Active contour model for satellitale image combining local and global region based approach

<p>Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works. </p><p><em> </em></p>

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.

Implicit Active Contour Model with Local and Global Intensity Fitting Energies

We propose a new active contour model which integrates a local intensity fitting (LIF) energy with an auxiliary global intensity fitting (GIF) energy. The LIF energy is responsible for attracting the contour toward object boundaries and is dominant near object boundaries, while the GIF energy incorporates global image information to improve the robustness to initialization of the contours. The proposed model not only can provide desirable segmentation results in the presence of intensity inhomogeneity but also allows for more flexible initialization of the contour compared to the RSF and LIF models, and we give a theoretical proof to compute a unique steady state regardless of the initialization; that is, the convergence of the zero-level line is irrespective of the initial function. This means that we can obtain the same zero-level line in the steady state, if we choose the initial function as a bounded function. In particular, our proposed model has the capability of detecting multiple objects or objects with interior holes or blurred edges.

Region-Based Level Set Model for Image Segmentation

To reduce the impact of intensity inhomogeneity to image segmentation, a region-based level set (RBLS) model was proposed in this study. Its energy functional consists of four terms: local term, area term, length term and penalty term. The proposed model utilizes both global image information and local image information, and by using the local image information, the image with intensity inhomogeneity can be efficiently segmented. In addition, the global implementation of our RBLS model is introduced. It can detect all of the targets in the image. The experimental results showed that the proposed model can segment the image with intensity inhomogeneity efficiently, which is better than that of CV model.