Abstract: Obtaining visual information is a crucial issue for autonomous robots. Indeed per¬ception of 3D depth information can be achieved by 3D measurement instruments such as laser range fingers and stereo visions, but the obtained range data is no more than a cloud 3d coordinate data. To obtain meaningful information for objects, more advanced intellgent sens¬ing is required. In this paper, a fuzzy-clustering-based modeling method of multiple quadric surfaces in a scene is proposed. This method is intended for scenes involving multiple objects, where each object is approximated by a primitive model. The proposed method is composed of three steps. In the first step, 3D data is reconstructed using a stereo matching technique from a stereo image whose scene involves multiple objects. Next, the 3D data is divided into a single object by employing fuzzy c-means accompanied by principal component analysis (PCA) and a criterion with respect to the number of clusters. Finally, the shape of each object is extracted by fuzzy c-varieties with noise clustering. The proposed method was evaluated with respect to some pilot scenes whose ground truth data is known, and it was shown to specify each location and each shape for multiple objects very well.
Abstract: Due to the wide diffusion of 3D maps, supporting tool for constructing the 3D maps are required. Individual information necessary for constructing the 3D maps is gathered by some measurement instruments. For example, 3D information and wall textures are acquired by triangulation (or GPS measurement) and optical devices, respectively. Building polygons in a 3D map can be constructed easily by combining the 3D (height) information and a 2D map. It is, however, difficult to obtain an appropriate texture for an arbitrary building polygon by combining those measurements information. In general, the texture mapping with the acquired image is performed manually. However, it takes huge costs to perform in a wide area. Therefore, the demand of the automation is extremely high. In this study, we aim to automatize the texture mapping by image information from the in-vehicle camera. An in-vehicle camera has the advantage that it is possible to take a picture of the building wall while driving over the broad area. But, the vibration and the traffic have influences on the photography environment. As a result, it is difficult to specify the building area in the image. In the proposed method, tracks of the camera are calculated from the acquired continuous image, the building area that corresponds to the polygon on the map is specified. In this paper, we described our observation about the result of a miniature model.
Abstract: Autonomous robots are at advanced stage in various fields, and they are expected to autonomously work at the scenes of nursing care or medical care in the near future. In this paper, we focus on object counting task by images. Since the number of objects is not a mere physical quantity, it is difficult for conventional phisical sensors to measure such quantity and an intelligent sensing with higher-order recognition is required to accomplish such counting task. It is often that we count the number of objects in various situations. In the case of several objects, we can recognize the number at a glance. On the other hand, in the case of a dozen of objects, the task to count the number might become troublesome. Thus, simple and easy way to enumerate the objects automatically has been expected. In this study, we propose a method to recognize the number of objects by image. In general, the target object to count varies according to user's request. In order to accept the user's various requests, the region belonging to the desired object in the image is selected as a template. Main process of the proposed method is to search and count regions which resembles the template. To achieve robustness against spatial transformation, such as translation, rotation, and scaling, scale-invariant feature transform (SIFT) is employed as a feature. To show the effectiveness, the proposed method is applied to few images containing everyday objects, e.g., binders, cans etc.
Abstract: Perceptions of image surface are very challenging work for computer vision. Human can amazingly expert at recognizing the reflective properties of surfaces of various materials which a robot can not do easily so far. Smoothly we can differentiate a shiny metallic sphere from the plastic sphere of similar dimensions and structure. In this paper, various image surfaces are analyzed according to various image statistics for robot vision systems. Identification of synonymous objects under various real-world illumination or other environments are very daunting task. However, this is very challenging and crucial for machine vision systems. Both statistical analyses and human evaluation by various subjects under rigorous illumination conditions, we find significant improvement in our analysis and emphasis the importance of statistical evaluation of surfaces for computer vision. Our findings clearly demonstrate that skewness has direct resemblance with the surface glossiness-level. Intensity histogram also shows crucial clue for surface analysis.
Abstract: In recent, brain function field analysis attracts concentrated attention, especially on the significant study about BMI (Brain-machine Interface) using fMRi, NIR[1-5], EEG. However, it is known that there exists a problem for the use of an input support device with this serious problem on considerable time for extracting characteristic event related pattern from brain wave and for the large-scale and large-amount device itself such as the MRI equipment. This study aims at rapid BMI pattern recognition for the eye-ball movement, which is considered to be removed a factor from EEG as an artifact . We investigated the repeatability of eye-ball movement Event Related scalp electroencephalogram Potential (ERP) and the characteristics, which possess steady, high voltage and 50ms prompt reaction. As the ERP pattern discriminator, this paper proposes 2 methods, ISE based Euclid Norm and linear mapped Euclid Norm methods.
Abstract: Vibration suppression control considering elastic part deformation is one of the indispensable issues in the precision engineering field and a high-density LSI circuit processing. This study aims at suppressing the induced vibration at rapid positioning operation using the N4SID (Subspace State Space model Identification) Model Referenced predictive Anti-phase pitch Driving Active Damping (N4SID-MRAD) and improve the positioning performance limit generated by conventional PID feedback control with existing actuator, which has low dynamic characteristics with a time lag. This control method consists of reduced order model identification (model order reduction using N4SID concept: offline process) and predictive control using the counter pitch driving based on the model output (: on-line process). This method was applied to principal axis vibration suppression of three axes positioning machine driven by a ball screw feeding mechanism to control the probe tip end positioning in feeding rod axis. The result showed that amplitude of residual vibration at the probe tip end suppressed with conventional PI controlled and the 77% performance improvement under low stable conditions that Balanced Realization could not be applied.
Abstract: In this paper, we propose an object tracking system using an arm robot and two pan-tilt cameras. By combining these devices, we realize the high speed and wide range object tracking method. In order to trace an object, we must find the pattern of object in camera images. To perform fast object detection, we employ a method of the particle filter which describes object location probabilistically. In experiments, our tracking system can trace objects which move surround of the system, and we can confirm effectiveness of proposed method.
Abstract: Active triangulation methods assume that points in a scene are only illuminated by the sources of light. However, this assumption is valid when the light is reflected at one time from a single convex surface. Actual objects often include concave surfaces where points reflect light between themselves. In the presence of these interreflections, active triangulation methods produce erroneous estimates of shape. In this paper, an approach is proposed to recover the shape of a specular object in the presence of interreflection, with prior knowledge on the shape information of the specular object or not. Both simulation and experiment results proved that the proposed approach was effective.
Abstract: Many practical tasks in industry require the simultaneous determination of 3-D shape and refractive index of a transparent object. We have formulated an approach to evaluating the surface shape and refractive index by light path analysis. The effectiveness of the proposed method was confirmed from experimental results.
Abstract: The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.