Nobuyuki Otsu

Real-world applications of analog and digital evolvable hardware

In contrast to conventional hardware where the structure is irreversibly fixed in the design process, evolvable hardware (EHW) is designed to adapt to changes in task requirements or changes in the environment, through its ability to reconfigure its own hardware structure dynamically and autonomously. This capacity for adaptation, achieved by employing efficient search algorithms based on the metaphor of evolution, has great potential for the development of innovative industrial applications. This paper introduces EHW chips and six applications currently being developed as part of MITIs Real-World Computing Project; an analog EHW chip for cellular phones, a clock-timing architecture for Giga hertz systems, a neural network EHW chip capable of autonomous reconfiguration, a data compression EHW chip for electrophotographic printers, and a gate-level EHW chip for use in prosthetic hands and robot navigation.

Maximum likelihood thresholding based on population mixture models

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 the conditional distribution in the population mixture model under the assumption of normal distributions with a common variance. It is also shown that Kittler and Illingworths thresholding, which minimizes a criterion related to the average classification error rate assuming normal distribution with different variances, is equivalent to the maximization of the likelihood of the joint distribution in the population mixture model. A multi-thresholding algorithm based on Dynamic Programming is also presented.

Face recognition system using local autocorrelations and multiscale integration

In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons, organized in training and test sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. We focus on the difficult problem of recognizing a large number of known human faces while rejecting other, unknown faces which lie quite close in pattern space. A multiresolution system achieves a recognition rate of 95%, while falsely accepting only 1.5% of unknown faces. It operates at a speed of about one face per second. Without rejection of unknown faces, we obtain a peak recognition rate of 99.9%. The good performance indicates that local autocorrelation coefficients have a surprisingly high information content.

A sketch retrieval method for full color image database-query by visual example

Gives a basic idea and its fundamental algorithms of the visual interface for image database systems. The QVE (Query by Visual Example) accepts a sketch roughly drawn by a user to retrieve the original image and the similar images. The system evaluates the similarity between the rough sketch, i.e. a visual example, and each of the image data in the database automatically. The QVE interface is implemented and examined on an experimental electronic art gallery called ART MUSEUM. This paper also gives some experimental results and a current evaluation. The algorithms are quite effective for content based image retrieval.<<ETX>>

A face recognition method using higher order local autocorrelation and multivariate analysis

Proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions: first, 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 basis of multivariate analysis methods so as to provide new effective features for face recognition in learning from examples.<<ETX>>

Image Feature Extraction Using Gradient Local Auto-Correlations

In this paper, we propose a method for extracting image features which utilizes 2nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT.

Complex autoregressive model for shape recognition

A complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane is presented. A fast algorithm to calculate complex autoregressive coefficients and complex PARCOR coefficients of the model is also shown. The coefficients are invariant to rotation around the origin and to choice of the starting point in tracing a boundary. It is possible to make them invariant to scale and translation. Experimental results that the complicated shapes like nonconvex boundaries can be recognized in high accuracy, even in the low-order model. It is seen that the complex PARCOR coefficients tend to provide more accurate classification than the complex AR coefficients. >

METHODS FOR QUANTIFYING THE CAUSAL STRUCTURE OF BIVARIATE TIME SERIES

In the study of complex systems one of the major concerns is the detection and characterization of causal interdependencies and couplings between different subsystems. The nature of such dependencies is typically not only nonlinear but also asymmetric and thus makes the use of symmetric and linear methods ineffective. Moreover, signals sampled from real world systems are noisy and short, posing additional constraints on the estimation of the underlying couplings. In this article, we compare a set of six recently introduced methods for quantifying the causal structure of bivariate time series extracted from systems with complex dynamical behavior. We discuss the usefulness of the methods for detecting asymmetric couplings and directional flow of information in the context of uni- and bidirectionally coupled deterministic chaotic systems.

A method of block truncation coding for color image compression

A basic color block truncation coding (CBTC) algorithm for color image compression is described. A modification of the algorithm that reduces truncation errors is also described. The block statistics related to CBTC methods are investigated. Some experimental results are given for a 256-*256-pixel color image with 24 b/pixel. >

Three-way auto-correlation approach to motion recognition

This paper presents a feature extraction method for three-way data: the cubic higher-order local auto-correlation (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-, y- and t-axes. The CHLAC method is based on three-way auto-correlations of pixels in motion images. It effectively extracts spatio-temporal local geometric features characterizing the motion, such as gradients (velocities) and curvatures (accelerations). It has also several advantages for motion recognition. Firstly, neither a priori knowledge nor heuristics about the objects in question is required. Secondly, it is shift-invariant and thus segmentation-free. Thirdly, its computational cost is less than that of traditional methods, which makes it more suitable for real time processing. The experimental results on large datasets for gesture and gait recognition showed the effectiveness of the CHLAC method.