In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

在本文中，我们提出了一种快速的多阶段图像分割方法，该方法将纹理分析合并到基于级别集的活动轮廓框架中。这种方法允许集成多种特征提取方法，并且不依赖于任何特定的纹理描述符。也不需要图像图案的先验知识。该方法从初始特征提取和选择开始，然后执行基于快速级别集的演化过程，最后以集成基于区域的模型的最终细化阶段结束。提出的实现采用了一组基于灰度共生矩阵，Gabor滤波器和结构张量的功能。GPU加速可实现特征提取和轮廓演化阶段的高性能。