Abstract In chemistry, computer graphics is now well established as a tool to interpret simulation results, since molecules are complicated in their structures and mutual interactions. As a probe to study such molecular interactions, electrostatic fields are considered to be useful. However, since they are given as 3D vector fields having cusps in the fields, conventional drawing techniques are not applicable. In this article, two new approaches are presented to visualize the electrostatic fields of molecules. One is an extension of topological skeletons, by which interactions between atoms having opposite charges are expressed, which is not shown with the conventional methods. The other is to define new functions called selective functions to select regions of interest only from the geometrical features of the fields. Furthermore, from the definition of the new functions, mathematical relations between the topological skeletons and selective functions are discussed. An example is presented in applications to chemical reactions to show how the scheme is used. Copyright © 2001 John Wiley & Sons, Ltd.

Describes a method for vehicle recognition, in particular for recognizing make and model. Our system takes into account the fact that vehicles of the same make and model number come in different colors; it employs infrared (IR) images, thereby eliminating color differences. The use of IR images also enables us to use the same algorithm both day and night. This ability is particularly important because the algorithm must be able to locate many feature points, especially at night. Our algorithm is based on a configuration of local features. For the algorithm, our system first makes a compressed database of local features of a target vehicle from training images given in advance; the system then matches a set of local features in the input image with those in the training images for recognition. This method has the following three advantages: (1) it can detect even if part of the target vehicle is occluded; (2) it can detect even if the target vehicle is translated due to running out of the lanes; and (3) it does not require us to segment a vehicle part from input images. We have two implementations of the algorithm. One is referred to as the eigenwindow method, while the other is called the vector-quantization method. The former method is good at recognition, but is not very fast. The latter method is not very good at recognition but it is suitable for an IMAP parallel image-processing board; hence, it can be fast. In both implementations, the above-mentioned advantages have been confirmed by performing outdoor experiments.

This paper proposes a method for obtaining surface orientations of transparent objects using polarization in highlight. Since the highlight, the specular component of reflection light from objects, is observed only near the specular direction, it appears merely on limited parts on an object surface. In order to obtain orientations of a whole object surface, we employ a spherical extended light source. This paper reports its experimental apparatus, a shape recovery algorithm, and its performance evaluation.

The paper describes a method for vehicle recognition, in particular, for recognizing a vehicle's make and model. Our system employs infra-red images so that we can use the same algorithm both day and night. Originally, the algorithm was the eigen-window method based on local features, but it has been changed to a vector quantization based algorithm which was originally proposed by J. Krumm (1997), to implement on an IMAP parallel image processing board. Any of these systems, based on both the eigen-window method and the vector quantization method, make a compressed database of local features for the algorithm of a target vehicle from given training images in advance; the system then matches a set of local features in the input image with those in training images for recognition. This method has the following three advantages: (1) it can detect even if part of the target vehicle is occluded; (2) it can detect even if the target vehicle is translated due to running out of lanes; (3) it does not require us to segment a vehicle from input images. The above advantages have been confirmed by performing outdoor experiments.