Yukiko Kenmochi

Estimation of eyeglassless facial images using principal component analysis

For facial image analysis, facial parts such as eyes, nose, and mouth are generally focused and used. When these facial parts are hindered by additional objects (eyeglasses, beard, injury, etc.), the feature extraction from facial image will not be accurate. In this paper, we focus on the eyeglass faces because they account for 40% of population in Japan, and present a method of the removal of eyeglass frame in facial images with eyeglasses using principal component analysts. Two approaches are discussed for removal of eyeglasses in facial image. The first method calculates basis vectors from many eyeglassless facial images and one eyeglass facial image, and reconstructs the facial image with the basis vectors which include no feature of eyeglass frame. The second method calculates basis vectors from a set of eyeglassless facial images, and reconstructs the facial image using the values of inner product of the basis vectors and an eyeglass facial image. The former obtains the images which restrain. The features of eyeglass frame while loses the facial individuality a little. The latter obtains a natural eyeglassless facial image.

Marching cubes method with connectivity

In this paper, we solve the topological problem of isosurfaces generated by the marching cubes method using the approach of combinatorial topology. For each marching cube, we examine the connectivity of polyhedral configuration in the sense of combinatorial topology. For the cubes where the connectivities are not considered, we modify the polyhedral configurations with the connectivity and construct polyhedral isosurfaces with the correct topologies.

Boundary Extraction of Discrete Objects

One of the aims in the field of computer vision is to acquire information about the geometric and topological properties of objects in a three-dimensional world. First, we measure the objects and then we convert the measured data into geometric and topological properties of the objects. As an intermediate between the measured data and the geometric and topological properties, a representation of the objects for computers is desired. In this paper, we propose a representation of objects and their boundaries, which is based on combinatorial topology, and develop a method of extracting boundaries of objects from measured data. It is sufficient to extract boundaries, because they include information about the shape of the objects; the internal structure of the objects is not necessary for information about the shape. In addition, we prove that boundaries are uniquely obtained using our algorithm.

Surface area estimation for digitized regular solids

The problem of multigrid convergent surface area measurement came with the advent of computer-based image analysis. The paper proposes a classification scheme of local and global polyhedrization approaches which allows us to classify different surface area measurement techniques with respect to the underlying polyhedrization scheme. It is shown that a local polyhedrization technique such as marching cubes is not multigrid convergent towards the true value even for elementary convex regular solids such as cubes, spheres or cylinders. The paper summarizes work on global polyhedrization techniques with experimental results pointing towards correct multigrid convergence. The class of general ellipsoids is suggested to be a test set for such multigrid convergence studies.

Facial individuality and expression analysis by eigenspace method based on class features or multiple discriminant analysis

This paper presents two methods for the analysis of facial individuality and expression; an eigenspace method based on class features (EMC) and multiple discriminant analysis (MDA). Those methods are used since they derive eigenvectors by which we may extract facial individuality or expression information from a given facial image. The facial individuality and expression analysis can be achieved by projecting the facial image onto the subspace spanned by a set of those eigenvectors. We apply EMC and MDA to the classification of facial images into 50 classes of individuals or into seven classes of facial expressions, and verify their effectiveness with some experimental results.

A hierarchical image segmentation algorithm based on an observation scale

Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenszwalb and Huttenlocher. Quantitative and qualitative assessments of the method on Berkeley image database shows efficiency, ease of use and robustness of our method.

Polyhedra generation from lattice points

This paper focuses on a method for generating polyhedra from a set of lattice points, such as three-dimensional (3D) medical computerized tomography images. The method is based on combinatorial topology [1] and algebraic properties of the 3D lattice space [2]. It is shown that the method can uniquely generate polyhedra from a subset of the lattice space independently of the choice of neighborhood. Furthermore, a practical algorithm is developed and experimental results using 3D medical imagery are presented.

Combinatorial structure of rigid transformations in 2D digital images

Rigid transformations are involved in a wide range of digital image processing applications. When applied on discrete images, rigid transformations are usually performed in their associated continuous space, requiring a subsequent digitization of the result. In this article, we propose to study rigid transformations of digital images as fully discrete processes. In particular, we investigate a combinatorial structure modelling the whole space of digital rigid transformations on arbitrary subset of Z^2 of size NxN. We describe this combinatorial structure, which presents a space complexity O(N^9) and we propose an algorithm enabling to construct it in linear time with respect to its space complexity. This algorithm, which handles real (i.e., non-rational) values related to the continuous transformations associated to the discrete ones, is however defined in a fully discrete form, leading to exact computation.

Discrete combinatorial geometry

Abstract In computer vision, one of the ultimate aims is the determination of geomettric properties of 3-dimensional objects in our real world from measured data. As an expression intermediate between measured raw data and geometric properties, we need a method of representing objects in computers. For the object representations, geometry which uses only finite-precision numbers is necessary because in computers we can only manipulate finite-precision numbers. In this paper, we develop a new geometry, which we call discrete combinatorial geometry due to the discreteness of the space of finite-precision numbers, applying fundamental definitions of classical combinatorial geometry. Using discrete combinatorial geometry, we introduce a new method for representing curves, surfaces and objects in computers. We also show that our new representation is based on the fact that the boundary of a surface consists of curves and the boundary of an object consists of surfaces.

Optimal Consensus set for digital line and plane fitting

This article presents a new method for fitting a digital line or plane to a given set of points in a 2D or 3D image in the presence of noise by maximizing the number of inliers, namely the consensus set. By using a digital model instead of a continuous one, we show that we can generate all possible consensus sets for model fitting. We present a deterministic algorithm that efficiently searches the optimal solution with time complexity O(Nd log N) for dimension d, where d = 2,3, together with space complexity O(N) where N is the number of points.