Image segmentation is an essential component in many image processing and computer vision tasks. The primary goal of image segmentation is to simplify an image for easier analysis, and there are two broad approaches for achieving this: edge based methods, which extract the boundaries of specific known objects, and region based methods, which partition the image into regions that are statistically homogeneous. One of the more prominent edge finding methods, known as the level set method, evolves a zero-level contour in the image plane with gradient descent until the contour has converged to the object boundaries. While the classical level set method and its variants have proved successful in segmenting real images, they are susceptible to becoming stuck in noisy regions of the image plane without a priori knowledge of the image and they are unable to provide details beyond object outer boundary locations. We propose a modification to the variational level set image segmentation method that can quickly detect object boundaries by making use of random point initialization. We demonstrate the efficacy of our approach by comparing the performance of our method on real images to that of the prominent Canny Method.
翻译:图像分割是许多图像处理和计算机视觉任务的一个基本组成部分。 图像分割的首要目标是简化图像,以便于分析。 实现这一目的有两种广泛的方法: 边缘方法,用来提取特定已知物体的界限,以及区域方法,把图像分割成统计上相同的区域。 较突出的边缘查找方法之一,称为水平设定方法,在图像平面上以梯度下降为零级等距,直到轮廓与对象边界交汇为止。 虽然古典水平设定方法及其变异方法已证明成功地分离了真实图像,但它们很容易被困在图像平面的吵闹区域,而没有事先了解图像,无法在目标外部边界地点之外提供细节。 我们提议修改变异水平设定图像分割方法,通过随机点初始化,可以快速探测物体边界。 我们通过将我们关于真实图像的方法的性能与突出的坎尼方法的性能进行比较,来证明我们的方法的有效性。