A printer model and human visual model based method on tone-dependent error diffusion (TDED) is proposed.
提出了一种基于打印机模型和视觉模型的阶调误差扩散算法。
At the same time, combined with the human visual model, our algorithm guarantees the visual impression and protects the low frequency part of image efficiently.
同时算法结合了人眼视觉模型，有效地保护了图像的低频部分和强边缘部分。从而确保了加密图像的视觉效果。
The duo then attempted to mimic how the human visual system might be processing these images by adding a set of filters to their model designed to detect these features.
两位科学家随后通过在他们用于探测这些特征的模型上放置一组滤镜，来尝试模仿人类的视觉系统会如何处理这些图像。
The conventional quadric rate-distortion model was improved and a new one was introduced by incorporating the human visual system (HVS) characteristics.
算法对传统二次率失真模型进行了改进，并结合人眼的立体视觉特性，提出了新的码率控制策略。
The reconstruction model for the lost image information of the arbitrary-shape area based on the human visual characteristics is put forward.
因此提出了基于人眼视觉特性的任意形状区域丢失图像信息的重建模型。
An image quality evaluation model that considers characteristics of the human visual system should improve its performance.
在图像质量评价中加入人眼视觉系统特性能够提高其评价性能。
In this system, computer simulated dam system layout model, and could dynamic analysis and get amount, type and composing of building, combining 3d visual modeling and Human-Computer interaction.
本系统把各坝系布局的专业模型用计算机语言来实现，结合小流域三维可视化环境，通过人机交互方式确定最终坝的数量、类型、建筑物组成以及进行动态分析。
Meanwhile, the redundant skeleton branches are eliminated by Discrete Curve Evolution model, the visual branches are remained completely, the skeleton from this algorithm satisfy human vision.
同时，离散曲线演化模型有效的抑制了冗余骨架支的产生，得到了符合人类视觉规律的多尺度骨架。
Image information processing before the microelectrode arrays requires a suitable mathematical model for human visual information processing firstly.
而微电极之前的图像信息处理过程需要一个视觉信息处理模型为基础。
Finally, the paper set up an object recognition theoretical model that is based on human visual attention mechanism.
最后，本文根据视觉注意的焦点，建立了基于视觉注意的物体识别的理论模型。
Finally, the paper set up an object recognition theoretical model that is based on human visual attention mechanism.
最后，本文根据视觉注意的焦点，建立了基于视觉注意的物体识别的理论模型。
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