Subakan Ozlem N, Vemuri Baba C
Department of Computer and Information Science and Engineering.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008;2008:1-6. doi: 10.1109/CVPR.2008.4587460.
Image segmentation is a fundamental task in Computer Vision and there are numerous algorithms that have been successfully applied in various domains. There are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present a novel approach that does not make use of any prior information about the objects in the image being segmented. Segmentation is achieved using local orientation information, which is obtained via the application of a steerable Gabor filter bank, in a statistical framework. This information is used to construct a spatially varying kernel called the Rigaut Kernel, which is then convolved with the signed distance function of an evolving contour (placed in the image) to achieve segmentation. We present numerous experimental results on real images, including a quantitative evaluation. Superior performance of our technique is depicted via comparison to the state-of-the-art algorithms in literature.
图像分割是计算机视觉中的一项基本任务，有许多算法已在各个领域成功应用。但仍有诸多挑战有待应对。在本文中，我们考虑其中一个挑战，即在保留图像（无论是灰度图像还是纹理图像）中存在的复杂和详细特征的同时实现分割。我们提出了一种新颖的方法，该方法不使用关于正在分割的图像中对象的任何先验信息。分割是在统计框架中使用通过应用可操纵的伽柏滤波器组获得的局部方向信息来实现的。此信息用于构建一个称为里高特核的空间变化核，然后将其与演化轮廓（放置在图像中）的符号距离函数进行卷积以实现分割。我们展示了在真实图像上的大量实验结果，包括定量评估。通过与文献中最先进的算法进行比较，展示了我们技术的卓越性能。
