Optimization of Image Zernike Moments Shape Feature Based on Evolutionary Computation

2013 ◽  
pp. 199-216
Author(s):  
Maofu Liu ◽  
Huijun Hu
Keyword(s):  
Image Analysis ◽  
Evolutionary Computation ◽  
High Dimension ◽  
Optimization Algorithm ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature ◽  
Original Image ◽  
Feature Based ◽  
Image Shape

The image shape feature can be described by the image Zernike moments. In this chapter, the authors point out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. Therefore, the optimization algorithm based on evolutionary computation is designed and implemented in this chapter to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

Evolutionary computation based optimization of image Zernike moments shape feature vector

2008 ◽  
Vol 13 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Maofu Liu ◽  
Hujun Hu ◽  
Ming Zhong ◽  
Yanxiang He ◽  
Fazhi He
Keyword(s):  
Evolutionary Computation ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature

scholarly journals Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yong Ding
Keyword(s):  
Synthetic Aperture Radar ◽  
High Dimension ◽  
Feature Vector ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Original Image ◽  
Intrinsic Mode Functions ◽  
Canonical Correlations ◽  
Aperture Radar

A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.

Feature Selection in High Dimension

2010 ◽  
pp. 97-119
Author(s):  
Sébastien Gadat ◽  
Sébastien Gadat
Keyword(s):  
Image Analysis ◽  
Feature Selection ◽  
Stochastic Approximation ◽  
High Dimension ◽  
Small Proportion ◽  
Features Selection ◽  
Approximation Techniques ◽  
Whole Number ◽  
Special Case ◽  
Features Weighting

Variable selection for classification is a crucial paradigm in image analysis. Indeed, images are generally described by a large amount of features (pixels, edges …) although it is difficult to obtain a sufficiently large number of samples to draw reliable inference for classifications using the whole number of features. The authors describe in this chapter some simple and effective features selection methods based on filter strategy. They also provide some more sophisticated methods based on margin criterion or stochastic approximation techniques that achieve great performances of classification with a very small proportion of variables. Most of these “wrapper” methods are dedicated to a special case of classifier, except the Optimal features Weighting algorithm (denoted OFW in the sequel) which is a meta-algorithm and works with any classifier. A large part of this chapter will be dedicated to the description of the description of OFW and hybrid OFW algorithms. The authors illustrate also several other methods on practical examples of face detection problems.

scholarly journals WDFB Digital Watermarking Method Based on Multiscale Geometrie Analysis

2012 ◽  
Vol 6-7 ◽  
pp. 1145-1149
Author(s):  
Gui Ming Shao ◽  
Zhi Hua Hu
Keyword(s):  
Image Analysis ◽  
Wavelet Transform ◽  
Digital Watermarking ◽  
Median Filtering ◽  
Watermark Image ◽  
Original Image ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Strong Robustness ◽  
The One

Combined with the human visual system and proposes a digital watermarking algorithm based WDFB domain. The algorithm is not the original image and watermark image WDFB coefficient directly superimposed, but the original image WDFB coefficient after pretreatment by the addition criteria to implement the embedded watermark. The experimental results show that the algorithm has strong robustness to shear, median filtering, noise and JPEG compression attacks. WDFB transform a new image analysis method is proposed for the lack of wavelet transform, but the watermarking method that there exist inadequacies; on the one hand, the need to use the original extract the watermark image should be along the zero-watermark the direction of the watermarking algorithm improvements.

Terrain-Aided Localization Using Feature-Based Particle Filtering

2011 ◽  
Author(s):  
Sneha Kadetotad ◽  
Pramod K. Vemulapalli ◽  
Sean N. Brennan ◽  
Constantino Lagoa
Keyword(s):  
Particle Filter ◽  
Kalman Filtering ◽  
Computational Efficiency ◽  
Particle Filtering ◽  
Feature Vector ◽  
Global Search ◽  
Wide Area ◽  
Global Localization ◽  
Number Of Solutions ◽  
Feature Based

The localization of vehicles on roadways without the use of a GPS has been of great interest in recent years and a number of solutions have been proposed for the same. The localization of vehicles has traditionally been divided by their solution approaches into two different categories: global localization which uses feature-vector matching, and local tracking which has been dealt by using techniques like Particle Filtering or Kalman Filtering. This paper proposes a unifying approach that combines the feature-based robustness of global search with the local tracking capabilities of a particle filter. Using feature vectors produced from pitch measurements from Interstate I-80 and US Route 220 in Pennsylvania, this work demonstrates wide area localization of a vehicle with the computational efficiency of local tracking.

Sign in / Sign up

Export Citation Format

Share Document