Research On Geometric Active Contour Models For Image Segmentation

Image segmentation is a classical and crucial problem in the fields of image understanding. Geometric active contour models impletemented via curve evolution and level set method are used as a powerful tool to address a wide range of image segmentation problems. There are several desirable advantanges of geometric active contour models over classical image setmentation methods. First, geometric active contour models are capable of describing the topology change of contours, and can provide smooth and closed contours as segmentation results. Sencond, geometric active countour are numerically stable and not sensitive to initial conditions.Our works are mainly on some problems in segmenting inhomogeneous images by geometric active contour models and our major contributions in this paper are as follows.Firstly, Classical region-based geometric active contours (e.g. C-V model) only take intensity homogeneity as the similarity measure for regions, and can not obtain satisfactory segmentation results of complicated images. Thus, a fast active contour model based on Earth Mover's Distance (EMD) is proposed and well adapted to segment images. First, a similarity measure based on EMD is proposed and employed to the segmentation model. Then, a novel regularization and curve evolution method using oversegmentation is enforced to improve the numerical accuracy and evolution efficiency. Experimental results of both synthetic and remote sensing images verify that the algorithm is efficient and accurate.Sencondly, due to the fact that the segmentation accuracy of local binary fitting energy based variational model (LBF model) is highly dependent on kernel bandwidth, and it always lead to unsatisfactory segmentation results (e.g., unnecessary contours, rough boundaries) of inhomogeneous images because of inappropriate bandwidth, an novel edge-preserving local fitting model is proposed and well adapted to segment images with intensity inhomogeneity. First, a geodesic time based kernel using spatial location and spectral gradient is defined, and it provide an adaptive geodesic neighborhood for every pixel. Then, an efficient multichannel gradient based extension combined with adjusted dissimilarity measure is enforced to segment color and multispectral images. Experiments results show that the proposed model could remain potential edge information while using larger bandwidth, and desirable segmentation results of both gray and color images can be obtained.After that, the main attention is paid on Graph Portioning Active Contours (GPAC). Recently, a new region-based active contour model based on pairwise similarity between pixels, i.e., GPAC is presented, and well adapted to segment images with intensity homogeneity. However, it only takes spectral similarity as the cost function between vertices, and can not obtain satisfactory segmentation results for low contrast images with weak boundaries. In order to overcoming this limitation of GPAC model, a novel localized graph-cuts based multiphase active contours model using geodesic kernel based cost function is proposed. Experimental results of natural images verify that the model is efficient and accurate.Finally, Due to the fact that classical active contour models for SAR image segmentation are highly dependent on statistical distributions, a novel active contour model based on pairwise region similarity is proposed and well adapted to segment SAR images. First, the image is initially divided into almost homogenous regions with high accuracy. Then, a region similarity measure using intensity and texture is defined and employed to energy functional. Finally, an efficient regularization and curve evolution method based on oversegmentation is enforced to improve the numerical accuracy and evolution efficiency. Experiments of SAR images show that the proposed model can fast and accurately obtain segmentation results of SAR images.