Fan J, Yau D Y, Elmagarmid A K, Aref W G
Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN 47907, USA.
IEEE Trans Image Process. 2001;10(10):1454-66. doi: 10.1109/83.951532.
We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.
我们提出了一种新的自动图像分割方法。首先，通过结合改进的各向同性边缘检测器和快速熵阈值技术自动获取图像中的彩色边缘。在获得的彩色边缘提供了图像中的主要几何结构之后，将这些相邻边缘区域之间的质心作为种子区域生长（SRG）的初始种子。然后，通过逐步合并所需的额外像素，将这些种子替换为生成的均匀图像区域的质心。此外，将彩色边缘提取和SRG的结果进行整合，以提供具有精确且封闭边界的均匀图像区域。我们还讨论了我们的图像分割方法在自动面部检测中的应用。此外，通过将检测到的面部作为对象种子的种子区域聚合过程生成语义人类对象。
