We propose a new algorithm to detect the irises of both eyes from a human face in an intensity image. Using the separability filter proposed by Fukui et al., the algorithm first extracts intensity valleys, which we call blobs in this paper, as the candidates for the irises. Next, for each pair of blobs, the algorithm computes a cost using the Hough transform and separability filter to measure the fit of the pair of blobs to the image. Then, the algorithm selects a pair of blobs with the smallest cost as the irises of both eyes. As the result of the experiment using all faces without spectacles in the face database of the University of Bern, the success rate of the proposed algorithm was 96.5% on the average.

In this paper we propose a new algorithm to detect the irises of both eyes from a human face in an intensity image. Using the separability filter proposed by Fukui et al. (1997), the algorithm first extracts intensity valleys, which we call blobs in this paper, as the candidates for the irises. Next, for each pair of blobs, the algorithm computes a cost using Hough transform and separability filter to measure the fit of the pair of blobs to the image. And then, the algorithm selects a pair of blobs with the smallest cost as the irises of both eyes. As the result of the experiment using all faces without spectacles in the face database of the University of Bern, the success rate of the proposed algorithm was 96.5% on average.

In this paper we propose an algorithm to detect a 2D target model in cluttered environments and to find its scale s, translation (tx, ty) and orientation θ. The algorithm first extracts straight line segments from the image. Next, generating transformation vectors (s, tx, ty, θ), the algorithm computes the fitness values for these vectors through matching between data line segments extracted from the image and model line segments transformed by (s, tx, ty, θ). And, by a generic algorithm, the algorithm searches for a transformation vector with the highest fitness value. The obtained transformation vector gives the scale, translation and orientation of the model in the image.

In this paper we present a new algorithm to detect ellipses in an intensity image. The algorithm first extracts line-support regions from the original image. Next, candidates for elliptic arcs are selected from these line-support regions. In the proposed algorithm, each ellipse is defined by a triplet of line-support regions. And, using a genetic algorithm, the proposed algorithm searches for triplets of line-support regions that have the highest fitness to the image. By experiments using natural images, we estimate the performance of the proposed algorithm.

In this paper we present a new algorithm to extract and locate partially visible objects from a 2D image. For each model object, the algorithm cuts a prominent fragment front its boundary. Next, using a genetic algorithm, the algorithm finds an image fragment with the best match to the model fragment from match boundary of the image objects. As a result, the corresponding model is extracted and located from the image. The proposed algorithm can be used for the recognition of objects whose scales, rotations and translations are unknown. In addition, the algorithm can locate objects whose boundaries are distorted.

The operation scheduling is an important subtask in the automatic synthesis of digital systems. In this paper we present a neural network model to solve the operation scheduling problem. We also propose an algorithm to lead the network configuration to the global optimum.

In this paper we propose a new algorithm for recognizing 3-D objects from 2-D image. The algorithm takes the multiple view approach in which each 3-D object is modeled by a collection of 2-D projections from various viewing angles where each 2-D projection is called an object model. To select the candidates for the object model that has the best match with the input image, the proposed algorithm computes the surface matching score between the input image and each object model by using Hopfield nets. In addition, the algorithm gives the final matching error between the input image and each candidate model by the error of the pose-transform matrix proposed by Hong et al.[1989] and selects an object model with the smallest matching error as the best matched model.>

This paper studies the problem of scheduling a set of n independent tasks on m identical processors so as to minimize mean weighted flow-time. The problem is known to be NP-complete for $m \geqq 2$ and to be NP-complete in the strong sense for m arbitrary. The worst case behavior of a heuristic algorithm which requires time $O(n\log n)$ is investigated, and it is shown that the mean weighted flow-time obtained by the algorithm does not exceed ${{(\sqrt 2 + 1)} / 2} \cong 1.0207$ times that of an optimal schedule. Moreover the bound ${{(\sqrt 2 + 1)} / 2}$ is best possible.