Ken Nishino

Selection of optimal combinations of band-pass filters for ice detection by hyperspectral imaging

Hyperspectral imaging captures rich information in spatial and spectral domains but involves high costs and complex data processing. The use of a set of optical band-pass filters (BPFs) in the acquisition of spectral images is proposed for reducing dimensionality of spectral data while maintaining target detection and/or categorization performance. A set of BPFs that could distinguish ice from surrounding water on various materials (e.g., asphalt), was designed as an example. Relatively high accuracy (90.28%) was achieved with only two BPFs, showing the potential of this approach for accurate target detection with lesser complexity than conventional methods.

Optimization of excitation–emission band-pass filter for visualization of viable bacteria distribution on the surface of pork meat

A novel method of optically reducing the dimensionality of an excitation-emission matrix (EEM) by optimizing the excitation and emission band-pass filters was proposed and applied to the visualization of viable bacteria on pork. Filters were designed theoretically using an EEM data set for evaluating colony-forming units on pork samples assuming signal-to-noise ratios of 100, 316, or 1000. These filters were evaluated using newly measured EEM images. The filters designed for S/N = 100 performed the best and allowed the visualization of viable bacteria distributions. The proposed method is expected to be a breakthrough in the application of EEM imaging.

Visualisation of fat and fatty acid distribution in beef using a set of filters based on near infrared spectroscopy

Food quality is strongly affected by its components and their spatial distributions. Recently, spectroscopic methods have been widely applied as a non-destructive and rapid method to measure food quality. Although it is a versatile technique, the measurement system is extremely costly for practical use. In this paper, we propose a simple measurement system using a small set of band-pass filters. A food constituent was predicted using output from the band-pass filters as input for a multiple linear regression model, and the bands were designed to obtain high prediction accuracy characterised by the determination coefficient, using hyperspectral data by the optimisation approach. We designed three sets of filters to separately determine contents such as oleic acid, total unsaturated fatty acid and fat content in raw beef using NIR hyperspectral data, and then we implemented these designs as real optical filters. By mounting the filter in front of the lens of an NIR monochrome camera, we captured a set of filtered images. We then performed a pixel-by-pixel prediction of the content to enable the spatial distribution to be visualised. The determination coefficient (R2) and prediction error, which we characterised by the root mean square error of cross-validation (RMSECV), of this filtering method (R2 = 0.638–0.739, RMSECV = 3.13–5.15) were superior to those obtained with partial least squares (PLS) regression using hyperspectral measurements (R 2 = 0.610–0.643, RMSECV= 3.70–6.12). Our method, therefore, facilitates the application of a hyperspectral technique for practical use.

Optical filter for highlighting spectral features Part I: design and development of the filter for discrimination of human skin with and without an application of cosmetic foundation

Light reflected from an objects surface contains much information about its physical and chemical properties. Changes in the physical properties of an object are barely detectable in spectra. Conventional trichromatic systems, on the other hand, cannot detect most spectral features because spectral information is compressively represented as trichromatic signals forming a three-dimensional subspace. We propose a method for designing a filter that optically modulates a cameras spectral sensitivity to find an alternative subspace highlighting an objects spectral features more effectively than the original trichromatic space. We designed and developed a filter that detects cosmetic foundations on human face. Results confirmed that the filter can visualize and nondestructively inspect the foundation distribution.

Detection and visualization of intracutaneous allergic type-specific elements using long-wavelength near-infrared hyperspectral imaging

This study aimed to develop a method for the assessment of allergic dermatitis by using the long‐wavelength near‐infrared spectrum (more than 1000 nm) to detect intracutaneous allergic type‐specific elements. Such a method was realized by establishing a spectral classifier for the spectra of type I and type IV allergic dermatitis reactions.

Optical filter highlighting spectral features Part II: quantitative measurements of cosmetic foundation and assessment of their spatial distributions under realistic facial conditions

We previously proposed a filter that could detect cosmetic foundations with high discrimination accuracy [Opt. Express 19, 6020 (2011)]. This study extends the filters functionality to the quantification of the amount of foundation and applies the filter for the assessment of spatial distributions of foundation under realistic facial conditions. Human faces that are applied with quantitatively controlled amounts of cosmetic foundations were measured using the filter. A calibration curve between pixel values of the image and the amount of foundation was created. The optical filter was applied to visualize spatial foundation distributions under realistic facial conditions, which clearly indicated areas on the face where foundation remained even after cleansing. Results confirm that the proposed filter could visualize and nondestructively inspect the foundation distributions.