Nobuyuki Otsu

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A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zerothand the first-order cumulative moments of the(More)
We propose a new scheme for practical vision systems which are simple in structure, directly and adaptively trainable for various purposes. The feature extraction consists of two stages: At first, general and primitive features which are shift-invariant and additive are extracted over the retinal image plane. Then those features are linearly combined on the(More)
-Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of(More)
This paper 1 proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions. At rst, higher order local autocorrelation features which are shift-invariant and additive are extracted from an input image. Then those features are linearly combined on the(More)
In virtual reality and multimedia applications, 3D polygonal models are increasing in number. Similarity retrieval is an important task in 3D polygonal model databases. We present rotation invariant shape descriptors for similarity retrieval. Our feature descriptor grouping technique overcomes the efficiency problem of query processing in high-dimensional(More)
We propose a new method – Cubic Higher-order Local Auto-Correlation (CHLAC) – to address three-way data analysis. This method is a natural extension of Higherorder Local Auto-Correlation (HLAC) [6], which deals only with two-way data. Both methods use “correlation” to summarize relative positions or motions within a local data region, and these can be(More)
This paper presents a feature extraction method for three-way data: the cubic higher-order local autocorrelation (CHLAC) method. This method is particularly suitable for analysis of motion-image sequences. Motion-image sequences can be regarded as three-way data consisting of x-, yand t-axes. The CHLAC method is based on three-way auto-correlations of(More)