An encountered-type haptic interface generates touch sensation only when a user's hand “encounters” virtual objects. This paper presents an effective encountered-type haptic interface that enables rendering of surfaces with variable curvature. The key idea is to systematically bend a thin elastic plate so as to create a curved surface with desired curvature, which becomes a contacting end effector that follows the user's finger and becomes an interface a user can touch when needed. The pose of the curvature is controlled in a way that it corresponds to the curved surfaces of virtual objects and user's finger position. The idea is realized by attaching two commercial haptic interfaces to both edges of a thin acryl plate and squeezing the plate. This setup allows us to generate a cylindrical object with curvature up to 0.035 mm-1 and gives 3DOF position control and 1DOF rotational control of the curved surface. Achievable workspace and curvature range are analyzed, and the feasibility and physical performance are demonstrated through a visuo-haptic grabbing scenario. In addition, a psychophysical experiment shows perceptual competence of the proposed system.

With the rapid expansion of vector data model application to digital content such as drawings and digital maps, the security and retrieval for vector data models have become an issue. In this paper, we present a vector data-hashing algorithm for the authentication, copy protection, and indexing of vector data models that are composed of a number of layers in CAD family formats. The proposed hashing algorithm groups polylines in a vector data model and generates group coefficients by the curvatures of the first and second type of polylines. Subsequently, we calculate the feature coefficients by projecting the group coefficients onto a random pattern, and finally generate the binary hash from binarization of the feature coefficients. Based on experimental results using a number of drawings and digital maps, we verified the robustness of the proposed hashing algorithm against various attacks and the uniqueness and security of the random key.

Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. The curvature-based feature varies in position when multi-scale analysis is performed. Therefore, it is important to recognize the scale in order to detect the feature point. Numerous shape descriptors based on contour shapes have been developed in the field of pattern recognition and computer vision. A curvature scale-space (CSS) representation cannot be applied to a contour fragment and requires the tracking of feature points. In a gradient-based curvature computation, although the gradient computation considers the scale, the curvature is normalized with respect to not the scale but the contour length. The scale-invariant feature transform algorithm that detects feature points from an image solves similar problems by using the difference of Gaussian (DoG). It is difficult to apply the SIFT algorithm to a planar curve for feature extraction. In this paper, an automatic scale detection method for a contour fragment is proposed. The proposed method detects the appropriate scales and their positions on the basis of the difference of curvature (DoC) without the tracking of feature points. To calculate the differences, scale-normalized curvature is introduced. An advantage of the DoC algorithm is that the appropriate scale can be obtained from a contour fragment as a local feature. It then extends the application area. The validity of the proposed method is confirmed by experiments. The proposed method provides the most stable and robust scales of feature points among conventional methods such as curvature scale-space and gradient-based curvature.

In this paper, we present a revision of using eigenvalues of covariance matrices proposed by Tsai et al. as a measure of significance (i.e., curvature) for boundary-based corner detection. We first show the pitfall of Tsai et al.'s approach. We then further investigate the properties of eigenvalues of covariance matrices of three different types of curves and point out a mistake made by Tsai et al.'s method. Finally, we propose a modification of using eigenvalues as a measure of significance for corner detection to remedy their defect. The experiment results show that under the same conditions of the test patterns, in addition to correctly detecting all true corners, the spurious corners detected by Tsai et al.'s method disappear in our modified measure of significance.

We propose the extended Bezier spiral in this paper. The spiral is useful for both design purposes and improved aesthetics. This is because the spiral is one of the Bezier curves, which play an important role in interactive curve design, and because the assessment of the curve is based on the human reception of the curve. For the latter purpose we utilize the logarithmic distribution graph that quantifies the designers' preferences. This paper contributes the unification of the two different curve design objectives (the interactive operation and so called "eye pleasing" result generation); which have been independently investigated so far.

Landscapes have been the main theme in Chinese painting for over one thousand years. Chinese ink painting is a form of non-photorealistic rendering. Terrain is the major subject in Chinese landscape painting, and surface wrinkles are important in conveying the orientation of mountains and contributing to the atmosphere. Over the centuries, masters of Chinese landscape painting have developed various kinds of wrinkles. This work develops a set of novel methods for rendering wrinkles in Chinese landscape painting. A three-dimensional terrain is drawn as an outline and wrinkles, using information on the shape, shade and orientation of the terrain's polygonal surface. The major contribution of this work lies in the modeling and implementation of six major types of wrinkles on the surface of terrain, using traditional Chinese brush techniques. Users can select a style of wrinkle and input parameters to control the desired effect. The proposed method then completes the painting process automatically.

This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.

A great deal of effort has been concentrated on the longitudinal control for the collision avoidance of moving vehicles. In an emergency as well as in a normal situation, however, the steering control can be a very effective alternative as observed in the practice of manual evasive driving. In the reported methods of steering control, it is found that the dynamic motions of the neighboring vehicles are often ignored, which may result in some danger of 2nd collision. Therefore, it is necessary to assess the surrounding traffic situation to prevent 2nd collision that can occur just after escaping from the 1st collision situation. In this paper, we tackle the collision avoidance problem when steering actuation control is allowed in consideration of the dynamic motion of the neighboring vehicles. Specifically, a hierarchical control scheme is suggested as a feasible solution, and the proposed system is verified via simulation using a software simulator called DevACAS (DEVeolper of Active Collision Avoidance System), which we have developed.

This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.

For object analysis and recognition, an original shape often needs to be described by using a small number of vertices. Polygonal approximation is one of the useful methods for the description. In this paper, we propose the curvature-based polygonal approximation (CBPA) method that is an application of the weighted polygonal approximation problem which minimizes the number of vertices of an approximate curve for a given error tolerance (the weighted minimum number problem). The CBPA method considers the curvature information of each vertex of an input curve as the weight of the vertex, and it can be executed in O(n2) time where n is the number of vertices of the input curve. Experimental results show that this method is effective even in the case when relatively few vertices are given as an original shape of a planar object, such as handwritten letters, figures (freehand curves) and wave-form data.

There are many instances in which character shape of a class changes smoothly to that of another class. Although there are many ways of the change, the most delicate change is curvature feature. The paper treat this problem systematically in both theoretically and experimentally. Specifically some confusing pairs were constructed which are well known in the field of OCR, such as 2 Z and 4 9. A series of samples generated using each model which change subtly were provided to conduct a psychological experiment. The results exhibit a monotone change of recognition rates from nearly 100% to 0% as the shape changes continuously. To imitate the humans' performance, feature of curvature was extracted based on continuous function representation based on Bezier's spline curve. Specifically two methods were tried from theoretical and engineering points of view and very successful results were obtained.

Surface reconstruction and visualization from sparse and incomplete surface data is a fundamental problem and has received growing attention in both computer vision and graphics. This paper presents a computational scheme for realistic visualization of free-formed surfaces from 3D range images. The novelty of this scheme is that by integrating computer vision and computer graphics techniques, we dynamically construct a mesh representation of the arbitrary view of the surfaces, from a view-invariant shape description obtained from 3D range images. We outline the principle of this scheme and describle the frame work of a graphical reconstruction model, we call arbitrarily oriented meshes', which is developed based on differential geometry. The experimental results on real range data of human faces are shown.