A new method to recover and display 3D fundus pattern on the inner bottom surface of eye-ball from stereo fundus image pair is developed. For the fundus stereo images, a simple stereo technique does not work, because the fundus is observed through eye lens and a contact wide-angle enlarging lens. In this method, utilizing the fact that fundus forms a part of sphere, we identify their optical parameters and correct the skews of the lines-of-sight. Then, we obtain 3D images of the fundus by back-projecting the stereo images.

We introduce a crystalline flow for a contour figure analysis. The crystalline flow is a special family of evolving polygons, and is considered as a discrete version of a classical curvature flow. In the evolving process of the crystalline flow, each facet moves toward its normal direction. The velocity of the facet is determined by the nonlocal curvature, which depends on the length of the facet. Different from a classical curvature flow, it is easy to track each facet in a given contour through the evolving process, because a given polygon remains polygonal. This aspect helps us to make a scale-space representation of a contour in an image. In this article, we present a method for extracting dominant corners using a crystalline flow. Experimental results show that our method extracts several sets of dominant corner facets successfully from a given contour figure.

During the development of a human embryo, the position of eyes moves medially and caudally in the viscerocranium. A statistical model of this process can play an important role in embryology by facilitating qualitative analyses of change. This paper proposes an algorithm to construct a spatiotemporal statistical model for the eyeballs of a human embryo. The proposed modeling algorithm builds a statistical model of the spatial coordinates of the eyeballs independently for each Carnegie stage (CS) by using principal component analysis (PCA). In the process, a q-Gaussian distribution with a model selection scheme based on the Aaike information criterion is used to handle a non-Gaussian distribution with a small sample size. Subsequently, it seamlessly interpolates the statistical models of neighboring CSs, and we present 10 interpolation methods. We also propose an estimation algorithm for the CS using our spatiotemporal statistical model. A set of images of eyeballs in human embryos from the Kyoto Collection was used to train the model and assess its performance. The modeling results suggested that information geometry-based interpolation under the assumption of a q-Gaussian distribution is the best modeling method. The average error in CS estimation was 0.409. We proposed an algorithm to construct a spatiotemporal statistical model of the eyeballs of a human embryo and tested its performance using the Kyoto Collection.

In this article, we propose a vehicle positioning method that can estimate positions of cars even in areas where the GPS is not available. For the estimation, each car measures the relative distance to a car running in front, communicates the measurements with other cars, and uses the received measurements for estimating its position. In order to estimate the position even if the measurements are received with time-delay, we employed the time-delay tolerant Kalman filtering. For sharing the measurements, it is assumed that a car-to-car communication system is used. Then, the measurements sent from farther cars are received with larger time-delay. It follows that the accuracy of the estimates of farther cars become worse. Hence, the proposed method manages only the states of nearby cars to reduce computing effort. The authors simulated the proposed filtering method and found that the proposed method estimates the positions of nearby cars as accurate as the distributed Kalman filtering.