英语翻译One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments.We extend previous approaches following this framework by requiring that our image model sharply decreases

英语翻译One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments.We extend previous approaches following this framework by requiring that our image model sharply decreases
英语翻译
One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments.We extend previous approaches following this framework by requiring that our image model sharply decreases in its power to organize the image as a segment’s boundary is perturbed from its true position.Instead of making segment boundaries prefer image edges,we add a term to the objective function that seeks a sharp change in fitness with respect to the entire contour’s position,generalizing from edge detection’s search for sharp changes in local image brightness.We also introduce a prior on the shape of a salient contour that expresses the observed multi-scale distribution of contour curvature for physical contours.We show that our new term correlates strongly with salient structure.We apply our method to real images and verify that the new term improves performance.Comparisons with other state-of-the-art approaches validate our method’s advantages.

英语翻译One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments.We extend previous approaches following this framework by requiring that our image model sharply decreases
One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments.一种图像分割的方法定义了图像分割区的函数,分割区的最大值对应着感性的凸出分割.
We extend previous approaches following this framework by requiring that our image model sharply decreases in its power to organize the image as a segment’s boundary is perturbed from its true position我们拓展了以前的方法接下来的这个通过要求我们的图像模式在它的能量区大幅度减少来组织图像作为一个分割边界线的框架是从它的真实位置干扰的.
Instead of making segment boundaries prefer image edges,we add a term to the objective function that seeks a sharp change in fitness with respect to the entire contour’s position,generalizing from edge detection’s search for sharp changes in local image brightness取代作出分割边界线更喜欢图像边缘,我们增加了一个描述客观函数的课题,来寻求一个适合对整个轮廓线的位置大幅变化,在局部图像亮度里从边缘检测搜索大幅变化推广下去.
We also introduce a prior on the shape of a salient contour that expresses the observed multi-scale distribution of contour curvature for physical contours.我们也引进一个凸出轮廓线形状的前提,来描述观察到的物理性轮廓线的多尺度分布弧度
We show that our new term correlates strongly with salient structure我们展示出我们新的课题强烈关联了凸出结构.
We apply our method to real images and verify that the new term improves performance.我们将我们的方法应用到真实的图像上并且验证了新课题可以提高性能
Comparisons with other state-of-the-art approaches validate our method’s advantages.与其他陈述——艺术方法相比较,有效地印证了我们方法的优势.

这么长才5分。不干。

一种方法,定义了一个函数图像分割的图像分割极大值对应的感知显著的细分市场。我们把以前的方法证明在这个框架内,要求我们的形象急剧减少模型在其权力范围内组织图像作为部分的边界从它的实际位置。摄动不要让段边界喜欢图像的边缘,我们增加了目标函数的词句来寻求在健康急剧变化对整个外形推广的位置,从边缘检测的搜索的强烈需求,地方形象的改变亮度。我们也介绍前显著形状的轮廓,体现了多尺度分布曲率的轮廓,观察物理轮廓...

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一种方法,定义了一个函数图像分割的图像分割极大值对应的感知显著的细分市场。我们把以前的方法证明在这个框架内,要求我们的形象急剧减少模型在其权力范围内组织图像作为部分的边界从它的实际位置。摄动不要让段边界喜欢图像的边缘,我们增加了目标函数的词句来寻求在健康急剧变化对整个外形推广的位置,从边缘检测的搜索的强烈需求,地方形象的改变亮度。我们也介绍前显著形状的轮廓,体现了多尺度分布曲率的轮廓,观察物理轮廓。我们发现我们的新学期有强烈相关显著的结构。我们运用我们的方法并验证实际图像新学期改进效能。与其他先进的方法比较验证我们的方法的优点。

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