Biomedical subjects

Katsushi Ikeuchi

Publications and source records attributed to Katsushi Ikeuchi.

7 recordsLinked to original sources

PubMed2005

Separating reflection components of textured surfaces using a single image.

In inhomogeneous objects, highlights are linear combinations of diffuse and specular reflection components. A number of methods have been proposed to separate or decompose these two components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately, for complex textured images, current color segmentation algorithms are still problematic to segment correctly. Consequently, a method without explicit color segmentation becomes indispensable and this paper presents such a method. The method is based solely on colors, particularly chromaticity, without requiring any geometrical information. One of the basic ideas is to iteratively compare the intensity logarithmic differentiation of an input image and its specular-free image. A specular-free image is an image that has exactly the same geometrical profile as the diffuse component of the input image and that can be generated by shifting each pixel's intensity and maximum chromaticity nonlinearly. Unlike existing methods using a single image, all processes in the proposed method are done locally, involving a maximum of only two neighboring pixels. This local operation is useful for handling textured objects with complex multicolored scenes. Evaluations by comparison with the results of polarizing filters demonstrate the effectiveness of the proposed method.

Algorithms↗

PubMed2004

Three dimensional visualization and comparison of impressions on fired bullets.

Currently, optical devices, such as microscopes and CCD cameras, are utilized for identification of bullets and tool marks in the field of forensic science. While these optical methods are easily manageable and effective, they are under great influence of illumination condition. In other words, appearances of striations through these optical devices have possibility to be changed by lighting condition. Besides these appearance-based approaches, we can utilize three dimensional (3D) geometric data of tool marks that are free from lighting condition. In this study, we focused on 3D geometric data of landmark impressions on fired bullets for identification. We obtained the 3D surface data of tool marks by a confocal microscope and reconstructed virtual impressions on a PC monitor from the geometric data. Furthermore, the 3D data are exploited to numerical matching of two surface shapes. We also visualized the difference of two shapes. In order to do this, two surface models are aligned automatically. In this process, pairings of correspondent points on both surfaces are determined. Distance analysis between these pairs leads to a shape comparison. Since comparison results are visualized, they are intuitive and easily perceptive.

Algorithms↗

PubMed2004

Transparent surface modeling from a pair of polarization images.

We propose a method for measuring surface shapes of transparent objects by using a polarizing filter. Generally, the light reflected from an object is partially polarized. The degree of polarization depends upon the incident angle, which, in turn, depends upon the surface normal. Therefore, we can obtain surface normals of objects by observing the degree of polarization at each surface point. Unfortunately, the correspondence between the degree of polarization and the surface normal is not one to one. Hence, to obtain the correct surface normal, we have to solve the ambiguity problem. In this paper, we introduce a method to solve the ambiguity by comparing the polarization data in two objects, i.e., normal position and tilted with small angle position. We also discuss the geometrical features of the object surface and propose a method for matching two sets of polarization data at identical points on the object surface.

Algorithms↗

PubMed2004

Illumination normalization with time-dependent intrinsic images for video surveillance.

Variation in illumination conditions caused by weather, time of day, etc., makes the task difficult when building video surveillance systems of real world scenes. Especially, cast shadows produce troublesome effects, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. In this paper, we handle such appearance variations by removing shadows in the image sequence. This can be considered as a preprocessing stage which leads to robust video surveillance. To achieve this, we propose a framework based on the idea of intrinsic images. Unlike previous methods of deriving intrinsic images, we derive time-varying reflectance images and corresponding illumination images from a sequence of images instead of assuming a single reflectance image. Using obtained illumination images, we normalize the input image sequence in terms of incident lighting distribution to eliminate shadowing effects. We also propose an illumination normalization scheme which can potentially run in real time, utilizing the illumination eigenspace, which captures the illumination variation due to weather, time of day, etc., and a shadow interpolation method based on shadow hulls. This paper describes the theory of the framework with simulation results and shows its effectiveness with object tracking results on real scene data sets.

Algorithms↗

PubMed2004

Separating reflection components based on chromaticity and noise analysis.

Many algorithms in computer vision assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. To resolve this problem, we present a method to separate the two reflection components. The method is principally based on the distribution of specular and diffuse points in a two-dimensional maximum chromaticity-intensity space. We found that, by utilizing the space and known illumination color, the problem of reflection component separation can be simplified into the problem of identifying diffuse maximum chromaticity. To be able to identify the diffuse maximum chromaticity correctly, an analysis of the noise is required since most real images suffer from it. Unlike existing methods, the proposed method can separate the reflection components robustly for any kind of surface roughness and light direction.

Algorithms↗

PubMed2004

Color constancy through inverse-intensity chromaticity space.

Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces but cannot be applied to highly textured surfaces, since they require precise color segmentation. We present a single integrated method to estimate illumination chromaticity from single-colored and multicolored surfaces. Unlike existing dichromatic-based methods, the proposed method requires only rough highlight regions without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in "inverse-intensity chromaticity space," a novel two-dimensional space that we introduce. In addition, when Hough transform and histogram analysis is utilized in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.

Journal Article↗

PubMed2002

Determining surface orientations of transparent objects based on polarization degrees in visible and infrared wavelengths.

Techniques for modeling an object through observation are very important in object recognition and virtual reality. A wide variety of techniques have been developed for modeling objects with opaque surfaces, whereas less attention has been paid to objects with transparent surfaces. A transparent surface has only surface reflection; it has little body reflection. We present a new method for obtaining surface orientations of transparent surfaces through analysis of the degree of polarization in surface reflection and emission in visible and far-infrared wavelengths, respectively. This parameter, the polarization degree of reflected light at the visible wavelengths, is used for determining the surface orientation at a surface point. The polarization degree at visible wavelengths provides two possible solutions, and the proposed method uses the polarization degree at far-infrared wavelengths to resolve this ambiguity.

Journal Article↗