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A Novel Fine-Grained Method for Vehicle Type Recognition Based on the locally Enhanced PCANet Neural Network

Journal of Computer Science & Technology 2018年第2期33卷 335-350页

作者： Qian Wang You-Dong Ding School of Computer Enqineering and Science Shan qhai University Shanqhai 200072 China Information Center Criminal Investigation Department of Shanghai Public Security Bureau Shanghai 200083 China Shanghai Engineering Research Center of Motion Picture Special Effects Shanghai 200072 China Shanghai Film Academy Shanghai University Shanghai 200072 China

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° （12-angle in 360°） classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

关键词： fine-grained classification PCANet local enhancement vehicle type recognition

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Advanced Histogram Equalization Based on a Hybrid Saliency Map and Novel Visual Prior

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Machine Intelligence Research 2024年第6期21卷 1178-1191页

作者： Yuanbin Wu Shengkui Dai Zhan Ma School of Information Science and Engineering Huaqiao UniversityXiamen361021China School of Electronic Science and Engineering Nanjing UniversityNanjing210093China

The traditional grayscale histogram of an input image is constructed by simply counting its ***,the classical his-togram equalization(HE)technique has fundamental defects such as overenhancement,underenhancement,and brightness *** paper proposes an advanced HE based on a hybrid saliency map and a novel visual prior to addressing the defects mentioned ***,the texture saliency map and attention weight map are constructed based on the texture saliency and visual attention ***,the hybrid saliency map that is obtained by fusing the texture and attention weight maps is used to derive the saliency ***,a novel visual prior,the narrow dynamic range prior(NDP),is proposed,and the saliency histogram is modified by calculating the optimal parameter in combination with a binary optimization ***,the cumulative distribution function(CDF)is rectified to control the ***,the hybrid saliency map is applied again for local *** with several state-of-the-art algorithms qualitatively and quantitatively,the proposed algorithm effectively improves the contrast of the image,generates better sub-jective visual perception,and presents better performance broadly.

关键词： Texture saliency attention mechanism narrow dynamic range prior(NDP) brightness control histogram equalization(HE) local enhancement

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