There are methods used to test the optical purity of enantiomers; however, most of the simple methods are not precise and more complicated methods are better. As a result, these methods cannot be widely used for industrial purposes. The aim of this research is to design a sensor which can discriminate D-amino acids from L-amino acids. The designed sensor has chiral membranes and uses the technique of impedance change of these chiral membranes to discriminate the amino acids. We used a noise-FFT (Fast Fourier Transform) technique to determine the membrane impedance. When an enantiomer membrane resides in a chiral environment, (E*), diastereomeric interactions (E*-D) and (E*-L) are created, which may differ sufficiently in the arrangement of molecules of the membranes so as to permit the discrimination of optical substances due to the change in membrane characteristics. With increasing concentrations of the amino acids, the membrane resistance changes depended on the optical activity of the amino acids. The results suggest that the impedance changes of the chiral membrane with diastereomeric reaction can be used for the high-performance chemical sensor to measure the optical purity of different substances.

We present a new basis for discrete representation of stereo correspondence. This center referenced basis permits a more natural, complete and concise representation of constraints in stereo matching. In this context a MAP formulation for disparity estimation is derived and reduced to unconstrained minimization of an energy function. Incorporating natural constraints, the problem is simplified to the shortest path problem in a sparsely connected trellis structure which is performed by an efficient dynamic programing algorithm. The computational complexity is the same as the best of other dynamic programming methods, but a very high degree of concurrency is possible in the algorithm making it suitable for implementation with parallel procesors. Experimental results confirm the performance of this method and matching errors are found to degrade gracefully in exponential form with respect to noise.

We propose a new method for generating visual stereo images from the common two dimensional images without 3D reconstruction. The major novel contributions of this report are in two aspects. First, we address the detection of dominant motion presented in the given scenes, for doing so we borrow phase shift theorem and calculate the inverse Fourier transform of cross-power spectrum to find the maximum peak value whose position can be used to decide motion parameters. Secondly, unlike most of researchers study the stereo vision to recover 3D information for modeling and rendering, we address the visual stereo image generation without 3D reconstruction by applying the computed motion parameters to make decision of selecting two given images to form a stereo pair for left eye and right eye respectively. The proposed approaches can be employed for applications such as navigation in a virtual environment.

When a micromachine works inside a narrow space inside tubes and equipment such as a microfactory, a microdevice that has a visual function is indispensable. To monitor the minute shapes of microfabrication and microassembly process that are impossible to observe, fiber-optic sensors and actuators for environmental recognition devices have been developed. The devices are designed to allow stereoscopic and microscopic observation and to measure the dimensions of microparts. To achieve these goals and to realize minute structures and functions, we developed environmental recognition devices for microfabrication process with functions of far and near field observation, tactile sensing and tip articulation, for microassembly process with functions of stereoscopic observation and tip articulation. The results show that easy and safe environmental recognition is possible in the narrow spaces of a microfactory.

Human visual system can perceive 3-D structure of an object by binocular disparity, gradient of illumination (shading), occlusion, textures, perspective and so forth. Among them, binocular disparity seems to be the essentially important cues for the 3-D space perception and it is used widely for displaying 3-D visual circumstances such as in VR (virtual reality) system or 3-D TV. Visual illusions seem to be one of the phenomena which are purely reflecting the mechanism of human visual system. In the recent several years, the authors found several new types of 3-D visual illusions with binocular viewing. Entire 3-D illusory object including volume perception, transparency, dynamic illusions can be perceived only from the visual stimuli of disparity given by some inducing objects arranged with suitable relations. In this report, the authors introduced these newly found visual illusions and made some considerations on the human visual mechanism of 3-D perception and on their exploitation for new effective techniques in 3-D display. They introduced especially on the visual effect in two kinds of arrangement with occlusion and sustaining relationship between the illusory object and inducing objects. In the former case, the inducing objects which provide the stimuli were named as occlusion cues and classified into two types: contour occlusion cues and bulky occlusion cues. In the later case, those inducing objects were named as sustaining cues and a 3-D fully transparent illusory object was perceived. The perception was just like imagined from the scenes of the actions and positions of the pantomimists; then this phenomena was named as "Mime (Pantomime) Effect. " According to the positions of sustaining cues, they played different actions in this perception, and they are classified into three types: front sustaining cues, side sustaining cues and back sustaining cues. In addition, dynamic fusion and separation of volumetrical illusory objects were perceived when the visual stimuli were moving continuously between two structurally different conditions. Then the hysteresis was recognized in geometrical position between the fusion and separation. The authors believe that the occlusion cues and sustaining cues introduced in this paper could be effective clues for exploiting the new techniques for 3-D display.

This paper surveys the results of various studies on 3-D image coding. Themes are focused on efficient compression and display-independent representation of 3-D images. Most of the works on 3-D image coding have been concentrated on the compression methods tuned for each of the 3-D image formats (stereo pairs, multi-view images, volumetric images, holograms and so on). For the compression of stereo images, several techniques concerned with the concept of disparity compensation have been developed. For the compression of multi-view images, the concepts of disparity compensation and epipolar plane image (EPI) are the efficient ways of exploiting redundancies between multiple views. These techniques, however, heavily depend on the limited camera configurations. In order to consider many other multi-view configurations and other types of 3-D images comprehensively, more general platform for the 3-D image representation is introduced, aiming to outgrow the framework of 3-D "image" communication and to open up a novel field of technology, which should be called the "spatial" communication. Especially, the light ray based method has a wide range of application, including efficient transmission of the physical world, as well as integration of the virtual and physical worlds.

Acquisition of three-dimensional information of a real-world scene from two-dimensional images has been one of the most important issues in computer vision and image understanding in the last two decades. Noncontact range acquisition techniques can be essentially classified into two classes: Passive and active. This paper concentrates on passive depth extraction techniques which have the advantage that 3-D information can be obtained without affecting the scene. Passive range sensing techniques are often referred to as shape-from-x, where x is one of visual cues such as shading, texture, contour, focus, stereo, and motion. These techniques produce 2.5-D representations of visible surfaces. This survey discusses aspects of this research field and reviews some recent advances including video-rate range imaging sensors as well as emerging themes and applications.

This paper describes a factorization-based algorithm that reconstructs 3D object structure as well as motion from a set of multiple uncalibrated perspective images. The factorization method introduced by Tomasi-Kanade is believed to be applicable under the assumption of linear approximations of imaging system. In this paper we describe that the method can be extended to the case of truly perspective images if projective depths are recovered. We established this fact by interpreting their purely mathematical theory in terms of the projective geometry of the imaging system and thereby, giving physical meanings to the parameters involved. We also provide a method to recover them using the fundamental matrices and epipoles estimated from pairs of images in the image set. Our method is applicable for general cases where the images are not taken by a single moving camera but by different cameras having individual camera parameters. The experimental results clearly demonstrates the feasibility of the proposed method.

We discuss optimal rotation estimation from two sets of 3-D points in the presence of anisotropic and inhomogeneous noise. We first present a theoretical accuracy bound and then give a method that attains that bound, which can be viewed as describing the reliability of the solution. We also show that an efficient computational scheme can be obtained by using quaternions and applying renormalization. Using real stereo images for 3-D reconstruction, we demonstrate that our method is superior to the least-squares method and confirm the theoretical predictions of our theory by applying bootstrap procedure.

This paper presents a method which can effectively acquire free space on a plane for moving forward in safety by using height information of objects. This method can be applied to free space extraction on a road, and, in short, it is a road extraction method for an autonomous vehicle. Since a road area can be assumed to be a sequence of flat planes in front of a vehicle, it is effective to apply the inverse perspective projection model to the ground plane. However, conventional methods using this model have a drawback in that some areas on the road plane are wrongly detected as obstacle areas since these methods are sensitive to the error of the camera geometry with respect to the assumed plane. In order to overcome this drawback, the proposed approach named the Planar Projection Stereopsis (PPS) method supplies, to the road extraction method using the inverse perspective projection model, a contrivance for removing these erroneous areas effectively. Since PPS uses the inverse perspective projection model, both left and right images are projected to the road plane and obstacle areas are detected by examining the difference between these projected images. Because detected obstacle areas include a lot of erroneous areas, PPS examines the shapes of the obstacle areas and eliminates falsely detected areas on the road plane by using the following properties: obstacles whose heights are different from the road plane are projected to the shapes falling backward from the location where the obstacles touch the road plane; and the length of shapes falling backward depends on the location of obstacles in relation to the stereoscopic cameras and the height of obstacles in relation to the road plane. Experimental results for real road scenes have shown the effectiveness of the proposed method. The quantitative evaluation of the results has shown that on average 89. 3% of the real road area can be extracted and the average of the falsely extracted ratio is 1. 4%. Since the road area can be extracted by simple projection of images and subtraction of projected images from a set of stereo images, our method can be applied to real-time operation.

This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.

This paper presents a new scheme to estimate depth in a natural three-dimensional scene using a multi-viewpoint image set. In the conventional Multiple-Baseline Stereo (MBS) scheme for the image set, although errors of stereo matching are somewhat reduced by using multiple stereo pairs, the use of square blocks of fixed size sometimes causes false matching, especially, in that image area where occlusion occurs and that image area of small variance of brightness levels. In the proposed scheme, the reference image is segmented into regions which are capable of being arbitrarily shaped, and a depth value is estimated for each region. Also, by comparing the image generated by projection with the original image, depth values are newly estimated in a top-down manner. Then, the error of the previous depth value is detected, and it is corrected. The results of experiments show advantages of the proposed scheme over the MBS scheme.

An implementation of photometric stereo is described in which all directions of illumination are close to and rotationally symmetric about the viewing direction. THis has practical value but gives rise to a problem that is numerically ill-conditioned. Ill-conditioning is overcome in two ways. First, many more than the theoretical minimum number of images are acquired. Second, principal components analysis (PCA) is used as a linear preprocessing technique to determine a reduced dimensionality subspace to use as input. The approach is empirical. The ability of a radial basis function (RBF) neural network to do non-parametric functional approximation is exploited. One network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Results on real data are demonstrated.

In this paper, a new method for measuring three-dimensional (3D) moving facial shapes is introduced. This method uses two light sources and a slit pattern projector. First, the normal vectors at points on a face are computed by the photometric stereo method with two light sources and a conventional video camera. Next, multiple light stripes are projected onto the face with a slit pattern projector. The 3D coordinates of the points on the stripes are measured using the stereo vision algorithm. The normal vectors are then integrated within 2D finite intervals around the measured points on the stripes. The 3D curved segment within each finite interval is computed by the integration. Finally, all the curved segments are blended into the complete facial shape using a family of exponential functions. By switching the light rays at high speed, the time required for sampling data can be reduced, and the 3D shape of a moving human face at each instant can be measured.

Introducing a mathematical model of noise in stereo images, we propose a new criterion for intelligent statistical inference about the scene we are viewing by using the geometric information criterion (geometric AIC). Using synthetic and real-image experiments, we demonstrate that a robot can test whether or not the object is located very far away or the object is a planar surface without using any knowledge about the noise magnitude or any empirically adjustable thresholds.

We present a very fast method for calculating an inverse filter for audio reproduction system. The proposed method of FFT-based inverse filter design, which combines the well-known principles of least squares optimization and regularization, can be used for inverting systems comprising any number of inputs and outputs. The method was developed for the purpose of designing digital filters for multi-channel sound reproduction. It is typically several hundred times faster than a conventional steepest descent algorithm implemented in the time domain. A matrix of causal inverse FIR (finite impulse response) filters is calculated by optimizing the performance of the filters at a large number of discrete frequencies. Consequently, this deconvolution method is useful only when it is feasible in practice to use relatively long inverse filters. The circular convolution effect in the time domain is controlled by zeroth-order regularization of the inversion problem. It is necessary to set the regularization parameter β to an appropriate value, but the exact value of β is usually not critical. For single-channel systems, a reliable numerical method for determining β without the need for subjective assessment is given. The deconvolution method is based on the analysis of a matrix of exact least squares inverse filters. The positions of the poles of those filters are shown to be particularly important.

This paper describes a statistical integration algorithm for color, motion and stereo disparity, and introduces a real-time stereo system that can tell us where and what objects are moving. Regarding the integration algorithm, motion estimation and depth estimation are simultaneously performed by a clustering process based on motion, stereo disparity, color, and pixel position. As a result of the clustering, an image is decomposed into region fragments. Eath fragment is characterized by distribution parameters of spatiotemporal intensity gradients, stereo difference, color and pixel positions. Motion vectors and stereo disparities for each fragment are obtained from those distribution parameters. The real-time stereo system can view the objects with the distribution parameters over frames. The implementation and experiments show that we can utilize the proposed algorithm in real-time applications such as surveillance and human-computer interaction.

This paper describes our newly developed intelligent sensor system which comprises two eyes and four ears on a movable head. It can acquire its dynamical visual and auditory image of its surrouding 3-D environment while showing humanlike behavior naturally and autonomously. The most important feature of the sensor system is in an autonomous and optimum sensory architecture of it. This enables the sensor to achieve 1) repid (5 ms) and accurate (2 deg) auditory localization, 2) rapid (0.5 s/65536 pixel) extraction of visual motion in marginal view, 3) rapid (several TV frames' time) eye movement and binocular fixation to a suddenly appeared object, 3) rapid (0.1 s/4096 pixel) extraction of 3-D object profile and image features, which is activated by its own auditory localization and motion detection. We describe in this paper the several key items for realizing this sensor.

A new approach is presented for recovering the 3-D shape from shading image. Photometric Stereo Method (PSM) is generally based on the direct illumination. PSM in this paper is modified with the indirect diffuse illumination method (IDIM), and then applied to hybrid reflectance model which consists of two components; the Lambertian reflectance and the specular reflectance. Under the hybrid reflectance model and the indirect diffuse illumination circumstances, the 3-D shape of objects can be recovered from the surface normal vector extracted from the surface roughness, the surface reflectance ratio, and the intensity value of a pixel. This method is rapid because of using the reference table, simplifies the restriction condition about the reflectance function in prior studies without any loss in performance, and can be applied to various types of surfaces by defining general reflectance function.

Theoretically, corresponding pairs of feature points between two stereo images can determine their 3-D locations uniquely by triangulation. In the presence of noise, however, corresponding feature points may not satisfy the epipolar equation exactly, so we must first correct the corresponding pairs so as to satisfy the epipolar equation. In this paper, we present an optimal correction method based on a statistical model of image noise. Our method allows us to evaluate the magnitude of image noise a posteriori and compute the covariance matrix of each of the reconstructed 3-D points. We demonstrate the effectiveness of our method by doing numerical simulation and real-image experiments.