The issue about document structure recognition and document understanding is today one of interesting subjects from a viewpoint of practical applications. The research objective is to extract the meaningful data from document images interpretatively and also classify them as the predefined item data automatically. In comparison with the traditional image-processing-based approaches, the knowledge-based approaches, which make use of various knowledge in order to interpret structural/constructive features of documents, have been currently investigated as more flexible and applicable methods. In this paper, we propose a totally integrated paradigm for understanding table-form documents from a viewpoint of the architectural framework.

In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.

In this paper, we extend two-image photometric stereo method to treat a concave polyhedron, and present an iterative algorithm to remove the influence of interreflections. By the method we can obtain the shape and reflectance of a concave polyhedron with perfectly diffuse (Lambertian) and unknown constant reflectance. Both simulation and experiment show the feasibility and accuracy of the method.

A network system is proposed for segmenting and extracting multiple moving objects in 2D images. The system uses an interconnected neural network in which grouping factors, such as edge proximity, smoothness of edge orientatio, and smoothness of velocity perpendicular to an edge, are embedded. The system groups edges so that the network energy may be minimized, i.e. edges may be organized into perceptually plausible configuration. Experimantal results are provided to indicate the performance and noise robustness of the system in extracting objects in synthetic images.

We discuss the uniqueness of 3-D shape reconstruction of a polyhedron from a single shading image. First, we analytically show that multiple convex (and concave) shape solutions usually exist for a simple polyhedron if interreflections are not considered. Then we propose a new approach to uniquely determine the concave shape solution using interreflections as a constraint. An example, in which two convex and two concave shapes were obtained from a single shaded image for a trihedral corner, has been given by Horn. However, how many solutions exist for a general polyhedron wasn't described. We analytically show that multiple convex (and concave) shape solutions usually exist for a pyramid using a reflectance map, if interreflection distribution is not considered. However, if interreflection distribution is used as a constraint that limits the shape solution for a concave polyhedron, the polyhedral shape can be uniquely determined. Interreflections, which were considered to be deleterious in conventional approaches, are used as a constraint to determine the shape solution in our approach.