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仿生型机器视觉研究 *   总被引:4,自引:2,他引:2       下载免费PDF全文
在深入分析人类眼球的神经机理、运动形式和特点及人类视觉神经通路的基础上 ,从模拟人类眼球运动构筑仿生机器眼和模拟人类视觉感知机理应用于机器视觉两个方面 ,探讨了视觉仿生研究的方式方法、研究进展、应用前景和发展趋势。提出了采用复杂系统控制方法构建多自由度仿生型机器人双眼运动模型的思路 ,分析了人眼固视微动机制的综合利用和应用价值以及超人眼系统的研究设想等新的视觉仿生研究方向。  相似文献
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We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. We refine the approach in several biologically plausible ways. Sparsity is increased by constraining the number of feature inputs, lateral inhibition, and feature selection. We also demonstrate the value of retaining some position and scale information above the intermediate feature level. Our final model is competitive with current computer vision algorithms on several standard datasets, including the Caltech 101 object categories and the UIUC car localization task. The results further the case for biologically-motivated approaches to object classification. This paper updates and extends an earlier presentation (Mutch and Lowe 2006) of this research in CVPR 2006. J. Mutch’s research described in this paper was carried out at the University of British Columbia.  相似文献
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Visualization is crucial to the effective analysis of biological pathways. A poorly laid out pathway confuses the user, while a well laid out one improves the user’s comprehension of the underlying biological phenomenon.We present a new, elegant algorithm for layout of biological signaling pathways. Our algorithm uses a force-directed layout scheme, taking into account directional and rectangular regional constraints enforced by different molecular interaction types and subcellular locations in a cell. The algorithm has been successfully implemented as part of a pathway visualization and analysis toolkit named Patika, and results with respect to computational complexity and quality of the layout have been found satisfactory. The algorithm may be easily adapted to be used in other applications with similar conventions and constraints as well.Patika version 1.0 beta is available upon request at http://www.patika.org.  相似文献
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目的 为了提高轮廓检测的综合性能,特别是增强弱轮廓边缘的提取能力,在结合视觉机制的基础上提出了本文方法。方法 模拟视觉信息在视通路中的传递和处理过程,首先根据神经节细胞的中心周边拮抗机制,实现初级轮廓信息的快速提取;接着利用高斯函数与高斯差函数之间的差异性来模拟外膝体非经典感受野的调制作用,实现纹理背景的抑制;然后构建了一种V1区多朝向简单细胞感受野模型,提出了一种基于负值效应的DOG(difference of Gaussians)响应改进评价模式;最后考虑V1区复杂细胞在表征视觉高级特征的能力,给出了一种基于并行处理的视通路视觉响应融合模型,实现目标轮廓的检测与增强。结果 为了验证本文方法对自然场景图像的轮廓检测具备有效性,本文选取RuG轮廓检测数据库中的40幅自然场景图进行轮廓检测实验,并与二维高斯导函数模型(DG)、组合感受野模型(CORF)和空间稀疏约束纹理抑制模型(SSC)等3种典型的自然图像轮廓检测方法进行了分析比较。结果表明,本文方法检测提取到的主体轮廓更加完整,具有较高的图像纯净度,整体上反映了本文所提轮廓检测方法所具备的生物智能性。本文方法的平均P指标为0.45,相较于对比方法具有更好的轮廓检测性能。结论 本文方法具有较好的自然轮廓检测提取能力,尤其对于图像包含部分弱轮廓边缘的检测。本文构建的新模型将有助于对视通路中各层级功能和内在机制的理解,也将为基于视觉机制的图像分析和理解提供一种新的思路。  相似文献
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