Active contours textural and inhomogeneous object extraction
Active contours textural and inhomogeneous object extraction
复制标题
DOI：
10.1016/j.patcog.2016.01.021
复制
发表时间：
2016-07-01
影响因子：
8
通讯作者：
Khan, Gulzar Ali
中科院分区：
文献类型：
--
作者：
Mahood, Lutful;Ali, Haider;Khan, Gulzar Ali
本文提出了一种新的选择性分割活动轮廓模型，该模型嵌入了增强的图像信息。通过利用处理纹理和噪声的平均通道图像（AIC），我们的模型能够选择性地分割和捕获具有不均匀特征的对象。此外，AIC 配备了定期更新的线性函数，以准确指导水平集函数处理非恒定强度。此外，我们利用几何约束方面的先验信息，与图像信息结合起来捕获强度不均匀的物体。实验表明，该方法比最新的选择性分割模型取得了更好的结果。此外，我们的方法保持了一些硬真实和合成彩色图像的性能。 (C) 2016 Elsevier Ltd. 保留所有权利。
A new selective segmentation active contour model is proposed in this paper that embeds an enhanced image information. By utilizing the average image of channels (AIC), which handles texture and noise, our model is capable to selectively segment and capture objects with nonuniform features. Moreover, the AIC is fitted with linear functions which are updated regularly to accurately guide the level set function to handle nonconstant intensities. Furthermore, we employ prior information in terms of geometrical constraints which work in alliance with image information to capture objects with intensity inhomogeneity. Experiments show that the proposed method achieves better results than the latest selective segmentation models. In addition, our approach maintains the performance on some hard real and synthetic color images. (C) 2016 Elsevier Ltd. All rights reserved.