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Relief Extraction of Rough Textured Reflecting Surface by Image Processing

机译:图像处理的粗糙纹理反射面的浮雕提取

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In this paper, we present a method for relief extraction of rough textured reflecting surface by image processing. The rough textured reflecting surface is the surface which mixes both diffuse and specular components. Rough surface relief extraction is generally made by a mechanical method using a tactile sensor or by using an auto-focus laser sensor. With these sensors we can estimate surface relief from the analysis of a series of profiles. Since these measurements spend a lot of time, we hope that we can determine the relief by image processing. Several methods in the field of image processing have been proposed for relief extraction, such as shape from shading, optical flow, shape from focus and photometric stereovision. Our works are based on the photometric stereovision. In 1980, Woodham indicated that the relief of a Lambertian surface can be determined by the exploitation of a photometric model, which takes into account camera and light source positions according to the plan of surface. The proposed model expresses the gray level on the image according to the local relief variations. Three images of the same relief obtained under different angles of lighting are used to reconstruct the surface relief. From the method of Woodham, several important ameliorations have been proposed by other researchers. But a limit study in section 2.1.3 proves that the above methods worked with Lambert's model is adapted to the diffuse reflection, but not to the specular reflection. Thus, we propose another method to extract the relief of rough textured reflecting surface. In the proposed method, we show that the diffuse and specular components of the acquired images can be decomposed in two independent components. The diffuse component can be processed by Lambert's model, the specular component can be processed according to the position knowledge of facets. Finally, section 3 presents the experimental results obtained by this method, and compares measurement precision with the experimental results obtained by Lambert's model.
机译:在本文中,我们提出了一种通过图像处理来浮雕提取粗糙纹理反射面的方法。粗糙的纹理反射面是混合了漫反射和镜面反射分量的表面。通常通过使用触觉传感器的机械方法或通过使用自动聚焦激光传感器来进行粗糙表面起伏提取。使用这些传感器,我们可以通过分析一系列轮廓来估计表面起伏。由于这些测量花费大量时间,因此我们希望可以通过图像处理来确定浮雕。已经提出了在图像处理领域中用于浮雕提取的几种方法,诸如阴影的形状,光流,聚焦的形状和光度立体视觉。我们的作品基于光度立体视觉。 1980年,伍德汉姆(Woodham)指出,可以通过使用光度模型来确定朗伯表面的起伏,该模型根据表面的计划将照相机和光源的位置考虑在内。所提出的模型根据局部浮雕变化在图像上表达灰度。在不同的照明角度下获得的同一浮雕的三个图像用于重建表面浮雕。根据伍德汉姆的方法,其他研究人员提出了一些重要的改进措施。但是在2.1.3节中的极限研究证明,上述与Lambert模型一起使用的方法适用于漫反射,而不适用于镜面反射。因此,我们提出了另一种方法来提取粗糙的纹理反射表面的浮雕。在提出的方法中,我们表明,所获取图像的漫反射和镜面反射分量可以分解为两个独立的分量。可以使用Lambert模型处理漫反射分量,可以根据构面的位置知识来处理镜面反射分量。最后,第3节介绍了通过这种方法获得的实验结果,并将测量精度与通过Lambert模型获得的实验结果进行了比较。

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