Spectral Image Analysis of a natural color sample using Rewritable Transparent Broad-band Filters Kanae Miyazawa (1), Markku Hauta-Kasari (2), and Satoru Toyooka (1) (1) Graduate School of Science and Engineering, Saitama University 255 Shimo-okubo, Urawa, Saitama , JAPAN (2) Department of Computer Science, University of Joensuu, P.O. Box 111, FIN Joensuu, FINLAND Multispectral imaging computer vision, art, environmental monitoring… The aim of this study To acquire a spectral image of a sample from low-dimensional data & under arbitrary illumination

How to obtain the spectral images ex.) by a CCD-camera with narrow-band interference filters 400 nm 410 nm 700 nm 420 nm Filter 1 Filter 2 Filter 3 Filter 4 step2) estimate the original spectral images from the compressed image data step1) obtain the compressed image data by a CCD-camera with a low- dimensional broad-band filter set a large amount of image data must be processed and stored We present Optical Implementation of transparent broad-band filter system which is rewritable arbitrarily Application to two-dimensional spectral image ( Indoor / Outdoor )

Spectral distribution of an object color can be represented by a set of inner products between the low-dimensional color filter set and the spectral distribution of the object. The color filter set we used are non-orthgonal. To estimate a spectrum s, a Pseudoinverse Matrix can be used. s ’=W(W T W) -1 W T s ( W : color filter set, s : Spectral distribution of the object ) W(W T W) -1 ： known, (W T s) ： can be calculated optically Spectral Distribution S’ can be estimated Main idea of this study ~Spectrum Estimation~

ACTIVE TYPE Optimal Light Source Sample CCD camera CCD camera Outdoor Indoor Light Source Optimal Filter Inner product ＝ Sample ＊ Optimal Light Source Inner product ＝ Sample ＊ Optimal Filter How to obtain the Inner product PASSIVE TYPE

Monochrome Image Board Computer Sample Monochro me CCD camera LVF LCSLM White Light Source Stag e Experimental Setup (indoor) Lens

Characteristics of the Linear Variable Filter Transmitting Center Wavelength[nm] Position of the LVF[mm] Size: 60×25×5t [mm] The designed filter patterns corresponding to the color filters are written on the LC-panel. LVF LC-panel Input Level 400nm700nm Input Level Pixel Number One of the Filter Function Spatial Filter Pattern on the LC-panel Spectral position of the transmitting center wavelength

Characteristics of the Linear Variable Filter 60×25×5t [mm] (a) Spectral Position of The Central Wavelength (b) Transmittance of Linear Variable Filter (a)(b)

Wanted Filter Implemented Filter Max Error = 6.4% Mean Error= 5.8% Color Filter Set Light Source Spectrum Wavelength [nm] Filter No. Normalized Intensity Wavelength [nm] Optically Implemented Filters Normalized Intensity 4

Filter No.1Filter No.2Filter No.3Filter No.4 Experiments with Real World Object (indoor) Detected intensity images of the object through the 4 filters.

Estimated Spectra at the Spectral Images (indoor) Blue sheet Green sheet Yellow sheetRed sheet StrawberryKamquat (Orange) The spectra at different locations of the spectral images. Black lines 31 narrow band filters Red lines 4 filters

Spectral Images Converted to RGB-images (indoor) 4 Filters （ proposed system ） 31 Narrow Band Filters (a) (b) (a) RGB-image acquired by the proposed system using 4 filters. (b) RGB-image measured by the CCD-camera with 31 narrow-band filters.

Experimental Setup (outdoor) Monochrome CCD camera Sun Object Standard White (BaSO4) Monochrome Image Board Computer LVF LCSLM Stage Spectroradiometer controller

Filter No.1Filter No.2Filter No.3Filter No.4 Experiments with Real World Object (outdoor) Detected intensity images of the object through the 4 filters.

Black lines 31 narrow band filters Red lines 4 filters Red Blue Orange Yellow Estimated Spectra at the Spectral Images (outdoor)

31 Narrow Band Filters (a) (b) 4 Filters （ proposed system ） Spectral Images Converted to RGB-images (indoor) (a) RGB-image acquired by the proposed system using 4 filters. (b) RGB-image measured by the CCD-camera with 31 narrow-band filters.

1. We proposed the optical transparent broad-band filters, which is rewritable arbitrarily. 2. The spectral distribution of the intensity image through the proposed filter almost coincided with the expected filter functions. 3. Intensity images were detected through the proposed filters, and spectral images were acquired. 4. The estimated spectral images were compared to the spectral images measured by the use of CCD-camera with 31 narrow-band filters. The spectra obtained by the both method correlated well. 5. This system was applied to outdoor measurement under sunlight illumination and the spectral images were estimated. 6. The data obtained from the filtering process is only 4 monochrome images. It is convenient for storing and transmitting the spectral image. Conclusions