Advanced Land Observing Satellite-2 (ALOS-2) is a spaceborne Synthetic Aperture Radar (SAR) system, developed by Mitsubishi Electric Corporation under contract to Japan Aerospace Exploration Agency (JAXA). The SAR system named Phased Array type L-band Synthetic Aperture Radar-2(PALSAR-2) is installed to ALOS-2. Its unique frequency of L-band is expected continuous investigation such as forest, volcanoes, and earthquake activities. Compact polarimetry mode and Along track interferometry (ATI) mode are available in PALSAR-2 as experimental mode. The evaluation of each experimental mode has been completed and reported in this paper. Compact polarimetry mode is that H and V polarization are transmitted at the same time, therefore the swath can be maintained. Each polarization status of compact polarimetry is calculated from the covariance matrix using some assumption. The full polarimetry-like image can be obtained as first evaluation. Along track interferometry is the technique to detect the moving target. ALOS-2 has two receiver channels. The moving target and its velocity are obtained from the interferometric image of each channel. The velocity can be calculated from the phase difference between two images. This result compared with another method using Doppler shift to validate it.

In this paper, we describe a novel image-based person identification task. Traditional face-based person identification methods have a low tolerance for occluded situation, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces a restriction of the installation location of a camera and solves the problem of occluded images. In addition, we utilize depth information for the identification task. We apply four features to the identification method, (1) estimated body height, (2) estimated body dimensions, (3) estimated body size and (4) depth histogram. Experimental result shows the effectiveness of our method.

In this paper, we propose a method for speech understanding using a corpus. First, the method extracts keywords from an N‐best list of speech recognizer output. This process employs two measures: a confidence measure of speech recognizer output and an association probability between words. Next, the method uses dependencies between keywords in a corpus. Finally, it evaluates relations (tagged labels) of the dependencies. Our method obtained high accuracy as compared with a method with only the confidence measure of the speech understanding module. The results show the effectiveness of the proposed method.

A dialogue plays an important role in learning how to solve a problem and form a concept. We are developing a problem solving and knowledge acquisition system based on co‐reference between drill texts and dialogue with a teacher, focusing on first‐grade mathematics. This paper presents a method of cooperative understanding of utterances and gestures within dialogue. We first describe our system design principles, which provide the basis for the integration of multimodal information during a dialogue. We define a principle of complementarity, explain its implementation, and describe the architecture of the problem solving system. We then show how to integrate our algorithms for utterance and gesture analysis within that software architecture. A feature‐based approach is used for gesture recognition, derived from a sequence of images arising during the cooperative analysis of utterances. We conclude with an evaluation of the system against the design principles.

A story generation system based on dynamics of the mind is presented. Semantic theories until now avoided discussing a linkage of language with encyclopedic human knowledge. This paper attacks the problem of how to make clear the complicated, wide‐ranging dynamics of the mind and how to connect it to language. First, the background of this study is shown, in which our model of the mind, with both five levels along the concept formation process and eight domains according to the contents of mental activities, is surveyed. Based on this model, then, the dynamics of mind are discussed, along an Aesop fable. A unit of data processing in the mind is called a “module,” and the mental dynamics are considered as a chain activation of those modules, represented by a dynamic network. Next, a method for story generation is discussed. Propositional information of a sentence is embedded in a slot of activated, frame‐structured modules, and the discourse structure of a generated story basically depends on the dynamic network. Both the chain activation and the generation processes are verified by experiments. Lastly, residual problems of our research, a comparison with related research, and its applications are discussed. This case study would be expected to give a basis to integrated systems for problem solving, natural language and image understandings, their generations, and intelligent robots.

A picture coding strategy titled DF-expression (Depth-First picture expression) is now studied from another point of view. The basic idea of DF-expression is briefly reviewed at first. Its capability in data compression is demonstrated using 1024 × 1024 binary pictures. Then the new aspects of DF-expression are studied in reference to picture processing algorithm on the coded form. They include circular shiftings, spectrum of primitives, logical operations, etc. Application of DF-expression to gray images (or multivalued pictures) is the next topics of the paper. DF-expression is applicable to bit-plane coding of any binary image data. Our conclusion with this point is that the gray code is the best binary code system for DF-expression in information preserving sense. Information-lossy type data reduction is another topics in this paper. The authors propose a new data reduction technique in terms of uniformalization of excessively complicated regions in bit-planes. Experimental study follows using a 256 × 256 sized fourbit test picture. Finally, conclusions and other possibility of the applications are remarked.

A method of representing a binary pictorial pattern is developed. Its original idea comes from a sequence of terminal symbols of a context-free grammar. It is a promising technique of data compression for ordinary binary-valued pictures such as texts, documents, charts, etc. Fundamental notions like complexity, primitives, simplifications, and other items about binary-valued pictures are introduced at the beginning. A simple context-free grammar G is also introduced. It is shown that every binary-valued picture is interpretable as a terminal sequence of that G. The DF-expression is defined as the reduced terminal sequence of G. It represents the original picture in every detail and contains no surplus data for reproducing it. A quantitative discussion about the total data of a DF-expression leads to the conclusion that any binary-valued picture with complexity less than 0.47 is expressed by the DF-expression with fewer data than the original ones. The coding algorithm of original data into the DF-expression is developed. It is very simple and recursively executable. Experiments were carried out using a PDS (photo digitizing system), where test pictures were texts, charts, diagrams, etc. with 20 cm × 20 cm size. Data compression techniques in facsimile were also simulated on the same test pictures. Throughout these studies it was made clear that the DF-expression is a very effective technique as a data compression for binary pictorial patterns not only because it yields high data compression but also because its coding and decoding algorithms are very feasible.