Hirobumi Nishida

Recognizing plant species by leaf shapes-a case study of the Acer family

In this paper, a method for recognizing plant species by the shapes of the leaves is presented. The recognition is based on a hierarchical representation of shape features. First, the structures of the shapes of leaves are analyzed and the shapes of components are extracted in detail. The structures and detailed shapes of leaves are approximated by polygons whose vertices are critical points of curvature of contours of leaves. The method also takes variations of shapes of leaves of the same species into account. Experimental results using a number of species in the family of Acer (Maple) indicate that structures of leaves and shapes of their components can be considered as landmarks to recognize the species of plants.

Algebraic description of curve structure

The authors propose a compact and concise method of describing curves in terms of the quasi-topological features and the structure of each singular point. The quasi-topological features are the convexity, loop, and connectivity. The quasi-topological structure is analyzed in a hierarchical way, and algebraic structure is presented explicitly for each representation level. The lower-level representations are integrated into the higher-level one in a systematic way. When a curve has singular points (branch points), the curve is decomposed into components, where each is a simple arc or a simple closed curve, by decomposing each singular point. The description scheme is applied to character recognition. >

An algebraic approach to automatic construction of structural models

We present algebraic approach to the inductive learning of structural models and automatic construction of shape prototypes for character recognition on the basis of the algebraic description of curve structure proposed by Nishida and Mori (1991, 1992). A class in the structural models is a set of shapes that can be transformed continuously to each other. We consider an algebraic representation of continuous transformation of components of the shape, and give specific properties satisfied by each component in the class. The generalization rules in the inductive learning are specified from the viewpoints of continuous transformation of components and relational structure among the components. The learning procedure generalizes a pair of classes into one class incrementally and hierarchically in terms of the generalization rules. We show experimental results on handwritten numerals. >

Structural feature indexing for retrieval of partially visible shapes

Abstract Efficient and robust information retrieval from large image databases is an essential functionality for the reuse, manipulation, and editing of multimedia documents. Structural feature indexing is a potential approach to efficient shape retrieval from large image databases, but the indexing is sensitive to noise, scales of observation, and local shape deformations. It has now been confirmed that efficiency of classification and robustness against noise and local shape transformations can be improved by the feature indexing approach incorporating shape feature generation techniques (Nishida, Comput. Vision Image Understanding 73 (1) (1999) 121–136). In this paper, based on this approach, an efficient, robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. The effectiveness is confirmed by experimental trials with a large database of boundary contours obtained from real images, and is validated by systematically designed experiments with a large number of synthetic data.

Model-based shape matching with structural feature grouping

An essential problem in online handwriting recognition is the shape variation along with the variety of stroke number and stroke order. In this paper we present a clear and systematic approach to shape matching based on structural feature grouping. To cope with topological deformations caused by stroke connection and breaking, we incorporate some aspects of top-down approaches systematically into the shape matching algorithm. The grouping of local structural features into high-level features is controlled by high-level knowledge as well as the simple geometric conditions. The shape matching algorithm has the following properties from the viewpoint of online character recognition: (1) stroke order, direction, and number are free, and (2) stroke connection and breaking are allowed. >

Shape Recognition by Integrating Structural Descriptions and Geometrical/Statistical Transforms

The prime difficulty in research and development of handwritten character recognition systems is the variety of shape deformations. The key to recognizing such complex objects as handwritten characters is through shape descriptions which are robust against shape deformation, together with quantitative estimation of the amount of deformation. In this paper, on the basis of the structural description by Nishida and Mori (1992), we propose a shape matching algorithm and a method for analysis and description of shape transformation for handwritten characters. The object is described in terms of a qualitative and global structure which is robust against deformation, and the description is matched against built-in models. On the basis of the correspondence of components between the object and the model, geometrical and statistical transformations are estimated, and the decision of recognition or rejection is based on the estimations. Structural descriptions and geometrical/statistical transforms are integrated in a systematic way. Experimental results are shown for off-line handwritten numeral recognition and on-line handwriting recognition.

Curve description based on directional features and quasi-convexity/concavity

Abstract Qualitative and global features are appropriate for describing the shape of a complex and deformed object rather than quantitative and local features. In particular, quantized directional features and quasi-convexity/concavity are powerful and flexible for describing the shape of handwritten characters. In this paper, we present a method for structural analysis and description of simple (open) arcs or closed curves based on 2 m -directional features ( m = 1,2,3,…) and quasi-convexity/concavity. We show some examples of shape description of handwritten characters and experimental results for selecting the optimal quantization of direction in handwritten character recognition.

A multiscale approach to restoring scanned color document images with show-through effects

This paper describes a new approach to restoring scanned color document images where the backside image shows through the paper sheet. A new framework is presented for correcting show-through components using digital image processing techniques. First, the foreground components on the front side are separated from the background and backside components through locally adaptive binarization for each color component and edge magnitude thresholding. Background colors are estimated locally through color thresholding to generate a restored image, and then corrected adaptively through multiscale analysis along with comparison of edge distributions between the original and the restored image. The proposed method does not require specific input devices or the backside to be input; it is able to correct unneeded image components through analysis of the front side image alone. Experimental results are given to verify effectiveness of the proposed method.

Structural feature extraction using multiple bases

Abstract The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mort (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Moris method, we present a method for structural analysis and description of simple arcs or closed curves based on 2m-directional features (m = 2, 3, 4, ...) and convex/concave features. On the other band, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.

Correcting show-through effects on document images by multiscale analysis

This paper describes a new approach to restoring color document images where the back image shows through the paper sheet. A new framework is presented for correcting show-through components using digital image processing techniques. First, the foreground components at the front are separated from the background and back components through locally adaptive binarization for each color component and edge magnitude thresholding. Background colors are estimated locally through color thresholding to generate a restored image, and then corrected adaptively through a multi-scale analysis along with comparison of edge distributions between the original and restored images. The proposed method is able to correct the redundant image components by analysing the front image alone. Experimental results are given to verify the effectiveness of the proposed method.