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3D Film Image Classification Based on Optimized Range of Histogram  

Lee, Jae-Eun (Dept. of IT Convergence & Applications Engineering, Pukyong National University)
Kim, Young-Bong (Dept. of IT Convergence & Applications Engineering, Pukyong National University)
Kim, Jong-Nam (Dept. of IT Convergence & Applications Engineering, Pukyong National University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.22, no.2, 2021 , pp. 71-78 More about this Journal
Abstract
In order to classify a target image in a cluster of images, the difference in brightness between the object and the background is mainly concerned, which is not easy to classify if the shape of the object is blurred and the sharpness is low. However, there are a few studies attempted to solve these problems, and there is still the problem of not properly distinguishing between wrong pattern and right pattern images when applied to actual data analysis. In this paper, we propose an algorithm that classifies 3D films into sharp and blurry using the width of the pixel values histogram. This algorithm determines the width of the right and wrong images based on the width of the pixel distributions. The larger the width histogram, the sharp the image, while the shorter the width histogram the blurry the image. Experiments show that the proposed algorithm reflects that the characteristics of these histograms allows classification of all wrong images and right images. To determine the reliability and validity of the proposed algorithm, we compare the results with the other obtained from preprocessed 3D films. We then trained the 3D films using few-shot learning algorithm for accurate classification. The experiments verify that the proposed algorithm can perform higher without complicated computations.
Keywords
3D film image; classification; histogram; image processing; width of histogram;
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1 P. Hsu, and B. Y. Chen. "Blurred image detection and classification." International Conference on Multimedia Modeling. Springer, Berlin, Heidelberg, pp. 277-286, 2008.
2 R. Wang, W. Li, R. Li, and L. Zhang, "Automatic blur type classification via ensemble SVM," Signal processing: image communication, Vol. 71, pp. 24-35, 2019.
3 Y. Li, and L. Liu, "Image quality classification algorithm based on InceptionV3 and SVM,". In MATEC Web of Conferences, Vol. 277, EDP Sciences, 2019.
4 M. Fan, R. Huang, W. Feng, and J. Sun, " Image blurred classification and blurred usefulness assessment," In 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 531-536, 2017.
5 S. W. Kwon, M. H. Kim, J. H. Kim, and S. W. Hong, "Changes in the Performance for Predicting Inappropriate Thermal Images according to the Composition of Datasets," Transactions of the Korean Society of Mechanical Engineers-A, Vol. 44, No. 12, pp. 933-940, 2020.   DOI
6 Y. Li, X. Ye, and Y. Li, "Image quality assessment using deep convolutional networks," AIP Advances, Vol. 7, No. 12, 125324, 2017.   DOI
7 J. S. Owotogbe, T. S. Ibiyemi, and B. A. Adu, "Edge Detection Techniques on Digital Images-A Review," Int J Innov Sci Res Technol, Vol. 4, pp.329-332, 2019.
8 W. A. Mustafa, and M. M. M. A.Kader, " Binarization of document images: A comprehensive review," In Journal of Physics: Conference Series, IOP Publishing, Vol. 1019, No. 1, p. 012023, 2018.   DOI
9 Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, "Generalizing from a few examples: A survey on few-shot learning," ACM Computing Surveys (CSUR), Vol. 53, No. 3, pp. 1-34, 2020.
10 R. Wang, W. Li, R. Qin, and J. Wu, "Blur image classification based on deep learning." In 2017 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1-6, 2017.
11 Keras, https://keras.io/examples/vision/reptile/