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首页> 外文期刊>IEICE transactions on information and systems >Recognition of Moving Object in High Dynamic Scene for Visual Prosthesis
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Recognition of Moving Object in High Dynamic Scene for Visual Prosthesis

机译：视觉假体在高动态场景中的运动物体识别

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Currently, visual perceptions generated by visual prosthesis are low resolution with unruly color and restricted grayscale. This severely restricts the ability of prosthetic implant to complete visual tasks in daily scenes. Some studies explore existing image processing techniques to improve the percepts of objects in prosthetic vision. However, most of them extract the moving objects and optimize the visual percepts in general dynamic scenes. The application of visual prosthesis in daily life scenes with high dynamic is greatly limited. Hence, in this study, a novel unsupervised moving object segmentation model is proposed to automatically extract the moving objects in high dynamic scene. In this model, foreground cues with spatiotemporal edge features and background cues with boundary-prior are exploited, the moving object proximity map are generated in dynamic scene according to the manifold ranking function. Moreover, the foreground and background cues are ranked simultaneously, and the moving objects are extracted by the two ranking maps integration. The evaluation experiment indicates that the proposed method can uniformly highlight the moving object and keep good boundaries in high dynamic scene with other methods. Based on this model, two optimization strategies are proposed to improve the perception of moving objects under simulated prosthetic vision. Experimental results demonstrate that the introduction of optimization strategies based on the moving object segmentation model can efficiently segment and enhance moving objects in high dynamic scene, and significantly improve the recognition performance of moving objects for the blind.
机译：当前，由视觉假体产生的视觉感知是低分辨率的，具有不规则的颜色和受限的灰度。这严重限制了假体植入物在日常场景中完成视觉任务的能力。一些研究探索了现有的图像处理技术，以改善假肢视觉中物体的感知能力。但是，它们大多数提取运动对象并优化一般动态场景中的视觉感知。视觉假体在具有高动态性的日常生活场景中的应用受到很大限制。因此，在这项研究中，提出了一种新颖的无监督运动对象分割模型来自动提取高动态场景中的运动对象。该模型利用具有时空边缘特征的前景线索和具有边界先验的背景线索，根据流形排序功能在动态场景中生成运动物体邻近图。此外，同时对前景和背景提示进行排名，并通过两个排名图集成来提取运动对象。评估实验表明，与其他方法相比，该方法能够在高动态场景下均匀地突出运动对象并保持良好的边界。在此模型的基础上，提出了两种优化策略来改善模拟假肢视觉下运动物体的感知。实验结果表明，基于运动对象分割模型的优化策略的引入可以有效地分割和增强高动态场景中的运动对象，并显着提高盲人运动对象的识别性能。

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