Expansive graph grammar is one of the suitable tools for describing pattern structures in syntactic pattern recognition. To describe the numerical features of patterns and relations among them simultaniously with pattern structure, in this paper we show with expansive graph grammar: (1) how to describe the dependent relations among attributes of (sub) patterns with the concepts of inherited and synthesized attributes, (2) how to describe conditions supposed to be satisfied by attributes of patterns with the concept of predicate. An interpreter for such processing is presented based on the Problem-Reduction Representation (PRR). Some features of the method and the interpreter are discussed. It is showed that the method introduced in this paper is suitable for describing patterns in scene analysis when numerical features of patterns are considered.

Although the line-of-sight (LoS) is expected to be useful as input methodology for computer systems, the application area of the conventional LoS detection system composed of video camera and image processor is restricted in the specialized area, such as academic research, due to its large size and high cost. There is a rapid eye motion, so called 'saccade' in our eye motion, which is expected to be useful for various applications. Because of the saccade's very high speed, it is impossible to track the saccade without using high speed camera. The authors have been proposing the high speed vision chip for LoS detection including saccade based on the pixel parallel processing architecture, however, its resolution is very low for the large size of its pixel. In this paper, we propose and discuss an architecture of the vision chip for LoS detection including saccade based on column-parallel processing manner for increasing the resolution with keeping high processing speed.

It is an important topic to express solid objects in CAD, CAM and CG with a uniform way. In this article, a constructive dispersed surface points-set model (CDSPM) is proposed. CDSPM is made up by extracting the dispersed surface points sets from the simpler shapes, and then compose the points sets through a sequence of new defined Boolean set operation rules. Using these rules, solid object can be defined step by step, and the redundant data can be easily eliminated. In the article, a novel rendering method using the CDSPM medel data is introduced. A re-projection and pixel interpolation method enable us to effectively generate a reasonable image from a set of discrete surface points.

In this paper, we introduce the concepts of multi-node and mono-node to expand the expansive graph grammar given by Shi and Fu to a larger family, the family that contains all rooted acyclic directed graphs. Shi and Fu's approach deals with rooted acyclic digraphs containing only mono-nodes. The ways of numbering and representing rooted acyclic directed graphs are modified and grammars for such graphs are given so that directed graphs containing even multi-nodes can be treated. Parsing algorithms are also given which are described with State-Space representation. Our algorithms are as efficient as those of Ref.(6). We have removed a constraint that the basis graph must be a tree from the expansive graph grammars of Ref.(6) without increasing the space or time complexity of numbering, rewriting and parsing.

Rapid eye motion, or so called saccade, is a very quick eye motion which always occurs regardless of our intention. Although the line of sight (LOS) with saccade tracking is expected to be used for a new type of computer-human interface, it is impossible to track it using the conventional video camera, because of its speed which is often up to 600 degrees per second. Vision Chip is an intelligent image sensor which has the photo receptor and the image processing circuitry on a single chip, which can process the acquired image information by keeping its spatial parallelism. It has also the ability of implementing the very compact integrated vision system. In this paper, we describe the vision chip architecture which has the capability of detecting the line of sight from infrared eye image, with the processing speed supporting the saccade tracking. The vision chip described here has the pixel parallel processing architecture, with the node automata for each pixel as image processing. The acquired image is digitized to two flags indicating the Purkinje's image and the pupil by comparators at first. The digitized images are then shrunk, followed by several steps of expanding by node automata located at each pixel. The shrinking process is kept executed until all the pixels disappear, and the pixel disappearing at last indicates the center of the Purkinje's image and the pupil. This disappearing step is detected by the projection circuitry in pixel circuit for fast operation, and the coordinates of the center of the Purkinje's image and the pupil are generated by the simple encoders. We describe the whole architecture of this vision chip, as well as the pixel architecture. We also describe the evaluation of proposed algorithm with numerical simulation, as well as processing speed using FPGA, and improvement in resolution using column parallel architecture.

Existing solid models often contain redundant primitives and null blocks, which both slows down the rendering process and makes the process complex. There has been recent progress toward solving this problem, but existing modeling schemes cannot support eliminating all the redundancies, especially the null blocks, from the solid models. This paper proposed a technique that can eliminate redundancies. By dividing a primitive into some surface dispersed points, a new primitive representation is obtained. The sample segments of the primitive or the object are used to locate composition position to prevent the null primitives from being generated. By drawing out the geometric shape points set corresponding to a common acting area, the volume boundary of a primitive or an object is evaluated by only the Boolean set operations. The null blocks can be picked out in terms of the volume boundary. The resulting solid model generated in this way has no redundancies and is suitable for fast rendering of the image.

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.

In this paper, we propose a method to construct structure models of articulated objects from multiple local observations of their motion using state transition analysis of local geometric constraints. The object model is constructed by a bottom-up approach with three levels. Each level groups sensor data with a constraint among local features observed by the sensor, and constructs the local model. If the sensor data in current model conflict, the model is reconstructed. In each level, the first level estimates a local geometric feature from the local sensor data (eg. edge, feature point) The second level estimates a rigid body from the local geometric feature. The third level estimates an object from the rigid bodies. In the third level, the constraint between rigid bodies is estimated by transition states, which are motions between rigid bodies. This approach is implemented on a blackboard system.