In recent facsimile application fields, mixed documents comprising characters and photographs have come to be generally treated. Following this trend, the `joint bi-level image group (JBIG)' of ISO/IEC/JTC1/SCWG9 and CCITT/SG VIII prepared an international standard for the encoding of binary images obtained by quantizing mixed documents into binary levels. This JBIG coding scheme consists of two parts: (1) the modeling part based on binary Markov model referring to 10 pixels surrounding a current pixel to be encoded and (2) the coding part based on an adaptive arithmetic compression coder. This paper presents an adaptive model for mixed binary images, which realizes higher compression efficiency than the typical Markov model of the JBIG scheme. We describe two significant characteristics, generalized model and area classification. The generalized model refers not only to neighboring pixels like the typical Markov model, but also to a predicted gray-level calculated pixel by pixel from previously scanned pixels near a current pixel. The area classification classifies mixed binary images including both characters and halftone images into two types of areas. In this adaptive model, the typical Markov model and the generalized model for halftone images are selected according to the types of the areas. As a result, the adaptive model reduces the values of Markov entropy of mixed binary images and realizes high compression efficiency.

A method is proposed to extract two-dimensional shapes which are similar to a given model shape from a binary image composed of black and white pixels. This extraction problem is equivalent to the problem of determining the position, size, and rotational angle of each similar shape in the binary image. The model shape is transformed with four space transformation parameters, (chi) c, yc, M, and (theta) , and the transformed model shape is overlapped with the binary image. Parameters (chi) c and yc denote xy coordinate values of the center of gravity of the transformed model shape, M is magnification ratio and (theta) is rotational angle. The research goal here is to find out the space transformation parameter set that gives the maximum matching rate between the transformed model shape and a similar shape in the binary image. Genetic algorithm (GA), which is a kind of searching or optimizing algorithm, is employed for this problem. In this method, several virtual living things whose chromosomes represent space transformation parameters are randomly generated in a computer, and they are evolved according to GA. As the result of generation iterations, an evolved individual corresponding to the best space transformation parameter set is obtained. Algorithm of the method and several experimental results are described.

A new algorithm is proposed by which the circular arcs or straight lines in the original drawings are transformed into straight lines in a U-V complex plane. After these parameters of straight lines in the U-V plane have been extracted, circular arcs and straight lines in the original drawings are recognized. The results show that the extraction time and memory storage are reduced.>

We propose a new method to extract arbitrary shapes such as lines, circles, ellipses and other complex shapes, from noisy binary images. In this method, shapes in a given binary image are traced by the tracer named PVFT (Pseudo View Field Tracer ). The movement of PVFT is similar to that of the view field of a man who recognizes arbitrary shapes with a restricted view field. That is, PVFT selects line segments of a shape in a noisy image, and traces them. Moreover, movement of PVFT is controlled according to the shape to be extracted.

This paper presents an automatic method of interpolation or inbetweening for gray valued images, which have been difficult to be processed automatically. The method consists of three parts. The first i s the extraction of density contour lines from a pair of gray valued key frame images, the second i s the interpolation of the contour lines and the l a s t is the generation of in-between gray valued images. In the second part, the correlation of corresponding contour lines of the key frames i s carried out automatically utilizing some kinds of similarity functions. The method is applied to a pair of pictures of a plaster head photographed slightly different directions and generates a very smooth rotating animation picture of the p l a s t e r head. This method has much applicability to computer animation and motion picture analysis. Stereo pair images are available from key frame pictures of objects photographed from any direction. Interpolation of color images is also possible.

An algebra is proposed for compactly generating and describing binary pictures such as textures and graphics. Four arithmetic operations, differentiation, and integration of such pictures are defined using pictorial constants and rational polynomials. Examples for these operations are illustrated.

An algebraic system for binary digital pictures has already been described, along with the definition of the four arithmetic rules. In this paper, an extension of the binary algebraic system to a 2n-valued one is first proposed. It then becomes evident that this extended algebraic system satisfies several properties including those of a ring. An example of a 2n-valued model, an eight-valued algebraic system, is introduced and applied to painted digital pictures. Pictorial operations such as multiple arrangement, enlargement, differentiation, integration, and color changes are then dealt with by the combinations of the four arithmetic rules.

A description method of handprinted Chinese characters is presented. In the method, a Chinese character is composed of some partial patterns which are constructed using the concatenate relation, cross relation, and near relation. The relations of relative location among partial patterns are used for categorization of the partial pattems. A Chinese character is expressed from the results of categorization.

In this paper we propose a coding method of handprinted Chinese characters in which, defining a grammar, the process of block categorization is made to correspond to a process of sentential generation. We obtain two strings of production rules and block codes being equivalent to the descriptions of the structure and internal components of a character pattern, and a Chinese character is encoded effectively by the use of these two strings. It is shown that this coding method is available for the classification and discrimination of handprinted Chinese characters.

A syntactic method is applied to the recognition of Korean characters (Hangul). Since they develop into complex characters by the sequential addition of fundamental characters under positioning rules, there are a large amount of characters and consequently there exist many similar characters. Therefore, the sequential extraction, according to the positioning rules, of fundamental characters composing Korean characters is effective for automatic recognition. As a structural analysis, a production process of fundamental characters is represented by tree grammars, and the extraction algorithm of fundamental characters and the results of computer simulation are described.