Toshio Uchiyama

Bio: Toshio Uchiyama is an academic researcher from NTT DATA. The author has contributed to research in topics: Multispectral image & Color histogram. The author has an hindex of 4, co-authored 6 publications receiving 185 citations.

Color image reproduction based on multispectral and multiprimary imaging: experimental evaluation

Abstract: Multispectral imaging is significant technology for the acquisition and display of accurate color information. Natural color reproduction under arbitrary illumination becomes possible using spectral information of both image and illumination light. In addition, multiprimary color display, i.e., using more than three primary colors, has been also developed for the reproduction of expanded color gamut, and for discounting observer metamerism. In this paper, we present the concept for the multispectral data interchange for natural color reproduction, and the experimental results using 16-band multispectral camera and 6-primary color display. In the experiment, the accuracy of color reproduction is evaluated in CIE (Delta) Ea*b* for both image capture and display systems. The average and maximum (Delta) Ea*b* = 1.0 and 2.1 in 16-band mutispectral camera system, using Macbeth 24 color patches. In the six-primary color projection display, average and maximum (Delta) Ea*b* = 1.3 and 2.7 with 30 test colors inside the display gamut. Moreover, the color reproduction results with different spectral distributions but same CIE tristimulus value are visually compared, and it is confirmed that the 6-primary display gives improved agreement between the original and reproduced colors.

Development of a 16-band multispectral image archiving system

Abstract: To preserve museum collections of works of art, these collections are often photographed for display in digital museums. However, conventional photography cannot capture spectral characteristics of objects. In this paper, we describe a 16-band camera system designed to produce spectral images of ancient paintings. Results of color reproduction of captured images and results of spectral analysis of images of ancient paintings are also presented. The camera consists of a 2000×2000-pixel CCD, a rotational filter turret with 16 interference filters, and a PC-based image capturing and displaying unit. The camera's lens is interchangeable, and it enables two or more different view sizes. Each band image of the camera can be focused independently, and it reduces longitudinal chromatic aberration. A stroboscope is used for lighting, and the rotational filter turret and electrical shutter of the CCD have been synchronized with it. An electric motor-driven photographic platform is used to enable photographing large objects in several shots. We evaluated the results of color estimation for an image taken by this camera using the GretagMacbeth ColorChecker 24-color chart. The average ΔEab was 2.09 (maximum ΔEab was 4.03). Spectral reflectance were used to analyze a degraded area on an ancient painting.

Multispectral image retrieval using vector quantization

Abstract: A novel method for multispectral image retrieval is presented. This method uses a representation of image features based on vector quantization. Feature representation is important for image retrieval, but there are difficulties in applying conventional histogram-based representations to multispectral images. We developed an efficient feature representation and a novel method for the retrieval of images by quantizing each image adaptively.

Optimum structure learning algorithms for competitive learning neural network

Abstract: A competitive learning neural network (CLNN) has a mechanism to discover statistically distinctive features included in input population. Competitive learning is different from a classification paradigm that needs a supervisor. Therefore, the unknown features are expected to be extracted from the visual image. However, CLNN has a problem of a serious decline of learning ability from the lack of competition. The reason for this is that the units of CLNN are not allocated to adapt to the distribution of input vectors in the feature space. We propose learning algorithms to optimize the positions of units and attain valid competition. These learning algorithms are based on structure learning according to two ideas. The first idea is that many units should be allocated according to concentrations of input vectors in the feature space. The second idea is that at least one unit should exist within an appropriate distance form every input vector. We apply the proposed algorithms to CLNN and experiment on the distinction of different binary 64 X 64 dot patterns. This patterns explores the validity of the two algorithms for CLNN.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Multispectral Chiral Imaging with a Metalens.

Abstract: The vast majority of biologically active compounds, ranging from amino acids to essential nutrients such as glucose, possess intrinsic handedness. This in turn gives rise to chiral optical properties that provide a basis for detecting and quantifying enantio-specific concentrations of these molecules. However, traditional chiroptical spectroscopy and imaging techniques require cascading of multiple optical components in sophisticated setups. Here, we present a planar lens with an engineered dispersive response, which simultaneously forms two images with opposite helicity of an object within the same field-of-view. In this way, chiroptical properties can be probed across the visible spectrum using only the lens and a camera without the addition of polarizers or dispersive optical devices. We map the circular dichroism of the exoskeleton of a chiral beetle, Chrysina gloriosa, which is known to exhibit high reflectivity of left-circularly polarized light, with high spatial resolution limited by the numerical...

Interactive Learning Using a “Society of Models”

Abstract: Digital library access is driven by features, but features are often context-dependent and noisy, and their relevance for a query is not always obvious. This paper describes an approach for utilizing many data-dependent, user-dependent, and task-dependent features in a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized and context-dependent features. The selection process is guided by a rich example-based interaction with the user. The inherent combinatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. The stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. The weighting stage adapts the collection stage’s search space across uses, so that, in later interactions, good groupings are found given few examples from the user. Described is an interactive-time implementation of this architecture for semi-automatic within-image segmentation and across-image labeling, driven by concurrently active color models, texture models, or manually-provided groupings.

Color image segmentation using competitive learning

Abstract: Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results. >