Takio Kurita

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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)
Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessing step for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data. This is a characteristic(More)
The problem of fitting a straight line to a planar set of points<lb>is &considered. A parameter space computational approach capable of<lb>fitting one or more lines to a set of points is presented. The suggested<lb>algorithm handles errors in both coordinates of the data points, even<lb>when the error variances vary between coordinates and among(More)
This paper describes a data compression system using Evolvable Hardware (EHW) for digital color electrophotographic (EP) printers. EP printing is an important technology within digital printing, which is currently having a significant impact on the printing and publishing industry. Although, it requires data-compression to reduce the cost for transferring(More)
of precise motion, dexterous manipulation, and so forth—capabilities a factory robot requires—the increasingly popular interactive robot must be able to learn from and adapt to its dynamic environment and communicate with people. We have built an office robot, Jijo-2, as a testbed for autonomous intelligent systems that interact and learn in the real world(More)
The potential of brain-computer interfaces (BCI) in serving a useful purpose, e.g., supporting communication in paralyzed patients, hinges on the quality of the classification of the brain waves. This paper proposes a novel method to construct a classifier with improved generalization performance. A feature selection method is applied to features calculated(More)
Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem, we propose RANSAC-SVM method, which trains a number of small SVMs for randomly selected subsets of training set, while tuning their parameters to fit SVMs to whole training set.(More)