◽  
Pixel Value Difference

In this chapter a novel data hiding approach by combining Particle Swarm Optimization (PSO) and Pixel Value Difference (PVD) has been proposed. Pixel-Value-Difference (PVD) method of Steganography uses the difference between pixels within an image to hide secret data. The proposed method is a block-based adaptive steganographic approach, which selects M×N block of pixels from cover image and embed secret message within pixels using Pixel-value-difference and LSB substitution method. PSO is used to select most appropriate areas within the image for hiding secret information. Results obtained using the approach show that distortion due to data embedding is negligible. The proposed approach is compared with existing methods in terms of bits per pixel. This method could be applied to hide any digital secret data for secure transfer over internet.

Sign in / Sign up

Export Citation Format

Share Document