We have proposed a method for estimating the subjective age of a person; that is, a method that yields a person's age on the basis of estimations made by that person about the age of other people by observing their facial images. Thus far, experiments have shown that Japanese people tend to underestimate their subjective age. In this study, we focus on the socio-psychological effects that may influence the underestimation of subjective age. We conducted an international comparative study. In this study, experiments were performed in which American and Japanese participants viewed American facial images; in addition, the American participants also viewed Japanese facial images. Through these experiments, it was confirmed that the subjective age generally tended toward the negative direction despite differences in the nationalities and cultures of the Japanese and American participants and their facial images. Moreover, it was found that nationality and culture may have some effects on the estimation; for example, American males did not exhibit the tendency to underestimate the age, unlike Japanese male. When estimating facial images of different nationalities, the variance generally tended to be larger, although the average was similar. This study suggests that the underestimation of age occurs despite differences in Japanese and American societies and cultures; however, the tendency of underestimation of age is not related to facial images but to social and cultural factors that influence the participants.

The best of Kalman-filter-based frameworks reported in the literature for rigid object tracking work well only if the object motions are smooth (which allows for tight uncertainty bounds to be used for where to look for the object features to be tracked). In this contribution, we present a new Kalman-filter-based framework that carries out fast and accurate rigid object tracking even when the object motions are large and jerky. The new framework has several novel features, the most significant of which is as follows: the traditional backtracking consists of undoing one-at-a-time the model-to-scene matchings as the pose-acceptance criterion is violated. In our new framework, once a violation of the pose-acceptance criterion is detected, we seek the best largest subset of the candidate scene features that fulfill the criterion, and then continue the search until all the model features have been paired up with their scene correspondents (while, of course, allowing for nil-mapping for some of the model features). With the new backtracking framework, our Kalman filter is able to update on a real-time basis the pose of a typical industrial 3-D object moving at the rate of approximately 5 cm/s (typical for automobile assembly lines) using off-the-shelf PC hardware. Pose updating occurs at the rate of 7 frames per second and is immune to large jerks introduced manually as the object is in motion. The objects are tracked with an average translational accuracy of 4.8 mm and the average rotational accuracy of 0.27$^\circ$.

Tagging and tracking protein molecules are a key to a better understanding of proteomics in diverse aspects. In this paper, a common framework of particle filter using optimal importance function is proposed for confocal protein molecules tracking. To deal with the challenges stemming from small size, deformable shape, noisy environment, and multi-modality motion, a stochastic process based particle filter is used. Partial Gaussian state space (PGSS) model is developed as the importance function to incorporate the latest measurement in the state estimation. Experimental results have demonstrated the performance of the proposed algorithm for both Brownian and translational motion.

This paper presents a robust model-based visual tracking algorithm that can give accurate 3D pose of a rigid object. Our tracking algorithm uses an incremental pose update scheme in a prediction-verification framework. Extended Kalman filter is used to update the pose of a target incrementally to minimize the error between the expected map of the target model and the corresponding gradient edge in the image space. The main contributions of this paper include: 1) A novel approach to how we use the two extremities of straight-lines as features. By taking into account the measurement uncertainties associated with the locations of the extracted extremities of the straight-line, our approach can compare correctly two straight-lines of different lengths. 2) Our use of a test of mean criterion for initiating backtracking and our use of a variable threshold on the output of this criterion that makes nil-matching more effective. We have tested our tracking algorithm with image sequences containing highly cluttered backgrounds. The system successfully tracks objects even when they are highly occluded.

In this paper, we propose a new approach to simultaneously estimate the motion parameters and shape parameters of a rigid object over the image sequence while the object is in motion. Our approach applies the Kalman filter multiple times to sequentially reduce the error of feature point correspondences and to generate an accurate motion parameter by propagating the object motion uncertainty over the image frame as well as optimally establishing the feature correspondences on the basis of template-based optical flows. Some preliminarily experimental results are shown to verify our proposed approach.

Viewpoint calibration is a method to manipulate hand-eye for generating calibration parameters for active viewpoint control and object grasping. This paper presents a new approach to hand-eye robotic calibration for vision-based object modeling and grasping. Our method provides a 1.0 pixel level of image registration accuracy when a standard Puma robot generates an arbitrary viewpoint. In order to attain this accuracy, our new formalism of hand-eye calibration deals with a lens distortion model of a vision sensor and utilizes a new parameter estimation algorithm using an extended Kalman filter. We demonstrate the power of this new method called "SmartView" for: 1) generating 3D object models using an interactive 3D modeling editor; 2) recognizing 3D objects using stereo vision systems; and 3) grasping 3D objects using a manipulator. Experimental results using a Puma robot are shown.

This paper presents a robust and accurate vision-based augmented reality system for surgical navigation. The key point of our system is a robust and real-time monocular vision algorithm to estimate the 3D pose of surgical tools, utilizing specially designed code markers and Kalman filter-based position updating. The vision system is not impaired by occlusion and rapid change of illumination. The augmented reality system superimposes the 3D object wireframe onto the live viewing image taken from the surgical microscope as well as displaying other useful navigation information, while allowing the surgeons to freely change its zoom and focus for viewing. The experimental results verified the robustness and usefulness of the system, and acquired the image registration error less than 2 mm.

A new general-purpose color image segmentation editor (CISE) for the purpose of extracting a semantic object is designed, implemented and tested on a number of various natural scene images. Our editor integrates a deformable model and image statistics including intensity, color, gradient and texture. The editor starts with a coarse region segmentation which applies the Canny's operator followed by a low-complexity edge linking algorithm. This segmentation basically builds regions of smooth intensity by closing all dangling edges. Next, a topologically-based region labeling method makes full use of relationship among pixels and produces useful labeled image. Finally, to refine the extracted object of interest (O/sup 2/I), a deformable model based on energy minimization is applied by incorporating both the gradient and region criteria to the external constraint force. These processes are demonstrated through examples on natural scene color images. Experimental results suggest the efficiency and accuracy of the algorithm in its segmentation operations.

This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is achieved by a model-based vision system, and nonstop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We report on experiments in a hallway using the YAMABICO robot.

This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is done with a mode-based vision system, and a non-stop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We report on experiments in a hallway using the YAMABICO robot.

Vision-based bin-picking is increasingly more difficult as the complexity of target objects increases. We propose an efficient solution where complex objects are sufficiently represented by simple features/cues; thus invariance to object complexity is established. The region extraction algorithm utilized in our approach is capable of providing the focus of attention to the simple cues as a trigger toward recognition and pose estimation. Successful bin-picking experiments of industrial objects using stereo vision tools are presented.

A vision-based motion tracking method described in this paper estimates the 3D position and orientation of a moving object of known shape at an average speed of 2.5 seconds per image frame even in complex environments using a conventional computer power. Given a coarse estimate of the initial 3D object pose, the method first generates apt expectation view from which visible model features are automatically selected. The method then extracts potentially matched image features from image regions bounded by the propagation of object motion uncertainty. The special aspect of our vision-based trading is an optimal correspondence search for model features and image features in which we use a Kalman filter-based updating scheme to perform the precise 3D object pose estimation. Experimental results are presented to demonstrate the robustness of the method even in the presence of occlusion.

The model-based vision system described in this thesis allows a mobile robot to navigate indoors at an average speed of 8 meters/minute using ordinary laboratory computing hardware of approximately 16 MIPS power. The navigation capabilities of the robot are not impaired by the presence of the stationary or moving obstacles. The vision system maintains a model of uncertainty and keeps track of the growth of uncertainty as the robot travels towards the goal position. The estimates of uncertainty are then used to predict bounds on the locations and orientations of landmarks expected to be seen in a monocular image. This greatly reduces the search for establishing correspondence between the features visible in the image and the landmarks. Given a sequence of image features and a sequence of landmarks derived from a geometric model of the environment, a special aspect of our vision system is the sequential reduction in the uncertainty as each image feature is matched successfully with a landmark, allowing subsequent features to be matched more easily, this being a natural byproduct of the manner in which we use Kalman-filter based updating. Strategies for path planning, path replanning and perception planning are introduced for the robot to navigate in the presence of obstacles. Finally, experimental results are presented.