This paper presents an autonomous navigation system for a mobile robot using randomly distributed passive RFID tags. In the case of randomly distributed RFID tags, it is difficult to provide the precise location of the robot especially in the area of sparse RFID tag distribution. This, combined with the wide turning radius of the robot, can cause the robot to enter a zigzag exploration path and miss the goal. In RFID-based navigation, the key is to reduce both the number of RFID tags and the localization error for practical use in a large space. To cope with these, we utilized the Read time, which measures the reading time of each RFID tag. With this, we could estimate accurately the localization and orientation without using any external sensors or increasing the RFID tags. The average estimation errors of 7.8 cm in localization and 11 degrees in orientation were achieved with 102 RFID tags in the area of 4.2 m by 6.2 m. Our proposed method is verified with the path trajectories produced during navigation compared with conventional approaches.

ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.

In this paper, we propose a new method to measure the 3D object shape without special purpose lighting based upon the Backprojection of Pixel Data.This method need not extract feature points such as edges from images at all and can measure not only the feature points but the whole object surface. It is simply done by project all pixel data back into the object space from each image. Actually, we first assign all pixel data of images into voxels in the object space, and evaluate the variance of assigned data for all voxels. This process is based on the idea that a point on the object surface gives the similar color information or gray level when it is observed from different view points. Then, two kinds of voting are executed as an enhancement process to eliminate the voxels containing the false points. We present experimental results under the circular constraint of camera movement and show the possibility of the proposed method.

This paper introduces the multiple signal classification (MUSIC) method that utilizes the transfer characteristics of microphones located at the same place, namely aggregated microphones. The conventional microphone array realizes a sound localization system according to the differences in the arrival time, phase shift, and the level of the sound wave among each microphone. Therefore, it is difficult to miniaturize the microphone array. The objective of our research is to build a reliable miniaturized sound localization system using aggregated microphones. In this paper, we describe a sound system with N microphones. We then show that the microphone array system and the proposed aggregated microphone system can be described in the same framework. We apply the multiple signal classification to the method that utilizes the transfer characteristics of the microphones placed at a same location and compare the proposed method with the microphone array. In the proposed method, all microphones are placed at the same place. Hence, it is easy to miniaturize the system. This feature is considered to be useful for practical applications. The experimental results obtained in an ordinary room are shown to verify the validity of the measurement.

In vector analysis, it is important to classify three flow primitives as translation, rotation and divergence. These three primitives can be detected utilizing line integral and surface integral according to the knowledge of vector analysis. In this paper, we introduce a method for extracting these three primitives utilizing edges in an image based on vector analysis, namely edge field analysis. The edge has the information of inclination. However, the edge has no information of the direction unlike vector. Hence, line integral and surface integral can not be directly applied to detect these three primitives utilizing edges. We firstly formulate the problem and describe the algorithm for detecting the three primitives in vector analysis. We then propose an algorithm for estimating three primitives regarding edge image as pseudo-vector field. For illustration, we apply edge field analysis to quasi-motion extraction and feature extraction. We also show the experimental results in terms of estimating the center of the flowers, the cell body of neuron, the eye of the storm, the center of the explosion and so on.

A hierarchical relaxation method is presented for detecting local features in moving images. The relaxation processes are performed on the temporal-spatial pyramid, which is a multi-resolution data structure for the moving images. The accurate and high speed edge detection can be obtained by using infomation in the neighboring frames as well as the processed results in the higher layers of the pyramid.

Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristics of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.