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Examples of applications include [[blob detection]], [[corner detection]], [[ridge detection]], and object recognition via the [[scale-invariant feature transform]].
 
Examples of applications include [[blob detection]], [[corner detection]], [[ridge detection]], and object recognition via the [[scale-invariant feature transform]].
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In computer vision and biological vision, scaling transformations arise because of the perspective image mapping and because of objects having different physical size in the world. In these areas, scale invariance refers to local image descriptors or visual representations of the image data that remain invariant when the local scale in the image domain is changed.Lindeberg, T. (2013) Invariance of visual operations at the level of receptive fields, PLoS ONE 8(7):e66990. 
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在计算机视觉和生物视觉中,由于图像的透视映射和世界上物体的物理尺寸不同而产生了缩放变换。在这些区域中,尺度不变性是指当图像域的局部尺度发生变化时,图像数据保持不变的局部图像描述符或视觉表示。
Detecting local maxima over scales of normalized derivative responses provides a general framework for obtaining scale invariance from image data.T. Lindeberg (2014) "Scale selection", Computer Vision: A Reference Guide, (K. Ikeuchi, Editor), Springer, pages 701-713.
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Examples of applications include blob detection, corner detection, ridge detection, and object recognition via the scale-invariant feature transform.
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在计算机视觉和生物视觉中,由于透视图像映射和物体在世界上有不同的物理大小,缩放变换产生。在这些领域中,图像尺度不变性是指当图像域的局部尺度改变时保持不变的图像数据的局部图像描述符或视觉表示。视觉操作在感受野水平的不变性,PLoS ONE 8(7) : e66990。检测标准化导数响应的局部极大值为从图像数据中获取尺度不变性提供了一个通用框架。林德伯格(2014)“比例选择”,《计算机视觉: 参考指南》 ,(k. Ikeuchi,编辑) ,Springer,701-713页。应用的例子包括斑点检测、角检测、脊线检测和通过尺度不变特征转换识别系统进行的目标识别。
      
==See also 另见==
 
==See also 另见==
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