The distance transformation of a digital picture (DT) is one of the most important techniques in image processing. In this paper, we propose a new variant called the max-type distance transformation for binary pictures (max-type DT). This transformation can be compared to the propagation of a wave, which propagates with a constant velocity to the pixels whose connectivity numbers are less than two. The connectivity number is calculated on the binary pattern obtained by regarding the pixels which the wave has not reached yet as 1-pixels and all the others as 0-pixels. The transformed value of a pixel is defined as the time when the wave reaches the pixel. After the transformation, the pixels on the core line of an input picture are given larger distance values increasing along the core line. Therefore, the max-type DT is effectively used in thinning and structure analysis of binary pictures. In the structure analysis using the max-type DT, both distance information and topological properties of binary pictures are used together. As an example, experimental results using hand-printed characters are shown. The max-type DT can also be applied to extract shape features by measuring the length of the core line such as the major axis of an ellipse.

Based on a newly proposed notion of relational network, a novel learning mechanism for model acquisition is developed. This new mechanism explicitly deals with both qualitative and quantitative relations between parts of an object. Qualitative relations are mirrored in the topology of the network. Quantitative relations appear in the form of generalized predicates, that is, predicates that are graded in their validity over a certain range. Starting from a decomposition of binary objects into meaningful parts, first a description of the decomposition in terms of relational networks is obtained. Based on the description of two or more instances of the same concept, generalizations are obtained by first finding matchings between instances. Generalizing itself proceeds on two levels: the topological and the predicate level. Topological generalization is achieved by a simple rule-based graph generalizer. Generalization of the predicates uses some ideas from MYCIN. After successful generalization, the system attempts to derive a simple and coarse description of the achieved result in terms of near natural language. Several examples underline the validity of relational networks and illustrate the performance of the proposed system.

A line display that utilizes saccade has been proposed. When an observer moves his or her eyes on a one-dimensional fixed line display, two-dimensional information is perceived on the retina. In this paper, a high speed flashing line display was developed using a CPLD and PIC microcontroller. The flashing period was reduced to 20 µs, which was less than half that of our previous system. The relationship between the flashing frequency and the optimum distance that can be perceived with the least distortion was clarified. The results show that the higher the flashing frequency is, the more information can be perceived from a farther position. Calculated values, which were based on the relationship between the flashing period and the width of the light source, were almost identical with measured values at the flashing frequencies from 3.3 kHz to 10 kHz. Due to short flashing period, the developed line display not only was visible at distance of 15 m or more, which is suitable for outdoor use, but also realized 16 gray levels.

Many different edge detectors have been proposed. Most of them output the edge intensity and the edge orientation as edge features. In this paper we state necessity of a measure which can discriminate a clear edge with small edge height from a noisy edge with large edge height. To find such a measure as an edge feature, we analyze variances within a window around the edge and propose an edge-feature extractor based on this analysis. Then it is noticed that the traditional edge intensity can be considered as two elements: edge height and edge reliability. In multiple edge cases, the condition is clarified for calculating accurate edge locations by analyzing the edge-height function. From this analysis we suggest a method for determining edge points by thresholding edge height. Our detector is compared to Canny's detector both in synthetic models and in a real image and it is demonstrated that our method produces better results in edge locations than Canny's. We also show that our method can detect edges with low edge height and high edge reliability.