Scale Distorted Target Recognition Based on Machine Vision

Yu Chen; Fu Rong Huo; Li Qin Zheng

doi:10.4028/www.scientific.net/AMR.1006-1007.760

Paper Titles

A Detecting System on Defect Area of the Strip Steel’s Surface
p.743

Characteristics Detection Method for Material Surface Based on Polarization Vision Information
p.747

Review on Application of Near-Infrared Spectroscopy Identification Technology in Sorting Impurities from Waste Paper
p.752

Researches on Segmentation of Overlapped Convex Targets
p.756

Scale Distorted Target Recognition Based on Machine Vision
p.760

Vector Quantization by Minimizing Kullback-Leibler Divergence between the Class Label Distributions over Quantization Input and Output
p.764

An Image Quality Assessment Approach Based on Saliency Map in Space Domain
p.768

Research on Surface Defect Detection Technique of Rolling Element Based on Computer Vision
p.773

A Low Cost Scheme of Scene Depth Map for Stereoscopic Video Streaming Using Single Kinect Sensing
p.779

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1006-1007Scale Distorted Target Recognition Based on...

Scale Distorted Target Recognition Based on Machine Vision

Abstract:

Machine vision means to carry out measurement or judgment with machine instead of human eyes. In the field of target recognition, optical correlation technology is a main way to realize machine vision. Targets can be recognized and located with high precision taking advantage of optoelectronic hybrid joint transform correlator (OHJTC). However, when scale or angular distortion of the detected target exists relative to the reference template, the intensity of correlation peaks will decrease to a great extent, which restricted the recognition results greatly. In this paper, the development and principle of maximum average correlation height (MACH) algorithm is introduced. Through amounts of experiments, the controlling parameters of the synthesized filter are optimized, which makes MACH filter suppress background noise and widen recognition range of targets. To show the feasibility of this algorithm, simulative and optical experiments of the improved MACH filter are carried out. As an example, the recognition results of a fighter target in sky are given, which shows the scale distortion tolerance can reach up to ±23%. The actual effect of the improved MACH filter algorithm has been confirmed very well.

You might also be interested in these eBooks

Advanced Manufacturing and Industrial Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1006-1007)

Pages:

760-763

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1006-1007.760

Citation:

Cite this paper

Online since:

August 2014

Authors:

Yu Chen*, Fu Rong Huo, Li Qin Zheng

Keywords:

Frequency Domain, Machine Vision, Maximum Average Correlation Height Algorithm, Pattern Recognition, Scale Distorted Target, Space Domain

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] F.J. Song, S. Jutamulia, Advanced Optical Information Processing, Peaking University Press, Beijing, (2004).

Google Scholar

[2] X.Y. Yuan, D.Y. Yu, C. K Tao, Distortion-invariant joint transform correlator based on phase-encoding technique, Acta Photonica Sinica. 31 (2002), 1239-1243.

DOI: 10.1117/1.1512664

Google Scholar

[3] W.S. Wang, Y. Chen, C.P. Liang, Hybrid optoelectronic joint transform correlator for the recognition of target in cluttered scenes, Proc. SPIE5642, (2005), 204-212.

Google Scholar

[4] J.Y. Shang, Y. Zhang, Q.B. Zhang, Recognition technology of hybrid-distortion target in cluttered background, Acta Photonica Sinica, 42 (2013), 342-347.

DOI: 10.3788/gzxb20134203.0342

Google Scholar

[5] S. Zhang, W.S. Wang, C.Y. Xu, Optical correlation detection technology of moving target under low contrast environment, Chinese Journal of Scientific Instrument, 34(2013), 319-325.

Google Scholar