In a simulated world, a group of robots with diversified personalities performs a task where they carry several pieces of baggage to the destination. In this simulation, each robot makes a mutual concession to avoid collision and helps each other to carry some of baggage too big to be carried by one robot. Moreover, each robot makes learning the personalities of other robot in this simulation. We verify the influence that the learning the personalities exerts on the task efficiency. We give each robot three kinds of personalities; positivity, tenderness and temper. From the result, it was found that each robot has more versatile actions when each robot learns the personalities of the other robots, and the versatile actions of the robot result in better task efficiency. Thus, in this study, the learning of the others' personalities worked effectively, as in the case of biological evolution.

We describe a method for navigating a mobile robot in the environment using signboards in scenes as landmarks. This method will enable us to realize human-like navigation and map-generation, and also can benefit human-robot communication. We choose signboards on walls and doors as landmarks. An environment map that contains the approximate position of each signboard in the environment is generated by the robot when the robot explores the environment the first time, and it will be used for re-localizing and navigating the robot afterward. First, characters in scene images are detected by using several heuristics of characters and character lines. Then, we calculate the relative orientation of the camera and a detected signboard from a single view using two methods based on the geometry of perspective projection. Finally, we reconstruct a distorted signboard image using two corresponding methods, and this makes the recognition of characters under a wider range of viewing condition much easier.

To create a more realistic soccer game derived from TV images, we developed an image synthesis system that generates an image sequence from the viewpoint of a player on the field. This system is based on the camera calibration theory. The system first determines the camera parameters of a TV image by using the intersection points of the white lines drawn on the soccer field. It then extracts players from each image and estimates their positons in the world coordinate system. Finally it applies a running motion to the players in their respective positions and generates computer graphics animation from the viewpoint of any player selected by a user. The system was tested over seven sequences of TV images and demonstrated satisfactory results.

Regional image emphasis is often evident in paintings and illustrations. This technique increases local contrast while reducing global contrast by amplifying image intensity on shadowed surfaces, reducing intensity on illuminated surfaces, and then expanding contrast at intensity edges. The effects are assumed to result from the visual processing needed to interpolate the real world onto canvas. Therefore, we propose an intensity emphasis method based on human vision. This method simulates the adaptation of photoreceptor cells and the lateral inhibition of receptive fields. These attributes of a vision system are realized by computation of relative intensity and differential intensity in small areas. The proposed method can successfully generate painting‐like artifacts, which greatly improves the perception of visual elements displayed in an image. Since the method efficiently reduces the dynamic range of images, it can be used for displaying highlighted images on standard graphic monitors. Experiments on a computer‐generated image and a photograph confirm the advantages of our method.

This paper describes a fast analytic algorithm that generates exact highlights and soft shadows from area light sources. In order to realize fast shadowing, we propose the ray‐oriented buffer which segments 3D space by following light rays from polygonal sources. Each cell of the buffer stores objects that intersect a related subspace. Candidate objects which may cast shadows onto a point are selected by referring to the buffer. The candidates are then tested with their shadow bounding volumes to suppress objects that never occlude light sources. In addition, we propose the cross scanline clipping algorithm. It quickly determines the exact regions of uncovered area light sources with simple silhouette generation. Both diffuse and specular reflections are computed by integrating light rays from the uncovered sources. Experimental results confirm the high performance of the proposed method.

Abstract This paper presents a fast shadowing algorithm for linear light sources that uses a ray‐oriented buffer. Space segmentation by the buffer guarantees that if a point is included in a subspace, all light rays toward the point are also contained in the subspace. Each cell of the buffer stores a list of objects that lie within or intersect the subspace allocated to the cell. Therefore, candidate objects, those that may cast shadows onto a point, are determined by referring to the cell where the point is mapped. In addition, whether each candidate object actually casts shadows or not is tested with the bounding‐volume of the shadow space to reduce the number of objects subjected to expensive light clipping. The bounding‐volumes are also stored in the buffer. For efficiently generating the ray‐oriented buffer, we present the cylindrical scan‐conversion algorithm. The algorithm preconverts objects' surfaces to trapezia to decrease the light clipping cost, then connects the trapezia to the buffer cells. Due to the above improvements, our algorithm achieves over 10 times faster shadow generation compared to the conventional methods. Experimental results confirm that our method can generate realistic images with soft shadows in a few minutes.

Abstract We propose the Cross Scanline Buffer which preserves the result of hidden surface removal as performed by the Cross Scanline Algorithm. The Cross Scan Buffer reduces image re‐generation time and eliminates aliasing artifacts even if the image is arbitrarily scaled. Perfect anti‐aliasing is achieved because the Cross Scanline Algorithm analytically determines visible polygonal surfaces and divides them into sets of triangles and trapezia. The Cross Scan Buffer supports the various applications that currently use the conventional buffering methods for anti‐aliasing. This paper introduces and tests three applications: image scaling, shadow creation, and texture mapping. Experimental results verify that the Cross Scan Buffer is very powerful yet efficient.

A new parallel processing algorithm for solving string search problems is presented. The proposed algorithm uses O(m*n) processors where n is the length of a text and m is the length of a pattern. It requires two and only two iteration steps to find the pattern in the text, while the best existing parallel algorithm needs the computation time O(loglog n).>

This paper derives a shading model for area light sources which covers both diffuse and specular reflection. The shading model assumes ideally diffuse polygonal light sources and uses Phong’s reflection model. The model can accurately integrate the intensities of diffuse and specular reflection without simulating an area light source as an array of point light sources. To simplify the reflection integration, each light source is transformed from the Cartesian coordinate system into the polar system. The light source is projected onto a unit sphere and then triangulated along great circles of the unit sphere. Finally, the integration value is calculated by polynomial approximation. Since our method can accurately integrate both diffuse and specular reflection, it can generate images that are more photorealistic than conventional methods. Because point light sources are not employed, highlight roughness is completely suppressed. Several images are presented that show the advantages of our method.

This paper proposes a new hidden surface removal algorithm which is based on the scanline algorithm but scans in two directions, horizontally and vertically. Named the cross scanline algorithm, it can efficiently detect all polygons and calculate their exact projected areas in each pixel even if the polygons are much smaller than the pixel. Comparisons with the regular sub-scanlines algorithm show that high quality anti-aliased images can be generated.

A parallel algorithm based on neural networks for solving sorting problems is presented. The proposed algorithm usesO(n2) processing elements called binary neurons, wherenis the number of unsorted elements. It requires two and only two iteration steps, while the conventional parallel sorting algorithm using (On) processors proposed by F.T. Leighton (1984) requires the computation timeO(logn). A set of simulation results substantiates the proposed algorithm. The hardware system based on the proposed parallel algorithm is also presented