Abstract
This paper presents noise robust object detection using self-quotient ε-filter (SQEF) and histograms of oriented gradients (HOG). Although object detection combining HOG and support vector machine (SVM) is a promising approach, when the images are corrupted with noise, it is difficult to realize a good detection performance. To handle noise corrupted images, we employ SQEF, and apply it to object detection combining HOG and SVM. SQEF is an improved self-quotient filter (SQF), and can clearly extract features from the images not only when they have illumination variations but also when they are corrupted with noise. We confirmed the effectiveness of our approach by using human images and car images. Throughout the experiments, our approach can realize a robust object detection from noise corrupted images using the data trained by intact images without noise.
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Matsumoto, M. (2012). SVM-Based Object Detection Using Self-quotient ε-Filter and Histograms of Oriented Gradients. In: Madani, K., Dourado Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2010. Studies in Computational Intelligence, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27534-0_18
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DOI: https://doi.org/10.1007/978-3-642-27534-0_18
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