Akio Kosaka’s research while affiliated with Purdue University West Lafayette and other places

We designed a method for estimating the subjective age of a person; using this method, one evaluates one's own age by estimating whether a person shown in a facial image looks older or younger than oneself. Thus far, experiments have shown that Japanese people tend to underestimate their subjective age. In this study, we conducted an international comparative study to focus on the socio-psychological effects that may influence the underestimation of subjective age. Experiments were performed in which American and Japanese participants viewed American facial images; in addition, the American participants viewed Japanese facial images. Through these experiments, it was confirmed 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. Moreover, we carried out experiments of conventional look age estimates and compared the results to those of the subjective age estimates.

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.

A detailed understanding of subcellular structure motility is critical to understanding how cells regulate the delivery of specific proteins from the site of synthesis to the site of action. Diverse modalities have been observed for subcellular dynamics, such as directional movement, random movement, tethered movement, object appearing, and disappearing. Motion modality detection is important for efficient subcelluar structure tracking and population study. In this paper, we present a new technique, called "divergence filter," for detecting subcellular structure motion modalities. The plausibility of the efficient technique was applied to caveolar membrane trafficking sequences obtained using confocal microscopy.

Viewpoint calibration is a method to manipulate hand-eye for generating calibration parameters for active viewpoint control and object grasping. In robot vision applications, accurate vision sensor calibration and robust vision-based robot control are essential for developing an intelligent and autonomous robotic system. 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/Kawasaki robot generates an arbitrary viewpoint. To attain this accuracy, our new formalism of hand-eye calibration deals with a lens distortion model of a vision sensor. Our most distinguished approach of optimizing intrinsic parameters is to utilize a new parameter estimation algorithm using an extended Kalman filter. Most previous approaches did not even consider the optimal estimates of the intrinsic and extrinsic camera parameters, or chose one of the estimates obtained from multiple solutions, which caused a large amount of estimation error in hand-eye calibration. We demonstrate the power of this new method for: (1) generating 3-D object models using an interactive 3-D modeling editor; (2) recognizing 3-D objects using stereovision systems; and (3) grasping 3-D objects using a manipulator. Experimental results using Puma and Kawasaki robots are shown.

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^deg.

We propose a novel evidence accumulation framework that accurately estimates the positions of humans in a 3D environment. The framework consists of a network of distributed agents having different functionalities. The modular structure of the network allows scalability to large surveillance areas and robust operation. The framework does not assume reliable measurements in single cameras (referred to as dasiasensing agentspsila in our framework) or reliable communication between different agents. There is a position uncertainty associated with single camera measurements and it is reduced through an uncertainty reducing transform that performs evidence accumulation using multiple camera measurements. Our framework has the advantage that single camera measurements do not need to be temporally synchronized to perform evidence accumulation. The system has been tested for detecting single and multiple humans in the environment. We conducted experiments to evaluate the localization accuracy of the position estimates obtained from the system by comparing them with the ground truth. Also, two different configurations of the agents were tested to compare their detection performance.

Introduction to Stereo Vision Mathematical Preliminaries Basic Strategy for Model-Based Stereo Vision Stereo Vision for Dump Truck Recognition and Localization Model-Based Stereo Vision for Recognition and Localization of Alternator Covers Conclusions References

In this paper, we propose a new approach that uses a motion–estimation based framework for video tracking of objects in cluttered environments. Our approach is semi-automatic, in the sense that a human is called upon to delineate the boundary of the object to be tracked in the first frame of the image sequence. The approach presented requires no camera calibration; therefore it is not necessary that the camera be stationary. The heart of the approach lies in extracting features and estimating motion through multiple applications of Kalman filtering. The estimated motion is used to place constraints on where to seek feature correspondences; successful correspondences are subsequently used for Kalman-based recursive updating of the motion parameters. Associated with each feature is the frame number in which the feature makes its first appearance in an image sequence. All features that make first-time appearances in the same frame are grouped together for Kalman-based updating of motion parameters. Finally, in order to make the tracked object look visually familiar to the human observer, the system also makes its best attempt at extracting the boundary contour of the object—a difficult problem in its own right since self-occlusion created by any rotational motion of the tracked object would cause large sections of the boundary contour in the previous frame to disappear in the current frame. Boundary contour is estimated by projecting the previous-frame contour into the current frame for the purpose of creating neighborhoods in which to search for the true boundary in the current frame. Our approach has been tested on a wide variety of video sequences, some of which are shown in this paper.

This paper proposes a novel method, using constant inter-frame motion, for self-calibration from an image sequence of an object rotating around a single axis with varying camera internal parameters. Our approach makes use of the facts that in many commercial ...

This study was performed to design an automatic liver region extraction system to facilitate clinical liver size estimation and further serve as a prestage for liver reconstruction and volume estimation. We present a modification of the well-known snakes algorithm for extracting liver regions in noisy CT images. Our modification addresses the issues of selection of the control points on an estimate of the contour and the determination of the weighting coefficients. The weighting coefficients are determined dynamically on the basis of the distance between the control points and the local curvature of the contour. The proposed method was used in extracting liver regions from 98 cross-sectional abdominal images. The overall performance was estimated by comparisons with original liver regions. The deformable model method enables an efficient and effective automatic liver region extraction in noisy environments. This approach eliminates human-in-the loop, which is the common practice for the majority of current methods.