Lane detection plays an important role in Driver Assistance Systems and Autonomous Vehicle System. In this paper, we propose a parallel-snake model combined with balloon force for lane detection. Parallel-snake is defined as two open active contours with parallel constrain. The lane boundaries on the left and right sides are assumed as parallel curves, parallel-snake is deformed to estimate these two boundaries. As lane regions between left and right boundaries usually have low gradient, snake will lose external force on these regions. Furthermore, inspired by balloon active contour model, the balloon force is introduced into parallel-snake to expand two parallel curves from center of road to the left and right lane boundaries. Different from closed active contour, stretching force is adopted to prevent the head and tail of snake from converging together. The experimental results on three different datasets show that parallel-snake model can work well on images with shadows and handle the lane with broken boundaries as the parallel property.

Vector field convolution (VFC) provides a successful external force for an active contour model. However, it fails to extract the complex geometries, especially the deep concavity when the initial contour is set outside the object or the concave region. In this letter, dynamically constrained vector field convolution (DCVFC) external force is proposed to solve this problem. In DCVFC, the indicator function with respect to the evolving contour is introduced to restrain the correlation of external forces generated by different edges, and the forces dynamically generated by complex concave edges gradually make the contour move to the object. On the other hand, traditional vector field, a component of the proposed DCVFC, makes the evolving contour stop at the object boundary. The connections between VFC and DCVFC are also analyzed. DCVFC maintains desirable properties of VFC, such as robustness to initialization. Experimental results demonstrate that DCVFC snake provides a much better segmentation than VFC snake.

In this paper, we propose a novel active contour method for image segmentation using a local regional information on extendable search line. We call it the LRES active contour. Our active contour uses the intensity values along a set of search lines that are perpendicular to the contour front. These search lines are used to inform the contour front toward which direction to move in order to find the object's boundary. Unlike other methods, none of these search lines have a predetermined length. Instead, their length increases gradually until a boundary of the object is found. We compare the performance of our LRES active contour to other existing active contours, both edge-based and region-based. The results show that our method provides more desirable segmentation outcomes, particularly on some images where other methods may fail. Not only is our method robust to noise and able to reach into a deep concave shape, it also has a large capture range and performs well in segmenting heterogeneous textured objects.

A new phase retrieval method using an active contour model (snake) for image reconstruction is proposed. The proposed method reconstructs a target image by retrieving the phase from the magnitude of its Fourier transform and the measured area of the image. In general, the measured area is different from the true area where the target image exists. Thus a snake, which can extract the shape of the target image, is utilized to renew the measured area. By processing this renewal iteratively, the area obtained by the snake converges to the true area and as a result the proposed method can accurately reconstruct a target image even when the measured area is different from the true area. Experimental results show the effectiveness of the proposed method.

This paper presents a motion-based boundary tracking method for a moving deformable object in an image sequence using a parametric active contour model. Deciding the local converging directions of the contour points is essential for correctly extracting the boundary of a moving deformable object. Thus, a new energy function for a parametric active contour model is proposed based on the addition of a directional energy term using a frame difference map to the greedy snake. The frame difference map is used to obtain motion information on an object with fast and non-rigid motion. Plus, updating rules for the frame difference map are also developed to encourage the stable convergence of the contour points. Experiments on a set of synthetic and real image sequences show that the proposed method could fully track a speedy deformable object while exactly extracting the boundary of the object in every frame.

In this paper we propose a road object extraction technique based on an active contour model (snake) considering inertia and differential features in a movie. Different energy functions can be applicable to snake in order to use information of various objects and various environments. Using many methods for tracking a moving object, snake can be applied to a scene frame by frame. Initial positions of the control points in a frame can refer to the results in the previous frame. We focus on the inertia that works between object shapes in the previous and present frames. In this research inertia is the tendency of a control point to resist its changes in its state of motion in an image space. We introduce an external energy for snake based on inertia of control points. Internal energy functions based on differential features of road geometry are also introduced to extract straight, circular and S-shaped road segments smoothly. The proposed method is applied to extract road geometry from a movie taken by a camera equipped on the flont of a vehicle. Experimental results indicate the availability of the proposed method which is to extract road geometry smoothly and to improve its robustness.

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.

This paper deals with contour extraction of fetus' head from echocardiogram and its application to diagnosis in obstetrics. Active contour model "SNAKES" is modified and used for contour extraction. After contour extraction we automatically obtained the biparietal diameter (BPD) and the occipitofrontal diameter (OFD) from the contour.

In this paper, we address the problem of recovering the camera radial distortion coefficients from one image. The approach that we propose uses a special kind of snakes called radial distortion snakes. Radial distortion snakes behave like conventional deformable contours, except that their behavior are globally connected via a consistent model of image radial distortion. Experiments show that radial distortion snakes are more robust and accurate than conventional snakes and manual point selection.

We introduce an efficient interpolation attack which gives the tighter upper bound of the complexity and the number of pairs of plaintexts and ciphertexts required for the attack. In the previously known interpolation attack there is a problem in that the required complexity for the attack can be overestimated. We solve this problem by first, finding the actual number of coefficients in the polynomial used in the attack by using a computer algebra system, and second, by finding the polynomial with fewer coefficients by choosing the plaintexts. We apply this interpolation attack to the block cipher SNAKE and succeeded in attacking many ciphers in the SNAKE family. When we evaluate the resistance of a block cipher to interpolation attack, it is necessary to apply the interpolation attack described in this paper.

This paper considers the problem of generating various calligraphy from some sample fonts. Our method is based on the deformable contour model g-snake. By representing the outline of each stroke of a character with a g-snake, we cast the generation problem into global and local deformation of g-snake under different control parameters, where the local deformation obeys the energy minimization principle of regularization technique. The base values of the control parameters are learned from given sample fonts. The experimental results on alphabet and Japanese characters Hiragana show such processing as a reasonable method for generating calligraphy.

In this paper, the authors present general views of computer vision and image processing based on optimization. Relaxation and regularization in both broad and narrow senses are used in various fields and problems of computer vision and image processing, and they are currently being combined with general-purpose optimization algorithms. The principle and case examples of relaxation and regularization are discussed; the application of optimization to shape description that is a particularly important problem in the field is described; and the use of a genetic algorithm (GA) as a method of optimization is introduced.

In this paper a new snake model for image morphing with semiautomated delineation which depends on Hermite's interpolation theory, is presented. The snake model will be used to specify the correspondence between features in two given images. It allows a user to extract a contour that defines a facial feature such as the lips, mouth, and profile, by only specifying the endpoints of the contour around the feature which we wish to define. We assume that the user can specify the endpoints of a curve around the features that serve as the extremities of a contour. The proposed method automatically computes the image information around these endpoints which provides the boundary conditions. Then the contour is optimized by taking this information into account near its extremities. During the iterative optimization process, the image forces are turned on progressively from the contour extremities toward the center to define the exact position of the feature. The proposed algorithm helps the user to easily define the exact position of a feature. It may also reduce the time required to establish the features of an image.