Tsunehiro Aibara

An Improvement on the Moore-Penrose Generalized Inverse Associative Memory

An improvement of the Moore-Penrose generalized inverse associative memory method is presented. It is known that for noisy input key vectors the associative memory is extremely sensitive (unstable) and association errors become unacceptably large, particularly as the number of vectors approaches the number of components per vector. Using singular value decomposition the association behavior of the associative memory is analyzed theoretically and its association error is shown to consist of two kinds of errors. One is due to the linear dependency of the key vectors (dependency error), and the other is due to the input additive noise (noise error). For noisy input key vectors the noise error is greatly increased when at least one small eigenvalue of the key space exists. It is found that the noise error can be changed to the dependency error by eliminating the corresponding eigenvalues. Therefore, if the eigenvalues are appropriately eliminated, stable association behavior can be realized and the association error reduced. In the proposed improvement method an elimination condition of the eigenvalues is given. The proposed method is greatly effective for noisy input key vectors.

Human face profile recognition by a P-Fourier descriptor

A method of recognizing human face profiles is proposed. Most conventional methods of recognizing human face profiles use fiducial marks, length of the line connecting the marks, angles between fiducial points, and other measures of profile outlines as the components of characteristic vectors. The P-Fourier descriptor developed by Uesaka can describe open curves fairly well, and it has many features of curves in the low-frequency range. We use the P-Fourier descriptor as a characteristic vector of a human face profile. We show that the P-Fourier coefficients in the low-frequency range are useful for human face profile recognition, that is, by using 31 P-Fourier coefficients, we obtained a correct recognition rate of 93.1 % for 130 subjects.

Least squares associative memory and a theoretical comparison of its performance

A novel associative memory model is proposed in which both the stable and optimal (minimum error) association are realized even when noisy input key vectors are given. The performance of the model is compared with that of typical associative memory models (Kohonens model and the stabilized model) theoretically and experimentally (by computer simulation). The proposed model takes full consideration of the input noise conditions (magnitude of noise). Since the relationship between the proposed model and Kohonens model is similar to that between the Wiener filter and the inverse filter, the proposed associative memory model is more practical than Kohonens model. >

Construction of a Distributed Associative Memory on the Basis of Bayes Discriminant Rule

The purpose of this correspondence is to propose a new construction method of distributed associative memory which operates with discrete-valued signals. In this method, memorized pairs of vectors (cue vectors and data vectors) are recorded in the form of a matrix W and a vector T. From an input vector X, the data vector is recalled by an operation u(XW + T) where X is a cue vector or a noisy cue vector. and u is a quantizing function. The methods of memorization and recall are similar to the Associatron; however, the proposed model can recall the data vectors optimally in Bayesian sense even when noisy cue vectors are given as the input vectors.

Enumeration of Ternary Threshold Functions of Three Variables

This note discusses the generation of ternary threshold functions of three variables. Merrills generation method to generate ternary threshold functions is modified. The number of ternary threshold functions of three variables is counted by a computer, the number is 85629. Tables of characterizing parameters of canonical ternary threshold functions of two and three variables are presented. A table-lookup method to realize ternary threshold functions is given. It is verified that the complete monotonicity (three-value extension of the complete monotonicity in two-valued logic) is a sufficient condition for a ternary three-variable switching function to be a ternary threshold function.

Human face recognition by P-type Fourier descriptor

This paper presents a method of recognizing human face profile. The conventional methods of recognizing human face profile use the computer-derived fiducial marks, lines, angles, and other measures of profile outline as the components of characteristic vector. We use P-type Fourier descriptor as a characteristic vector of human face profile. It is shown that four P-type Fourier coefficients in the low frequency range can identify 65 face profiles, with the accuracy of 100%.

Optimal association with partly missing key vectors

A new association scheme which can still recall appropriate data when some key elements are missing (blank) is presented. The traditional associative memory models are designed to deal with complete (memorized) keys, but in the real world, key elements are often missing due to error, equipment failure, observation difficulty, etc. The traditional models, in this case, can not have an optimal association except for special cases. When an incomplete key containing blanks is given, we wish to get the same data, as nearly as possible, as would be obtained with the complete key. In this paper, the optimal associative memory model which operates with partly missing keys is proposed. The model is constructed on the basis of the theory of the pseudoinverse of matrices. Even from the incomplete keys which contain a large percentage of blanks, the model recalls the appropriate data optimally under the MSE criterion. From the results of computer simulations, we can show that the model has the expected ability.

On optimal associative recall by an incomplete key

A new association scheme is proposed. The fundamental principle of the conventional associative memory models is to solve the matrix equation which is made by the complete (memorized) keys and responses. Therefore, when an incomplete key pattern which is a fraction of memorized key is given to the models as a key, these models can not have an optimal association except for a special case. Analyzing the property of the incomplete key pattern, in this paper, we propose a new association model which behaves optimally for an incomplete key pattern. From the result of the computer simulation, we understand that this model has the expected ability.

Automatic assessment of the appearance of seam pucker on textiles using Hough transform

Two methods of automated assessment of the appearance of seam pucker based on the Hough transform are proposed. We treat this problem as a pattern recognition problem. From the given standard photographs of suits, which are classified into five classes, we determine a template pattern for each class. These patterns are separated well in the feature space. Although there are several items to be assessed, we focus our attention on the seam of the back of suits. Using a few test samples we made an experiment on the assessment. The results suggest the possibility of practical use.

Curvedness of a line picture

Abstract The shape analysis of a binary picture is of great importance for pictorial pattern recognition. In this paper, we propose a useful geometric feature parameter called curvedness. Curvedness represents which lines are dominant in a binary picture, straight or curved. Our algorithm for measuring curvedness is based on the relationship between the distance to a boundary point on each black point along each quantized direction and the mean width of a binary picture. We investigate the fundamental property of curvedness. The experimental results for Japanese Hiragana and Kanji characters show the validity of curvedness.