Geometry and Bionic Fusion Feature Extraction Method for Affine Target Recognition

YU Ling-li; YI Qian; JIN Ming-yue; ZHOU Kai-jun

doi:10.12263/DZXB.20210450

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Geometry and Bionic Fusion Feature Extraction Method for Affine Target Recognition

PAPERS | 更新时间：2025-12-08

- Geometry and Bionic Fusion Feature Extraction Method for Affine Target Recognition
- ACTA ELECTRONICA SINICA Vol. 51, Issue 6, Pages: 1607-1618(2023)
- 作者机构：
  
  1.中南大学自动化学院，湖南长沙 410083
  2.湖南工商大学智能工程与智能制造学院，湖南长沙 410205
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61976224;61976088)
- DOI：10.12263/DZXB.20210450
  CLC： TP391.4;TN911.73
- Received：09 April 2021，
  
  Revised：2022-04-27，
  
  Published：25 June 2023
- 稿件说明：
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余伶俐,易倩,金鸣岳等.面向仿射目标识别的几何与仿生融合特征提取方法[J].电子学报,2023,51(06):1607-1618.

YU Ling-li,YI Qian,JIN Ming-yue,et al.Geometry and Bionic Fusion Feature Extraction Method for Affine Target Recognition[J].ACTA ELECTRONICA SINICA,2023,51(06):1607-1618.
余伶俐,易倩,金鸣岳等.面向仿射目标识别的几何与仿生融合特征提取方法[J].电子学报,2023,51(06):1607-1618. DOI： 10.12263/DZXB.20210450.

YU Ling-li,YI Qian,JIN Ming-yue,et al.Geometry and Bionic Fusion Feature Extraction Method for Affine Target Recognition[J].ACTA ELECTRONICA SINICA,2023,51(06):1607-1618. DOI： 10.12263/DZXB.20210450.

摘要

针对由于拍摄视角不同，目标图像在水平或垂直方向发生拉长或压缩等仿射变换，进而无法正确识别的问题，本文设计了一种几何与仿生融合的特征提取方法.首先，对传统的角点和直线检测进行改进，提出自适应Harris角点检测方法和去冗余的直线检测方法，并将角点数、直线数和面积比向量作为几何特征.然后，采用生物启发变换算法提取图像的仿生启发特征，该算法包括两个阶段，每个阶段均需执行方向边缘检测和局部空间频率检测.接着，将输入图像的两种特征向量分别与标准数据库中的特征向量进行Pearson相关距离计算，获得匹配得分.最后，在考虑不同数据库两种特征区分性强弱的基础上自适应确定权值，最高融合分数所对应的标签即为该图像的识别结果.实验结果表明，该方法能较好地提取图像的仿射不变特征，并且该方法在Alphanumeric，MPEG-7，GTSRB和MNIST数据库的识别准确率分别为92.2%，96%，90%和87.3%.

Abstract

In view of the problems that the target image is elongated or compressed in the horizontal or vertical direction due to different shooting angles

and the target image cannot be correctly recognized

this paper designs a feature extraction method that combines geometry and bionic vision. Aiming at geometric feature extraction

this paper improves the traditional corner and line detection algorithms

and proposes an adaptive Harris corner detection algorithm based on iterative threshold that draws on the similarity of regional pixels and a de-redundant line detection algorithm based on Hough transform. The number of corner points

the number of straight lines

and the area ratio feature vectors are taken as geometric features. Then

the bio-inspired transformation algorithm is used to extract the bionic visual features of the target image. The algorithm includes two stages. Each stage needs to perform directional edge detection and local spatial frequency detection. Calculate the Pearson correlation distance between the geometric and bionic inspired features of the target image and the features in the standard database to obtain the matching scores. The weights are adaptively determined on the basis of considering the distinguishing strength of the two characteristics of different databases. And the label corresponding to the highest fusion score is the recognition result of the image. Experimental results show that the fusion method can well extract the affine invariance features of affine images. The recognition accuracy of this method in Alphanumeric

MPEG-7

GTSRB and MNIST databases are 92.2%

96%

90% and 87.3% respectively.

关键词

Keywords

references

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