In this paper we present a new algorithm to extract and locate partially visible objects from a 2D image. For each model object, the algorithm cuts a prominent fragment front its boundary. Next, using a genetic algorithm, the algorithm finds an image fragment with the best match to the model fragment from match boundary of the image objects. As a result, the corresponding model is extracted and located from the image. The proposed algorithm can be used for the recognition of objects whose scales, rotations and translations are unknown. In addition, the algorithm can locate objects whose boundaries are distorted.

Pattern information and natural language information used together can complement and reinforce each other to enable more effective communication than can either medium alone. A good example is a pictorial book of flora (PBF). In the PBF, readable explanations combine texts and pictures. However, it is difficult to retrieve explanation text and pictures from the PBF when we don't know the names of flowers. To solve this problem, we propose a retrieval method for the PBF using the color feature of each flower and fruit, and construct an experimental retrieval system for the PBF. For obtaining the color feature of each flower and fruit, we analysed the PBF pictures and found several problems as follows: Pictures of the PBF contain many kinds of objects. In addition to flowers and fruits, there are leaves, stems, skies, soils, and sometimes humans in the PBF pictures. The position, size, and direction of flowers and fruits vary quite widely in each picture. Each flower and fruit has its unique shape, color, and texture which are commonly different from those of the others. Because of these problems, it is difficult to build the general and precise model for analyzing the PBF pictures in advance. We propose a method for image analysis using natural language information. Our method works as follows. First, we analyse the PBF explanation texts for extracting the color information on each flower and fruit. Then, we analyse the PBF pictures by using the results of the natural language processing, and finally obtain the color feature of each flower and fruit.

Pattern recognition and object detection systems so far developed required the algorithmic description of every detail of the objects to be recognized by bottom-up process from pixel-to-pixel relation to line, corner, and structural description. Because this low-level process does not see global information, feature detection is highly sensitive to noise. To overcome this problem and to give human-like flexibility to machine recognition process, we developed a new system which had non-algorithmic feature detection functions by seeing a comparatively large area at once. It uses a variable size window which is applied to the most plausible parts in an image by a top-down command from an object model, and obtains characteristic features of object parts. This window application is realized mostly in hardware, and has some autonomic ability to detect the best features by a sort of random trial and error search. The system has some other hardware functions such as mutual correlation of one- and two-dimensional patterns, which are also flexible according to the variable size window. The system interprets user's declarative description of objects, and activates the window application functions to obtain characteristic features of the description. This new flexible approach of object detection can be used as a robot eye to recognize many simple two-dimensional shapes.

Diagram understanding and its cooperative use with other media are important subjects in both pattern understanding and communication. However, it is quite difficult to understand diagrams without supplementary explanation by other media. For this purpose, the authors propose a new framework for semantic understanding of a diagram by utilizing textual information. In this framework, the elements in a diagram are tightly linked to words or other structure in a natural language text, and the semantic structure of a diagram is interpreted clearly by using natural language information. The obtained result contributes to the construction of hypertext and multimedia.>

Introduces a system (PAFE) which provides a flexible framework for feature extraction. This system is constructed based on the multi agents model, in which many agents work co-operatively and concurrently. The objective of this system is to achieve flexible control and coarse-grained parallelism of feature extraction by organizing feature extraction as parallel graph search.>

This paper describes the outline of our Japanese to English machine translation system, which is supported by the Agency of Science and Technology of the Japanese Government. Many new methodologies are introduced to obtain high-quality translation results. The analysis is based on case grammar, which is suitable for a word-order-free language such as Japanese. The dictionary is rich enough to handle many specific expressions. It contains not only case frame information, but also semantic information, idiomatic expressions, and many others. In the transfer phase, the system applies many structural transformations, so that the structural difference of the same contents in Japanese and English can be relieved. In the generation phase, many structural transformations are again applied so that the ellipsis problems can be avoided, and that better stylistic expressions can be obtained. The system is running mainly for the abstracts of scientific and technical papers. The evaluation method of the translated results is also discussed, with many example translations.

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A powerful grammar writing system has been developed. This grammar writing system is called GRADE (GRAmmar DEscriber). GRADE allows a grammar writer to write grammars including analysis, transfer, and generation using the same expression. GRADE has powerful grammar writing facility. GRADE allows a grammar writer to control the process of a machine translation. GRADE also has a function to use grammatical rules written in a word dictionary. GRADE has been used for more than a year as the software of the machine translation project from Japanese into English, which is supported by the Japanese Government and called Mu-project.

Analysis grammar of Japanese in the Mu-project is presented. It is emphasized that rules expressing constraints on single linguistic structures and rules for selecting the most preferable readings are completely different in nature, and that rules for selecting preferale readings should be utilized in analysis grammars of practical MT systems. It is also claimed that procedural control is essential in integrating such rules into a unified grammar. Some sample rules are given to make the points of discussion clear and concrete.