Encoder–decoder semantic segmentation models for pressure wound images

Eldem, Hüseyin; Ülker, Erkan; Işıklı, Osman Yaşar

doi:10.1080/13682199.2022.2163531

Cited by 3 publications

(1 citation statement)

References 71 publications

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“…The proposed model employs the VGG16 network [12] for extracting and comparing texture features of images. This choice is based on the superior retention of texture details in the first convolutional layer of the VGG network [13], which utilizes large kernels and strides to compress input, compared to architectures such as AlexNet [14] and GoogLeNet [15]. The research modifies the texture loss calculation scheme, drawing inspiration from the method proposed by Jiang et al [16].…”

Section: Training Of the Reference Image Generation Modelmentioning

confidence: 99%

Camouflage pattern adversarial generation model using color and semantic constraints

Zhang,

Xie

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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In response to the challenge that traditional camouflage design methods struggle to evade detection by modern unmanned aerial reconnaissance, we propose a camouflage pattern generation adversarial network model using color-semantic constraints. A reference image generation model using color-semantic constraints has been established, which through sentence encoding model, generates reference images with fundamental texture and color features. This is achieved by incorporating adversarial loss, texture loss, and pixel-level loss. We design a color standardization processing strategy based on the SimCLR framework. This model generates semantic camouflage images in batches by designing data augmentation strategies, positive-negative sample similarity measurement strategies, and sample structural similarity algorithms, regard to reference images. Qualitative and quantitative experimental results demonstrate that our proposed method exhibits strong camouflage performance in different environmental settings.

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