Object localization based on smoothing preprocessing and cascade classifier

Xingfu Zhang; Lei Liu; Feng Zhao

doi:10.1117/12.2265450

23 January 2017 Object localization based on smoothing preprocessing and cascade classifier

Xingfu Zhang, Lei Liu, Feng Zhao

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 103222T (2017) https://doi.org/10.1117/12.2265450
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

Abstract

An improved algorithm for image location is proposed in this paper. Firstly, the image is smoothed and the partial noise is removed. Then use the cascade classifier to train a template. Finally, the template is used to detect the related images. The advantage of the algorithm is that it is robust to noise and the proportion of the image is not sensitive to change. At the same time, the algorithm also has the advantages of fast computation speed. In this paper, a real truck bottom picture is chosen as the experimental object. Images of normal components and faulty components are all included in the image sample. Experimental results show that the accuracy rate of the image is more than 90 percent when the grade is more than 40. So we can draw a conclusion that the algorithm proposed in this paper can be applied to the actual image localization project.

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Xingfu Zhang, Lei Liu, and Feng Zhao "Object localization based on smoothing preprocessing and cascade classifier", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103222T (23 January 2017); https://doi.org/10.1117/12.2265450

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KEYWORDS

Detection and tracking algorithms

Image processing

Digital filtering

Image quality

Filtering (signal processing)

Electronic filtering

Signal processing

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