Image preprocessing is an important process during pattern recognition which increases the recognition performance. Linear convolution filtering is a primary preprocessing method used to enhance particular local patterns of the image which are essential for recognizing the images. However, because of the vast search space of the preprocessing filter, almost no earlier studies have tackled the problem of identifying an optimal preprocessing filter that yields effective features for input images. This paper proposes a novel design method for the optimal preprocessing filter corresponding to a given task. Our method calculates local image bases of the training dataset and represents the optimal filter as a linear combination of these local image bases with the optimized coefficients to maximize the expected generalization performance. Thereby, the optimization problem of the preprocessing filter is converted to a lower-dimensional optimization problem. Our proposed method combined with a higher-order local auto-correlation (HLAC) feature extraction exhibited the best performance both in the anomaly detection task with the conventional pattern recognition algorithm and in the classification task using the deep convolutional neural network compared with typical preprocessing filters.

In this paper, we propose a novel method of label propagation for one-class learning. For binary (positive/negative) classification, the proposed method simultaneously measures the pair-wise similarity between samples and the negativity at every sample based on a cone-based model of local neighborhoods. Relying only on positive labeled samples as in one-class learning, the method estimates the labels of unlabeled samples via label propagation using the similarities and the negativities in the framework of semi-supervised learning. In the proposed method, unlike standard label propagation methods, it is not necessary to prepare negative labeled samples since the measured negativity works as an alternative of such labeling negative samples. In experiments on target detection in still images and motion images, the proposed method exhibits the favorable performances compared to the other methods.

In this paper, we propose a method to extract features from three-dimensional acceleration signals. The proposed method is based on the (auto-)correlation matrix of Fourier transform features, naturally containing the correlations between the frequencies as well as the ordinary power spectrum for each frequency. The proposed features are inherently invariant to both rotational variations and temporal shift (delay), whereas the other methods employ ad hoc preprocessing to increase robustness to those variations. Thereby, we can favorably apply the proposed method to analyze 3-D acceleration signals regardless of the orientations of the accelerometer. In the experiment on gait identification using an accelerometer embedded in a cellular phone, the proposed method outperformed the other methods.

In biology, peptide ion detection from mass spectra is important for identifying proteins. Many methods have been proposed for detecting peptide ion peaks, some of which use wavelet transform. In these methods, however, the co-occurrence pattern of peptide ions and those isotopes is not directly considered. In this paper, we propose a novel method for detecting peptide ion peaks from a mass spectrum by using a weighted auto-correlation. The weight functions derived from Maxwell-Boltzmann distribution and the sine function are introduced to the proposed auto correlations to effectively represent the peptide ion co-occurrence patterns. The multi-scaled auto-correlation features extracted with those weight functions are compressed by using principal component analysis. Experiments on raw mass spectra show that the proposed method achieves the favorable performances and is capable of automatically detecting peptide ion peaks.

In the cancer detection from stained biopsy images, it is important to extract histologically discriminative characteristics. For this purpose, we propose a novel method to extract statistical and morphological features. At the first stage, we estimate cell component memberships at each pixel by applying an expectation maximization (EM) algorithm to the color information. Next we calculate the local co-occurrence of the memberships as image features. And then, linear discriminant analysis (LDA) is applied to those features for final decision of whether cancer or not, with enhancing the discrimination. In the experiments on real biopsy images of cancers, the resulting detection accuracy is superior to the other methods.

We propose cone-restricted kernel subspace methods for pattern classification. A cone is mathematically defined in a manner similar to a linear subspace with a nonnegativity constraint. Since the angles between vectors (i.e., inner products) are fundamental to the cone, kernel tricks can be directly applied. The proposed methods approximate the distribution of sample patterns by using the cone in kernel feature space via kernel tricks, and the classification is more accurate than that of the kernel subspace method. Due to the nonlinearity of kernel functions, even a single cone in the kernel feature space can can cope with multi-modal distributions in the original input space. In the experimental results on person detection and motion detection, the proposed methods exhibit the favorable performances.

The goal of image segmentation is to partition an image into regions that are internally homogeneous and heterogeneous with respect to neighbouring regions. Recently, a link shifting based pyramidal segmentation method was proposed to resolve existing problems with elongated regions. In this paper, we propose further improvements by replacing pixel intensities at the base level with pixel level higher order local auto-correlation (HLAC) feature vectors over greyscale, RGB, and CIV channels. Thereby, rich texture-like information is incorporated into segmentation. We propose a normalized distance formula between HLAC vectors, where each component contributes with physically same unit. The new algorithms were tested on a set of Landsat images over forested areas, and compared with a non-HLAC variant and several other existing segmentation algorithms. A significant improvement in segmentation quality was achieved compared to non-HLAC variants, and it also gave better results than other existing algorithms on most examples.

We propose a method of clustering sample vectors on a hypersphere. Sample vectors are normalized in many cases, especially when applying kernel functions, and thus lie on a (unit) hypersphere. Considering the constraint of the hypersphere, the proposed method utilizes the von Mises-Fisher distribution in the framework of mean shift. It is also extended to the kernel-based clustering method via kernel tricks to cope with complex distributions. The algorithms of the proposed methods are based on simple matrix calculations. In the experiments, including a practical motion clustering task, the proposed methods produce favorable clustering results.

We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects' components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words to descriptors. For extracting descriptors, we employ two types of features hierarchically: narrow (local) descriptors, like SIFT, and broad descriptors based on co-occurrence features. The proposed method thus captures the co-occurrences of both small and large components. We conduct an experiment on image classification by applying the method to the Caltech 101 dataset and show the favorable performance of the proposed method.

In this paper, we propose a method for extracting color image features, called color index local auto-correlations. Pixel color is quantized and described sparsely in a manner similar to the color indexing of color histograms. In addition, by utilizing spatial auto-correlations of the color indexes, the characteristics of color texture can be extracted more effectively than ordinary histogram-based methods. The proposed method has variants in terms of the color space and basic colors used for indexing colors. These various settings are comprehensively compared in the experiments of image retrieval and image classification, and are shown to exhibit favorable results compared to the other conventional methods.

Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors supporting the classifier increases. In this paper, we propose a method for reducing the computational cost of classification by kernel-based methods while retaining the high performance. By using linear algebra of a kernel Gram matrix of the support vectors (SVs) at low computational cost, the method efficiently prunes the redundant SVs which are unnecessary for constructing the classifier. The pruning is based on the evaluation of the performance of the classifier formed by the reduced SVs in SVM. In the experiment of classification using SVM for various datasets, the feasibility of the evaluation criterion and the effectiveness of the proposed method are demonstrated.

Linear discriminant analysis (LDA) is one of the well known methods to extract the best features for the multi-class discrimination. Otsu derived the optimal nonlinear discriminant analysis (ONDA) by assuming the underlying probabilities and showed that the ONDA was closely related to Bayesian decision theory (the posterior probabilities). Also Otsu pointed out that LDA could be regarded as a linear approximation of the ONDA through the linear approximations of the Bayesian posterior probabilities. Based on this theory, we propose a novel nonlinear discriminant analysis named logistic discriminant analysis (LgDA) in which the posterior probabilities are estimated by multi-nominal logistic regression (MLR). The experimental results are shown by comparing the discriminant spaces constructed by LgDA and LDA for the standard repository datasets.

We propose methods for segmenting a motion sequence into motion primitives, taking into account temporal constraints (continuity along the time axis). In the proposed methods, dynamic programming (DP) is used on a motion feature sequence to allow for the effects of these constraints on the results of the segmentation. The methods do not require such a running window along the time axis, as is typical for the usual methods, and thus they can be applied to the segmentation of transient motions. The results of comparative experiments using several motion features and segmentation methods on weightlifting motion data demonstrate the effectiveness of the proposed methods.

In pattern recognition, feature vectors are occasionally subject to non-negative constraints. This characteristic can be expressed by a cone in feature vector space. In this paper, we propose cone-restricted subspace methods. The proposed methods admit the scaling and additivity of vectors as well as ordinary subspace methods; in addition, vectors can be strictly classified at the boundary of the cone. Some experimental results for face and person detection demonstrate the effectiveness of the proposed methods.

Image matting and segmentation, which are used to extract a foreground object from the background, are primary techniques for digital image and video editing. In digital matting, the transparency of the foreground object is considered, while segmentation performs a rigid extraction of the object. Recently, several algorithms for matting and segmentation problems have been proposed and have provided high-quality results. In this paper, we propose a unified formulation for image matting in the framework of the method of quantification IV based on a review of the previous studies from this framework. Our method also utilizes discriminative information provided by a user (a few strokes drawn by the user). The experimental results show a favorable matting performance.

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 shape descriptor spaces. Although high-dimensional feature descriptors are reduced by our techniques, they maintain high recall and precision.

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 MITI's 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.

In order for our office conversant robot to take robust actions according to events rising from different kind of sensors, a layered process network architecture based on an event-driven control model is presented. In this architecture, high level modules are freed from time consuming checking of sensors, and are able to follow multiple scenario-driven consistent behaviors, allowing interruption from human and environment. Programs are written as series of event-action pairs, which are also effective to keep reactiveness and modularity high.

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.

We describe and evaluate information fusion by fuzzy integration in a robust, high performance face recognition system. The system uses fuzzy integrals to combine classifiers operating at different image resolutions. Recognition is carried out by distance classification of transformed vectors of local autocorrelation coefficients. The transformation is determined by linear discriminant analysis. A large database of 11,600 images of 116 persons is used to determine the system performance. After being trained to recognize 60 persons, it is tested on images of all persons in the database. Both training and test stages use 50 images of each person. Under two different training schemes, it achieves peak recognition rates of 98.4% and 97.9%, respectively, accepting only 1.6% and 2.4% of the unknown faces. This exceeds the performance of any of the individual classifiers by at least 10%. Moreover, it exceeds earlier results obtained by multiple resolution averaging on the same database by at least 1.0%.

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.>

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.>

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.>

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.>

An architecture of a four-layer (two hidden layers) neural network is proposed in order to approximate nonlinear discriminant analysis. The architecture is based on a previously observed relationship between multilayer neural networks and back-propagation (least mean squared error) learning and nonlinear data analysis methods. The effectiveness of the architecture has been verified experimentally. It is shown that the networks have a much stronger capability of class separation than the usual linear discriminant analysis method