textureless ['tekstʃəlis]

textureless的基本解释

adj.

无明显结构的, 无定形的

与textureless相关的网络例句 [注：此内容来源于网络，仅供参考]

Experimental results show that the proposed algorithm has good performance especially in the occluded, textureless and discontinuity regions.

实验结果表明：该算法具有很好的性能，尤其在遮挡区域、无纹理区域和视差不连续区域。

The existing image matching methods work well in open areas with relatively smooth terrain. However, they still meet many problems in the case of larger scale images of nature, especially in the dense city centers, forestry areas and the textureless regions such as water bodies. The reliability of image matching is still a big issue in these cases. For example, the corners of buildings and some object boundary points sometimes cannot be matched successfully, and there are many blunders in the automatically derived digital surface models that need to be manually removed, which leads to the decrease of the automaticity in digital photogrammetry.

现有大多数立体影像匹配方法对于地形平滑的开阔地带具有较高的可靠性，然而，面对从千变万化的自然界所摄取的影像，现有的匹配方法仍然存在许多问题，尤其是在植被茂密、建筑物密集和水域等区域，影像匹配的可靠性还不高，一些建筑物的角点和地物的主要边界点还不能正确地匹配出来，自动生成的数字表面模型中存在着许多异常值，需要进行大量的人工编辑处理，这在很大程度上制约了数字摄影测量自动化水平的提高。

The combation of disparity consistency and matching cost are used to detect occlusion area. Fast and effective image segmentation is applied for extracting textureless and texture regions. The matching problem of textureless regions is then solved with constraint of disparity consistency and credibility concept. At the meantime, high credible seeds are obtained. Based on the smooth constraint of matching and disparity of seeds, dense disparity map is obtained via the adaptive-window based region growing.

然后采用视差一致性和匹配代价相结合的方法来检测遮挡区域；采用一种快速有效的图像分割算法提取了低纹理区和纹理区域，结合视差一致性和置信度约束来有效地解决低纹理区的匹配问题，同时得到一系列具有高置信度的种子点；根据立体匹配中的平滑性约束和种子点的视差，利用自适应窗口的区域增长技术最终得到致密的视差图。

Then hierarchical cluster analysis is adopted to classify all the 37 wool-like fabrics into 5 groups: warp double wool-like fabrics, elastic wool-like fabrics, thin wool-like fabrics, heavy wool-like fabrics and textureless wool-like fabrics.

在以上分析的基础上，运用主因子分析所建立的仿毛织物风格评价体系、相关性分析所揭示的仿毛织物风格显著影响因素、系统聚类分析得到的仿毛织物类别特征等，指导重经类和弹性类仿毛织物设计并试样。