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Lip segmentation is one of critical steps in a lip-reading system, because it closely relates to the accuracy of system recognition. In this paper, we aim to improve the accuracy of lip segmentation. A novel color space is proposed which consists of the $U$ component in the CIE-LUV space and the sum of $C$2 and $C$3 components of the image after discrete Hartley transform (DHT). We select a rhombus as the initial contour as its shape is approximate to a closed lip shape relatively. These notions are achieved based on the method of the Active contour model. The active contour model (ACM) is performed by the Chan–Vese model, and the result of each component is gained separately. Finally, the ultimate results are obtained by merging the result of each component together. Through experiments we can get a conclusion that this method can get more accurate and smoother lip contour. Meanwhile, the proposed method is more efficient compared with the classic ACM because it avoids some problems in the classic active contour model, like the radius of the initial contour needs to be set manually according to the size of images.

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

This paper presents a unified variational framework for seamlessly integrating prior segmentation information into non-rigid registration procedures. Under this framework, in addition to the forces arise from the similarity measure in seeking for detailed correspondence, another set of forces generated by the prior segmentation contours can provide an extra guidance in assisting the alignment process towards a more meaningful, stable and noise-tolerant procedure. Local correlation (LC) is being used as the underlying similarity measures to handle intensity variations. We present several 2D/3D examples on synthetic and real data.