Abstract
An image-matching method that can continuously recognize images precisely over a long period of time is proposed. On a production line, although a multitude of the same kind of components can be recognized, the appearance of a target object changes over time. Usually, to accommodate that change in appearance, the template used for image recognition is periodically updated by using past recognition results. At that time, information other than that concerning the target object might be included in the template and cause false recognition. In this research, we define the pixels which become those factors as “noisy-pixel”. With the proposed method, noisy pixels in past recognition results are extracted, and they are excluded from the processing to update the template. Accordingly, the template can be updated in a stable manner. To evaluate the performance of the proposed method, 5000 images in which the appearance of the target object changes (due to variation of lighting and adhesion of dirt) were used. According to the results of the evaluation, the proposed method achieves recognition rate of 99.5%, which is higher than that of a conventional update-type template-matching method.
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Shinohara, N., Hashimoto, M. (2017). An Image-Matching Method Using Template Updating Based on Statistical Prediction of Visual Noise. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_28
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DOI: https://doi.org/10.1007/978-3-319-64698-5_28
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