Spatial intensity variations caused by illumination changes have been a challenge for image segmentation and many other computer vision tasks. This paper presents a novel method for image segmentation with simultaneous estimation of illumination and reflectance images. The proposed method is based on the composition of an observed scene image with an illumination component and a reflectance component, known as intrinsic images. We define an energy functional in terms of an illumination image, the membership functions of the regions, and the corresponding reflectance constants of the regions in the scene. This energy is convex in each of its variables. By minimizing the energy, image segmentation result is obtained in the form of the membership functions of the regions. The illumination and reflectance components of the observed image are estimated simultaneously as the result of energy minimization. With illumination taken into account, the proposed method is able to segment images with non-uniform intensities caused by spatial variations in illumination. Comparisons with the state-of-the-art piecewise smooth model demonstrate the superior performance of our method.

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.