Hiroyuki Ogata

Saffron pistils are normally harvested by hand. As the amount of pistils obtained from one flower is very small, harvesting them a certain amount requires a hard task. In this paper, we discuss a system to automate harvesting saffron pistil using robot equipped with image processing unit. We introduce a mechanism and a task movement of the robot, and examine technique to detect cutting point and gripping point which are necessary for harvesting by image processing, and proposes a process to harvest based on it.

We propose an algorithm to detect characters in images. The proposed algorithm consists of three phases: training, candidate area detection, and character detection. In training phase, a classifier to detect the character is generated from several haar-like feature-based weak classifiers by using Adaboost. In candidate area detection phase, one haar-like feature is used to define the area to search characters. And in character detection phase, the generated Adaboost-based classifier is used to detect the character in the defined area. We performed preliminary experiments to evaluate the proposed algorithm using 13 images. The experimental results show that some character on homogeneous background can be detected by this algorithm.

In this paper, we report an implementation of self-localization system for mobile robots running in outdoor environments where GPS data is sometimes not accurate enough. Our system is equipped with GPS, encoder and magnetic sensor. Data obtained by these sensors are integrated using Kalman filter. GPS observation is omitted when the robot is running through particular areas. These areas are determined based on standard deviation of the data observed in advance.

In late years tests called the CBT where I used a computer for by information technology and the network technology that developed spread. However, the knowledge examinations such as TOEFL or the Kanji Aptitude Test are often found, and these do not advance in the skill examination. Therefore, toward the CBT of the skill examination, I analyze the skill as the part. I set a problem for forward upward circling and, using an electromyograph, an acceleration sensor, the data of the camera, wrestle with the analysis of a necessary skill.

We focus on a biometric person authentication method using features of pen holding style. The manner of holding pen can be distinctive among persons and be useful modality for person authentication, because the manner is affected by both the physical features and habitual behavior. In order to evaluate the efficiency, we extract several features from the pen-holding image, and fuse them for verification. In this paper, realAdaBoost algorithm is used for the fusion, and user-dependent threshold is applied for a decision making. The developed algorithm is evaluated using the database collected dorm 30 persons. The algorithm achieved an EER of 4.0% against the impersonation attacks.

The manner of holding a pen is distinctive among people. Therefore, pen holding style is useful for person authentication. In this paper, we propose a biometric person authentication method using features extracted from images of pen holding style. Images of the pen holding style are captured by a camera, and several features are extracted from the captured images. These features are compared with a reference dataset to calculate dissimilarity scores, and these scores are combined for verification using a three-layer perceptron. Preliminary experiments were performed by using a private database. The proposed system yielded an equal error rate (EER) of 2.6% against an impersonate attack..

Face detection algorithm used in Adaboost and Haar-like features were detected using the kana-kanji area detection. Create a set of images for learning. It was made by changing the number of sheets of the correct answer image and non-correct answer image. Correct answer images has Grayscale images and binary font images. Font used MS Gothic and MS Mincho. Non-correct answer images using gray-scale images of characters not included. The detection algorithm was verified by using 15 images for the verification. Verification images used 11 images including characters, two images including correct answer images, and two images not including characters. Adaboost and Haar-like feature using kana-kanji detection algorithms were validated.

Saffron pistils have been used, for example, as spice, dye and medicine from old days. But saffron pistils are considerably expensive, and are regarded to be luxury food. Saffron pistil is normally harvested by hand now. However, as the amount of pistils of one flower is very small, task of harvesting them is very hard. Therefore, in this paper, we discuss a method to automate harvesting saffron pistil using robot. We examine technique to detect cutting point and gripping point which are necessary for harvesting by online image processing. We verified the feasibility of our method through experiments.

All stigmas of saffrons which are used as a spice or a dye are harvested by hand. However, it is heavy work to gather an enough amount. The purpose of this paper is to examine whether harvesting saffron's stigmas automatically is possible by using image processing. As a process of harvesting saffron stigmas automatically, we conceieved an idea of cutting the root of flower and harvesting stigma which is hanging by turning it down. Features to harvest stigmas are detected by using color information. The feasibility of this method was shown by the experiment.

Recently, cleaning robots for domestic use are developed. However, present algorithm cannot always sweep the workspace uniformly, and this causes the inefficiency of the algorithm. The best sweeping algorithm may depend on the shape of the room. To select the best algorithm, we examined a method to acquire the shape of a room in the last report. In this report, we adopt an actual robot model so that the robot can distinguish rooms in the real world.

Recently, cleaning robots for domestic use have been actively developed. In our past studies, we introduced a new sweeping algorithm for cleaning robots, and verified its efficiency on a simulator. In this report, we mount the algorithm on a real robot, to compare the performance with the simulation result.

Nowadays, home-use robots that clean floors automatically are developed. However, the efficiency of these robots is not so good. That is because the best way of cleaning room depends on the room's shape. If the robot knows which room it is cleaning now, it will be possible to make a robot cleaning the room more efficiently. Therefore we propose a method which the robot goes around the room with hug motion, to detect the outline of the room.

We propose a technique that automatically estimates a sports skill of examinees. In our study, we are discussing the technique for golf putting. However, we have estimated the skill only from apart of the examinee's motion data. It became the problem that the arbitrariness characteristics occur when choosing sensor. In this study, we introduce ensemble leaning to treat the whole motion data with Recurrent Neural Networks as temporary estimate sports skill and unify these result. The experimental result suggested the effectiveness of our method.

The form of the putt swing is different among person. This suggests that an advice that can effectively improve a person's skill may differ between them. This paper describes a method to classify putt swing form aiming to develop an advisory system. We obtained motion data of subjects using a motion capture system. The putt swing form was appropriately classified using cluster analysis.

In order to reconstruct the entire 3-Dstructure of an object from the video sequence acquired by the camera attached to a mobile robot, one view direction is not sufficient; it is necessary to select the optimal direction at each spot on the robot's path, reconstruct local structures, and integrate the multiple local structures.this paper studies a Monte Cairo method and Bayes Estimationbased method for the local 3-D reconstruction. In addition, we study the volume of information from a view point(the initial frame of the view sequence) is useful for evaluating the robot's moving direction

In order to reconstruct the entire 3-Dstructure of an object from the video sequence acquired by the camera attached to a mobile robot, one view direction is not sufficient; it is necessary to select the optimal direction at each spot on the robot's path, reconstruct local structures and integrate the multiple local structures.this paper studies a Monte Calro method and Bayes Estimationbased method for the local 3-D reconstruction. In addition, we study the volume of information from a view point (the initial frame of the view sequence) is useful for evaluating the robot's moving direction

In order to reconstruct the entire 3-Dstructure of an object from the video sequence acquired by the camera attached to a mobile robot, one view direction is not sufficient; it is necessary to select the optimal direction at each spot on the robot's path, reconstruct local structures and integrate the multiple local structures.this paper studies a Monte Calro method and Bayes Estimationbased method for the local 3-D reconstruction. In addition, we study the volume of information from a view point (the initial frame of the view sequence) is useful for evaluating the robot's moving direction

Recently, computer-based testing that automates examination using computer is attracting a lot of attention due to the development of information technology and of the spread of computer network. This paper describes a method to automate a test in which examinees actually perform the task to solve given problems, taking putt swing for example. Our testing system assesses the performance of examinees from motion data. We got motion data using a motion capture system. In this paper, we propose a method using recurrent neural network to estimate the expertise of examinees.

Recently, it is noticed that the Computer-Based Testing is getting a lot of attention as personal computers become popular and the internet society is growing. At present, CBT is normally used to measure examinees' knowledge or intelligence. But, it is not considered enough to measure examinees' skill for now. In this paper, we propose a method to measure examinees' skill taking putt swing for example. The task is easy to analyze and compare because the initial condition of the task can be fixed easily. We got motion data using a motion capture system. And we analyzed the motion data.

An irregular cleaning is caused easily because the home cleaning robot reflects information acquired while directly in operation. It is thought that the sweep rate prediction is effective to an irregularity of cleaning that is one of the problems of the home cleaning robot. However, whether it predictions is not verified in the environment that arranges furniture. In this research, it is verified whether the prediction of sweep rate is possible in the environment that arranges furniture, and the influence of furniture in the sweep rate prediction of the cleaning robot is described.

Recently, robot cleaner used at home is developing popular. But performance of these robots on sale at present isn't enough, because sweeping by these robots make problems such as irregularities of a sweep. We think that an inefficiency of a sweep in narrow environment influences it as those causes. In the report, we analyzed sweep work in narrow environment and examined influence to a sweep.

It is important for robots to recognize unregistered objects when working in general environments like home or office. In such a case, the information of shape and material may play an important role to recognize them. In this research, a sequential method of inferring object's surface material using brightness data from a series of images is examined.

The probabilistic road map method in one of the good methods to generate trajectories of manipulators with multiple degrees of freedom. However, there is a problem that the subgoal cannot be put well, hi this research to solve such a problem, the Markov chain Monte Carlo method is applied to make the probabilistic road map method more efficient, and the effectiveness of the proposed method is confirmed by simulation.

Recently, the automation of the test using computer is emerging because of the development of information technology. This paper describes the method to automate tests in which examinees actually perform the task to solve given problems. Our testing system recognizes performance of the examinee with sensors such as cameras. The uncertainty of recognition is a non-negligible problem, here.hi this paper, we discuss a method to estimate the expertise of examinees considering such an uncertainty. The validity of this method is verified by using simulation.

Nowadays, many mobile robots are developed to automate cleaning task. Some of them aim at using at home. Most of the home cleaning robots are using reactive type algorithm. However, when a robot adopting reactive type algorithm gets into a narrow area surrounded by obstacles, the robot cannot readily get out of there. This causes the inefficiency of the cleaning task. We propose efficient hybrid algorithm to solve this problem.