This paper describes a learning assistant system using motion capture data and annotation to teach “Naginata-jutsu” (a skill to practice Japanese halberd) performance. There are some video annotation tools such as YouTube. However these video based tools have only single angle of view. Our approach that uses motion-captured data allows us to view any angle. A lecturer can write annotations related to parts of body. We have made a comparison of effectiveness between the annotation tool of YouTube and the proposed system. The experimental result showed that our system triggered more annotations than the annotation tool of YouTube.
When learning complicated movements by ourselves, we encounter such problems as a self-rightness. The self-rightness
results in a lack of detail and objectivity, and it may cause to miss essences and even twist the essences. Thus,
we sometimes fall into the habits of doing inappropriate motions. To solve these problems or to alleviate the problems as
could as possible, we have been developed mechanical man-machine human interfaces to support us learning such
motions as cultural gestures and sports form. One of the promising interfaces is a wearable exoskeleton mechanical
system. As of the first try, we have made a prototype of a 2-link 1-DOF rotational elbow joint interface that is applied for
teaching extension-flexion operations with forearms and have found its potential abilities for teaching the initiating and
continuing flection motion of the elbow.
KEYWORDS: Magnetism, Control systems, Adaptive control, Robots, Feedback signals, Space robots, Control systems design, Manufacturing, Air contamination, Machine vision
The objective of this paper is to establish a technique that levitates and conveys a hand, a kind of micro-robot, by
applying magnetic forces: the hand is assumed to have a function of holding and detaching the objects. The equipment to
be used in our experiments consists of four pole-pieces of electromagnets, and is expected to work as a 4DOF drive unit
within some restricted range of 3D space: the three DOF are corresponding to 3D positional control and the remaining
one DOF, rotational oscillation damping control. Having used the same equipment, Khamesee et al. had manipulated the
impressed voltages on the four electric magnetics by a PID controller by the use of the feedback signal of the hand's 3D
position, the controlled variable. However, in this system, there were some problems remaining: in the horizontal
direction, when translating the hand out of restricted region, positional control performance was suddenly degraded. The
authors propose a method to apply an adaptive control to the horizontal directional control. It is expected that the
technique to be presented in this paper contributes not only to the improvement of the response characteristic but also to
widening the applicable range in the horizontal directional control.
The authors developed a fingertip guiding system which consists of a haptic manipulator (PHANTOM Omni) to help a
blind person to create mental images of a pre-planned trajectory. When using this system, the person will grasp a stylus
(a pen-shaped stick) of the haptic manipulator by his/her fingertip. The system is equipped with dual mode fingertip
guiding function which allows switching between modes (a passive or an active mode) in recognizing the image
provided. In passive mode, the system will guide and pull a person's hand, and it will also provide force feedback to
his/her fingertip, while constraining the fingertips motion along the trajectory. On the other hand, in active mode, the
system will guide and provide force feedback at the fingertips of the subject but allows the persons to freely move his/her
fingertip on the trajectory. The main objective of the research is to create a hybrid exploration system which consists of
active and passive modes. In this paper, we targeted our scope on the active mode system. We examined the
effectiveness in understanding the trajectory by moving his/her fingertips with haptic manipulator either on a flat surface
or in a free aerial space (without any surface provided).It was confirmed that the active system with a flat surface
expedites the understanding of the layout's trajectory.
By using synthetic aperture methods for Ground Penetrating Radar (GPR), subsurface structural images are
reconstructed from spatial and temporal two-dimensional images that are known as B-Scope images. The spatial and
temporal coordinates in B-Scope images correspond to the horizontal position on the surface and the propagation time of
the reflected waveforms from the buried object. The synthetic aperture methods visualize buried objects by deconvolving
the B-Scope image with the transfer function of the reflected waveforms. Based on the characteristic that the transfer
function continuously changes with depth, the authors proposed an algorithm for suppressing the ill effect of the change
of the transfer function to enhance the reconstructed images. When applying the deconvolution of the B-Scope images
and the transfer function, the B-Scope images are divided into several sectors in the depth direction based on the amount
of the change of the transfer function, which is defined for respective sectors. Experimental results demonstrated the
effectiveness of the proposed algorithm.
Pattern matching between input and template images, which is carried out using Sum of Squared Differences (SSD), a
similarity value, has been widely used in various computer vision applications such as stereo measurements and superresolution
image syntheses. The crucial process in the pattern matching problem is estimating the translation of the input
image to match both images; a technique exists for improving the accuracy of the translation estimation at the subpixel
level. In addition, subpixel estimation accuracy is improved by synthetic template images that are assumed to represent
subpixel translated images using linear interpolation. However, calculation cost increases because the technique
necessitates additional SSD calculations for the synthetic template images. To eliminate the need for additional SSD
calculations, we found that we can obtain additional SSD values for the synthetic subpixel translated images by
calculating just the SSD values for the original template images: we never need additional SSD calculations. Moreover,
based on this knowledge, we proposed a novel algorithm for speeding up the estimation error cancellation (EEC) method
that was developed for estimating subpixel displacements in pattern matching. Experimental results demonstrated the
advantages of the proposed algorithm.
In computer vision, many algorithms have been developed for image registration based on image pattern matching.
However, there might be no universal method for all applications because of their advantages and disadvantages.
Therefore, we have to select the best method suited for each task. A representative sub-pixel registration method uses
one dimensional parabola fitting over the similarity measurements at three positions. The parabola fitting method could
be applied to two dimensional, assuming that horizontal and vertical displacements are independent. Although this
method has been widely used because of their simplicity and practical usability, large errors are involved. To avoid
these errors depending on the spatial structure of image pattern, "two-dimensional simultaneous sub-pixel estimation"
was proposed. However, it needs conditional branching control procedures such as scan field expansion and exception.
The conditional branching control procedures make estimation instable and disturb the speed of processing. Therefore,
the authors employ a paraboloid fitting: by using the least square method, a paraboloid is fitted with the image similarity
values at nine points and the best matching point is obtained with sub-pixel order. It is robust against the image pattern
and enables speed-up, but it still has error margin. The authors analyzed the error characteristics of the sub-pixel
estimation using the paraboloid fitting. The error can be characterized by "a bias; a systematic error" and "dispersion; a
random error." It was found that the magnitude of each error was different according to the sub-pixel values of the best
matching positions. In this paper, based on the analysis, the authors proposed a novel accurate algorithm for 2D subpixel
matching. The method does not need any iteration processes and any exception processes on runtime. Therefore,
it is easy to implement the method on software and hardware. Experimental results demonstrated the advantage of the
proposed algorithm.
KEYWORDS: Video, Cameras, Image processing, Computing systems, Digital cameras, Imaging systems, Video processing, Image quality, Digital video recorders, Video surveillance
Information processing and communication technology are progressing quickly, and are prevailing throughout various technological fields. Therefore, the development of such technology should respond to the needs for improvement of quality in the e-learning education system. The authors propose a new video-image compression processing system that ingeniously employs the features of the lecturing scene. While dynamic lecturing scene is shot by a digital video camera, screen images are electronically stored by a PC screen image capturing software in relatively long period at a practical class. Then, a lecturer and a lecture stick are extracted from the digital video images by pattern recognition techniques, and the extracted images are superimposed on the appropriate PC screen images by off-line processing. Thus, we have succeeded to create a high-quality and small-capacity (HQ/SC) video-on-demand educational content featuring the advantages: the high quality of image sharpness, the small electronic file capacity, and the realistic lecturer motion.
KEYWORDS: Video, Image processing, Digital imaging, Image compression, Digital image processing, Video compression, Cameras, Video surveillance, Video processing, Digital cameras
Information processing and communication technology are progressing quickly, and are prevailing throughout various technological fields. Therefore, the development of such technology should respond to the needs for improvement of quality in the e-learning education system. The authors propose a new video-image compression processing system that ingeniously employs the features of the lecturing scene: recognizing the a lecturer and a lecture stick by pattern recognition techniques, the video-image compression processing system deletes the figure of a lecturer of low importance and displays only the end point of a lecture stick. It enables us to create the highly compressed lecture video files, which are suitable for the Internet distribution. We compare this technique with the other simple methods such as the lower frame-rate video files, and the ordinary MPEG files. The experimental result shows that the proposed compression processing system is much more effective than the others.
The camera calibration for the intrinsic parameters such as the principal point and the principal distance is one of the most important techniques for the 3-D measurement applications based on the cameras' 2D images: the principal point is the intersection of optical axis of camera and image plane, and the principal distance is the distance between the center of lens and principal point. Though the techniques of camera parameter calibration have been intensively investigated by many researchers, the calibration errors were just examined through limited experiments and simulations and no more. Taking up the two-fiducial-plane camera calibration technique, this paper examined the calibration errors theoretically for various conditions such as the fiducial-plane translation, and the principal distances where the extraction errors of image coordinates of the fiducial points were considered as the source of the errors. The estimation error of F and P are theoretically formulized with the analytical equations, and the effectiveness of the formulas is confirmed by comparing the values by the theory with those by the simulations.
KEYWORDS: Object recognition, Principal component analysis, Detection and tracking algorithms, Scattering, Robots, Machine vision, Computer vision technology, Robot vision, Active vision, Chemical elements
Parametric eigenspace methods are well known appearance-based methods for object recognition, which involves object classification and pose estimation. However, ordinary parametric eigenspace methods consider only the expressive features, and they suffer from a problem arising from the fact that discriminative features are not considered. So, there have been developed some methods to construct such eigenspaces considering the discriminative features. However, the method might suffer from another problem, i.e., the so-called generalized eigenvalue problem: yet, we can manage to solve the problem. In this paper, two methods are referred to as representative methods considering discriminative features. Conducting an experiment of object recognition on two similar objects, performances of the methods are compared to one another, and a piece of important knowledge is also presented that the discriminative features are more effective than the expressive features.
Precise works and manipulating micro objects are tough jobs for operators both mentally and physically. To execute these jobs smoothly without feeling wrongness, use of master-slave system is preferable because position and force are able to be scaled up and down as well under the system. In this study we develop a master-slave system where the size of a slave robot is very small and the slave robot is levitated by magnetic forces. In distinction from ordinary master- slave systems, the levitated robot does not get any other contact forces from outside. Thus we introduce a method using an impedance model for constructing the master-slave system. We confirmed the effectiveness of the positioning control algorithm through experiments.
Image segmentation is an important component of image processing which is necessary in the early stages of image analysis. Typical methods of image segmentation are utilizing region information. They use statistics, such as the mean and standard deviation of the pixel intensity within sub-images, with the final segmentation being obtained by a succession of splitting and merging processes of sub-images in order to create regions with quasi- homogeneous properties. In this paper, we propose a co- occurrence matrix based method of image segmentation in region-based techniques. It utilizes the observation that features of the multiple windows neighboring a pixel do not differ significantly from one another, and that features corresponding to pixels belonging to the same object form a cluster in the feature space, which may frequently be approximated by a Gaussian distribution. This paper extends the co-occurrence matrix based method. The definition of co- occurrence features is extended from one dimension to many dimensions: the number of observation windows is extended from two to an arbitrary number.
The 3D information obtained from camera, actually 2D image, is given as visual angles, and, so, we should evaluate the accuracy of calibration based on the visual angles. When we evaluate the accuracy, the visual angle, which direction should we take as the reference? True values of the coordinates of the principal points have been ordinarily used as the reference of the visual angle. But, the kind of evaluation criterion makes us overestimate visual angle errors although it is simple, and conforms to a fail-safe principle. And, it also causes us an ill effect that we should change fiducial-chart setup to suit an optimal condition that is given to each camera according to principal distances. We propose a novel criterion for the visual angle evaluation: the calibrated coordinates of principal points are taken as the references of the visual angle. It yields useful results: it markedly decreases visual angle estimation errors, and it also makes the optimal setup condition not vary with the principal distance. Further more, we present a useful formula enabling us to estimate calibration error for every camera in advance.
This paper presents an efficient method to search a target- object image appearing on an unknown scene image. The target-object is affine-transformed from the original model image, that is, translated, rotated and scaled. The Gabor transform is used to obtain the spectrum information from both the model and the scene images. The spectrum information of the model has the characteristic that the spectrum plane response regularly corresponding to the rotation and scale-change in real space. Using the characteristic, target-object can be correctly detected and the pose is also calculated by spectrum matching. Also the few Gabor functions can cover the whole frequency spectrum of the original image with less interference, very few numbers of features, that is, the Gabor-expansion coefficients, can represent the target-object. It result in the highly efficient calculation.
This paper present a method to reduce the calculation costs of object pose measurements. They are estimated by matching L-shaped line-segments in 3D object models with those in 2D object images using a P3P solution. The L segments have the minimum pieces of information for the estimation, and, therefore, it produces a large amount of L segments both in the 3D object models and in the 2D object images, resulting in enormous correspondences, and P3P calculations. To solve the problem, we propose a strategy enabling to select fewer L segments: as an evaluation function, we utilizes an average of the estimation errors when observing the L segments from representative points on a geodesic dome. Furthermore, we deduce a useful approximate formula for L segments having various shapes.
This paper presents a simple and robust pattern matching algorithm working on image-data level and requiring no feature extraction. A model picture is transformed into an estimated picture, and the estimated picture is matched to an actually input picture. Both the geometrical affine transformation and a linear gray-level transformation are examined, and the transformation parameters relating to the rotation, translation, expansion, and brightness are estimated by using a statistical optimization technique, i.e., an iterative non-linear least squares method where the residual sum of squares between the actually input picture and the estimated picture is used as an evaluation function. The characteristic of the proposed method is that the parameters are estimated by linear matrices calculations so that the calculation is markedly simplified and it could be processed in parallel for all the pixels. The matrices are easily calculated from the gray-level and its spatial derivatives in the horizontal and vertical directions in the model picture, and the gray-level in the actually input picture. As a result of some experiments for a simple pattern and a complicated one, it is confirmed that a translation parameter value is accurately estimated with approximately 0.1 pixel. The dynamics of parameter estimation are also examined.
A simple and accurate camera calibration method is presented in this paper, and the relation between accuracy of calibrated TV camera parameters and calibration condition is examined by applying a law of error propagation. The optimal calibration condition is proposed where an iterative method is applied to calibrate the parameter values. Furthermore, the variance of the estimated 3-D information is determined quantitatively in the case of the optimal calibration condition. These results are confirmed through experiments.
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