In this paper, we present a study of transferable belief model for automatic hair segmentation process. Firstly, we recall the transferable Belief Model. Secondly, we defined for the parameters which characterize hair (Frequency and Color) a Basic Belief assignment which represents the belief that a pixel was or not a hair pixel. Then we introduce a discounting function based on the distance to the face to increase the reliability of our sensors. At the end of this process, we segment the hair with a matting process. We compare the process with the logical fusion. Results are evaluated using semi-manual segmentation references.

In this paper, we present an automatic method for hair segmentation. Our algorithm is divided into two steps. Firstly, we take information from frequential and color analysis in order to create binary masks as descriptor of the hair location. Secondly, we perform a 'matting treatment' which is a process to extract foreground object from an image. This approach is based on markers which positions are initialized from the fusion of frequential and color masks. At the end the matting treatment result is use to segment the hair. Results are evaluated using semi- manual segmentation references.

Lips segmentation is a very important step in many applications such as automatic speech reading, MPEG-4 compression, special effects, facial analysis and emotion recognition. In this paper, we present a robust method for unsupervised lips segmentation. First the color of the lips area is estimated using expectation maximization and a membership map of the lips is computed from the skin color distribution. The region of interest (ROI) is then found by automatic thresholding on the membership map. Given a mask of the ROI, we initialize a snake that is fitted on the upper and lower contour of the mouth by multi level gradient flow maximization. Finally to find the mouth corners and the final contour of the mouth, we use a parametric model composed of cubic curves and Bezier curves.

Mouth segmentation is an important issue which applies in many multimedia applications as speech reading, face synthesis, recognition or audiovisual communication. In this paper, we propose a method based on a statistical model of shape and appearance to detect the lips. To create the model, the outline of the lips and teeth has to be manually annotated with 30 key-points on a few visemes (450). Once the model has been trained on this set, it is used for segmentation. After a step to situate mouth corners, the goal is to find the parameters to fit the model to an unknown image. The originalities of this work are (a) an initialization step which broadly classify lip and skin pixels, (b) the mouth corners local model, and (c) the automatically extracted dynamic and static sampled-appearance which are well adapted to describe the mouth area and its components.

Lip segmentation is an essential stage in many multimedia systems such as videoconferencing, lip reading, or low-bit-rate coding communication systems. In this paper, we propose an accurate and robust quasi-automatic lip segmentation algorithm. First, the upper mouth boundary and several characteristic points are detected in the first frame by using a new kind of active contour: the "jumping snake." Unlike classic snakes, it can be initialized far from the final edge and the adjustment of its parameters is easy and intuitive. Then, to achieve the segmentation, we propose a parametric model composed of several cubic curves. Its high flexibility enables accurate lip contour extraction even in the challenging case of a very asymmetric mouth. Compared to existing models, it brings a significant improvement in accuracy and realism. The segmentation in the following frames is achieved by using an interframe tracking of the keypoints and the model parameters. However, we show that, with a usual tracking algorithm, the keypoints' positions become unreliable after a few frames. We therefore propose an adjustment process that enables an accurate tracking even after hundreds of frames. Finally, we show that the mean keypoints' tracking errors of our algorithm are comparable to manual points' selection errors.

Lip segmentation is an essential stage in many multimedia systems such as videoconferencing, lip reading, or low bit rate coding communication systems. In this paper, we propose an accurate and robust lip segmentation algorithm. First, the upper mouth boundary and several characteristic points are detected by using a new kind of active contour : the "jumping snake". Unlike classic snakes, it can be initialized far from the final edge and the adjustment of its parameters is easy and intuitive. In a second step, a parametric model composed of several cubic curves is fitted on the lips. This model is flexible enough to reproduce the specificities of very different lip shapes. Compared to existing models, it brings a significant accuracy and realism improvement.

Lip segmentation is an essential stage in many multimedia systems such as videoconferencing, lip reading, or low bit rate coding communication systems. In this paper, we propose an accurate and robust lip segmentation algorithm. First, the mouth region and several characteristic points are detected by using "hybrid edges" (which combine colour and intensity information) and a priori knowledge about the lip morphology. Corners position, which is crucial, is provided by a coarse-to-fine process. Then, a parametric model is fitted on the lips. We consider that the lip boundary is composed of several independent cubic polynomial curves. It gives a low complexity global model that is flexible enough to reproduce the specificity of very different lip shapes. Compared to existing models, it brings a significant accuracy and realism improvement. Moreover, it ensures a robust convergence towards the edges because the different parts of the model are independent.

Lip segmentation is an essential stage in many multimedia systems such as videoconferencing, lip reading, or low bit rate coding communication systems. We propose an accurate and robust lip segmentation algorithm. First, characteristic points are found by using "hybrid edges" (which combine color and intensity information) and a priori knowledge about the lip morphology. Corners position, which is crucial, is provided by a coarse-to-fine process. Then, a parametric model is fitted on the lips. We consider that the lip boundary is composed of several independent cubic polynomial curves. It gives a low complexity model that is flexible enough to reproduce the specificity of very different lip shapes. Compared to existing models, it brings a significant accuracy improvement. Moreover, it ensures a robust convergence towards the edges.

A joint algorithm-architecture analysis leads to a new version of picture segmentation system adapted to multimedia mobile terminal constraints. The asynchronous processors network, with a granularity level of one processor per pixel, based on data flow model, takes less than 10 /spl mu/s to segment a SQCIF $88*72 pixels - image (about 2000 times faster than the classical sequential watershed algorithms). The main originality of the proposed algorithm is only one global synchronization point is needed in order to complete the segmentation transformation, instead of the three (or more) classical points: minima detection, labelization and flooding. Our system tends to cope with multimedia mobile phones constraints, i.e. real time computing circuit, low power. We have simulated and validated this system thanks to "SystemC" library; VHDL synchronous prototyping shows up results accordingly.

A robust pre-processing algorithm for lip segmentation is presented. We define a new transformation based on RGB color space: the chromatic curve map. It aims at increasing discrimination between lips and skin. It allows robust lips detection under non uniform lighting conditions and without any particular make-up. The motivation of the present work is to extract visual information for automatic speech recognition, videoconferencing and speaker's face synthesis under natural lighting conditions.

Active contours or snakes are widely used in object segmentation for their ability to integrate feature extraction and pixel candidate linking in a single energy minimizing process. But the sensitivity to parameters values and initialization is also a widely known problem. The performance of snakes can be enhanced by better initialization close to the desired solution. We present a fine mouth region of interest (ROI) extraction using gray level image and corresponding gradient information. We link this technique with an original snake method. The automatic snakes use spatially varying coefficients to remain along its evolution in a mouth-like shape. Our experimentations on a large image database prove its robustness regarding speakers change of the ROI mouth extraction and automatic snakes algorithms. The main application of our algorithms is video-conferencing.