This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.

Recently, researches and developments for measuring and modeling of the human body have been receiving much attention. Our aim is to reconstruct an accurate shape of a human foot from multiple camera images, which can capture dynamic behavior of the object. In this paper, a foot-shape database is used for accurate reconstruction of human foot. By using Principal Component Analysis, the foot shape can be represented with new meaningful variables. The dimensionality of the data is also reduced. Thus, the shape of object can be recovered efficiently, even though the object is partially occluded in some input views. To demonstrate the proposed method, two kinds of experiments are presented: reconstruction of human foot in a virtual reality environment with CG multi-camera images, and in real world with eight CCD cameras. In the experiments, the reconstructed shape error with our method is around 2 mm in average, while the error is more than 4 mm with conventional volume intersection method.

Image segmentation is an essential technique of image analysis. In spite of the issues in contour initialization and boundary concavities, active contour models (snakes) are popular and successful methods for segmentation. In this paper, we present a new active contour model, Gaussian Gradient Force snake (GGF snake), for segmentation of an endoscopic image. The GGF snake is less sensitive to contour initialization and it ensures a high accuracy, large capture range, and fast CPU time for computing an external force. It was observed that the GGF snake produced more reasonable results in various image types : simple synthetic images, commercial digital camera images, and endoscopic images, than previous snakes did.

Unlike the time-consuming contour tracking method of snakes [5] which requires a considerable number of iterated computations before contours are successfully tracked down, we present a faster and accurate model-based landmarks" tracking method where a single iteration of the dynamic programming is sufficient to obtain a local minimum to an integral measure of the elastic and the image energy functionals. The key lies in choosing a relatively small number of salient land-marks", or features of objects, rather than their contours as a target of tracking within the image structure. The landmarks comprising singular points along the model contours are tracked down within the image structure all inside restricted search areas of 41 41 pixels whose respective locations in image structure are dictated by their locations in the model. A Manhattan distance and a template corner detection function of Singh and Shneier [7] are used as elastic energy and image energy respectively in the algorithm. A first approximation to the image contour is obtained in our method by applying the thin-plate spline transformation of Bookstein [2] using these landmarks as fixed points of the transformation which is capable of preserving a global shape information of the model including the relative configuration of landmarks and consequently surrounding contours of the model in the image structure. The actual image contours are further tracked down by applying an active edge tracker using now simplified line search segments so that individual differences persisting between the mapped model contour are substantially eliminated. We have applied our method tentatively to portraits of a class album to demonstrate the effectiveness of the method. Our experiments convincingly show that using only about 11 feature points our method provides not only a much improved computational complexity requiring only 0.94sec. in CPU time by SGI's indigo2 but also more accurate shape representations than those obtained by the snakes methods. The method is powerful in a problem domain where the model-based approach is applicable, possibly allowing real time processing because a most time consuming algorithm of corner template evaluation can be easily implemented by parallel processing firmware.

Active net is a deformable model which utilizes the network analogy of a physical region. In the model, the region of a target is detected by minimizing the energy defined for the sample points of the model. The region of the target is extracted using fixed network topology in the orginally proposed algorithm. In this paper, we introduce the network reconfiguration mechanisms such as tearing and division to realize multiple objects detection and complex object detecion. The introduced algorithm dynamically unlinks the arcs of the network when their strain value exceeds predefined threshold level. In the method, we propose a new image energy which improves the position sensitivity of edges without increasing computation cost. Experimental results for images taken by video camera show the validity of our approach.

A modified deformable model is presented for constructing bijective topology preserving feature maps. The algorithm can solve the optimization problem in the input space as well as that in the output space. A saturating distance function alternative to the Euclid norm is employed to obtain compact space filling maps.

We describe an approach for modelling a person's face for model-based coding. The goal is to estimate the 3D shape by combining the contour analysis and shading analysis of the human face image in order to increase the quality of the estimated 3D shape. The motivation for combining contour and shading cues comes from the observation that the shading cue leads to severe errors near the occluding boundary, while the occluding contour cue provides incomplete surface information in regions away from contours. Towards this, we use the deformable model as the common level of integration such that a higher-quality measurement will dominate the depth estimate. The feasibility of our approach is demonstrated using a real facial image.