Dr. Lu Zhao Profile

Dr. Lu Zhao

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Proceedings Article | 25 March 2024 Paper

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Fangbing Xia, Xiaoli Li, Zhenlong Du, Lu Zhao

Proceedings Volume 13089, 130891L (2024) https://doi.org/10.1117/12.3021534

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A novel method is proposed to construct relatively rich vector patterns from existing examples to address the problem of excessively simple and coarse details in automatically generated patterns. This method involves several key steps, including the extraction of vectorized primitives, the construction of primitive relationships, and the intelligent generation of patterns through optimization algorithms. Specifically, vectorized primitives are extracted from raster images, and directed graphs are used to establish relationships between primitives, taking into account the geometric relationships of the graph. Primitive relationships are calculated based on the extracted geometric relationships, and relevant constraints are used to transform the original pattern. The transformed pattern is then optimized to produce a more harmonious and aesthetically pleasing pattern variation. Experimental results show that the proposed algorithm can generate a diverse set of novel pattern variants, and the optimized variants demonstrate high levels of harmony and aesthetics. Users have the ability to influence the direction of pattern generation by adjusting the primitives, enabling them to compare and select the generated pattern variants that align with their implicit preferences. The proposed method provides an effective solution for pattern generation, catering to various requirements in practical applications and delivering a range of diverse pattern graphics for products.

SPIE Journal Paper | 28 August 2021

Mask-based generative adversarial networking for crowd counting

Guoxiu Duan, Aichun Zhu, Lu Zhao, Xiaomei Zhu, Fangqiang Hu, Xinjie Guan

JEI, Vol. 30, Issue 04, 043027, (August 2021) https://doi.org/10.1117/12.10.1117/1.JEI.30.4.043027

KEYWORDS: Convolution, Gallium nitride, Head, Performance modeling, Neural networks, Network architectures, Image quality, Lithium, Image segmentation, Feature extraction

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Crowd counting is still a challenging task due to the variability of the distance scale, crowd occlusion, and complex background information. However, the deep convolution neural network has been proved to be effective in solving these problems. By loading input images, the network generates predicted density maps, and the average absolute error between the predicted density maps and given ground truth (GT) maps is a solid standard for evaluating the quality of the network. We propose a mask-based generative adversarial network (MBGAN) structure to generate accurate predicted density maps. The network consists of two parts: the generator and the discriminator. In the generator, we embed a fundamental feature extracting module, multiple level dilated convolution blocks, a predicted mask, and shortcut connection operations. The discriminator is mainly used to distinguish whether the density map comes from the generator or the GT and urges the generator to produce the density map that can confuse itself. The training of the proposed MBGAN model is through the joint action of density loss and adversarial loss. In the training strategy, we use the cross training of the generator and discriminator. Through experiments on five available datasets, the MBGAN achieved state-of-the-art performances that outperform other advanced methods.

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