Jussi Parkkinen

Characteristic Spectra of Munsell Colors

The 1257 reflectance spectra of the chips in the Munsell Book of Color—Matte Finish Collection (Munsell Color, Baltimore, Md., 1976) were measured with a rapid acousto-optic spectrophotometer. Measured spectra were sampled from 400 to 700 nm at 5-nm intervals. The correlation matrix of this sample set was formed, and the characteristic vectors of this matrix were computed. It is shown, contradictory to earlier recommendations [ Psychon. Sci.1, 369 ( 1964)], that as many as eight characteristic spectra are needed to achieve good representation for all spectra.

Vector-subspace model for color representation

In multispectral imaging it is advantageous to compress spectral information with a minimum loss of information in a way that permits accurate recovery of the spectrum. By use of the simple vector-subspace model that we propose, spectral information can be stored and recovered by the use of a few inner products, which are easy to measure optically. Two large data sets, the first consisting of 1257 Munsell colors and the other of 218 naturally occurring spectral reflectances, were analyzed to form two bases for the model. The Munsell basis can be used to represent the natural colors, and the basis derived from the natural data can be used to represent the Munsell data. The proposed vector-subspace model includes a simple relation between the inner products and conventional color coordinates. It also provides a way to estimate the spectrum of an object that has known chromaticity coordinates.

Crosstalk between Hsp70 molecular chaperone, lysosomes and proteasomes in autophagy‐mediated proteolysis in human retinal pigment epithelial cells

The pathogenesis of age‐related macular degeneration involves chronic oxidative stress, impaired degradation of membranous discs shed from photoreceptor outer segments and accumulation of lysosomal lipofuscin in retinal pigment epithelial (RPE) cells. It has been estimated that a major part of cellular proteolysis occurs in proteasomes, but the importance of proteasomes and the other proteolytic pathways including autophagy in RPE cells is poorly understood. Prior to proteolysis, heat shock proteins (Hsps), agents that function as molecular chaperones, attempt to refold misfolded proteins and thus prevent the accumulation of cytoplasmic protein aggregates. In the present study, the roles of the Hsp70 molecular chaperone and proteasomal and lysosomal proteolytic pathways were evaluated in human RPE cells (ARPE‐19). The Hsp70 and ubiquitin protein levels and localization were analysed by Western blotting and immunofluorescense. Confocal and transmission electron microscopy were used to detect cellular organelles and to evaluate the morphological changes. Hsp70 levels were modulated using RNA interference and overexpression techniques. Cell viability was measured by colorimetric assay. The proteasome inhibitor MG‐132 evoked the accumulation of perinuclear aggregates positive for Hsp70, ubiquitin‐protein conjugates and the lysosomal membrane protein LAMP‐2. Interestingly, the hsp70 mRNA depletion significantly increased cell death in conjunction with proteasome inhibition. We found that the accumulation of lysosomes was reversible: a cessation of proteasome inhibition led to clearance of the deposits via a mechanism believed to include autophagy. The molecular chaperone Hsp70, proteasomes and autophagy have an important regulatory role in the protein turnover of human RPE cells and may thus open new avenues for understanding degenerative processes in retinal cells.

Spectral vision system for measuring color images

We present a spectral vision system that can be used to measure a color spectrum and two-dimensional spectral images. First, a low-dimensional color filter set was designed by an unsupervised neural network. Then a compact optical setup for the spectral synthesizer was constructed to synthesize the light that corresponds to the spectral characteristics of the color filter. In the optical setup a liquid-crystal spatial light modulator was used to implement color filters. A sample was illuminated by the synthesized lights, and the intensity images that correspond to the inner products between the color filter and the sample were detected by a CCD camera. From the detected inner products the sample’s color spectra were reconstructed by use of a pseudoinverse matrix. Experimental results of measuring a single color spectrum and spectral images are presented.

Compression of multispectral remote sensing images using clustering and spectral reduction

Image compression has been one of the main research topics in the field of image processing for a long time. The research usually focuses on compressing images that are visible to humans. The images being compressed are usually gray-level images or RGB color images. Recent advances in technology, however, enable the authors to make the detailed processing of spectral features in the images. Therefore, the compression of images with many spectral channels, called multispectral images, is required. Many methods used in traditional lossy image compression can be reused also in the compression of multispectral images. In this paper, a new combination of clustering spectra, manipulating spectral vectors, and encoding and decoding for multispectral images is presented. In the manipulation of the spectral vectors PCA, ICA, and wavelets are used. The approach is based on extracting relevant spectral information. Furthermore, some quantitative quality measures for multispectral images are presented.

Detecting texture periodicity from the co-occurrence matrix

Abstract Cooccurrence histograms have been widely used in texture and signal analysis. There have been suggestions on how to find structure and periodicity of the texture from cooccurrence histograms. A statistical property called agreement is here recommended as an indication of periodic structure. It is measured by a κ statistic.

Color features for quality control in ceramic tile industry

We study visual quality control in the ceramics industry. In tile manufacturing, it is important that in each set of tiles, every single tile looks similar. Currently, the estimation is usually done by human vision. Our goal is to design a machine vision system that can estimate the sufficient similarity, or same appearance, to the human eye. Our main approach is to use accurate spectral representation of color, and com- pare spectral features to the RGB color features. A laboratory system for color measurements is built. Experimentations with five classes of brown tiles are presented and discussed. In addition to the k-nearest neighbor (k-NN) classifier, a neural network called the self-organizing map (SOM) is used to provide understanding of the spectral features. Every single spectrum in each tile of a training set is used as input to a 2-D SOM. The SOM is analyzed to understand how spectra are clustered. As a result, tiles are classified using a trained 2-D SOM. It is also of interest to know whether the order of spectral colors can be determined. In our approach, all spectra are clustered in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Finally, the results are compared to experiments with human vision.

Unsupervised filtering of color spectra

We describe a class of unsupervised systems that extract features from databases of reflectance spectra that sample color space in a way that reflects the properties of human color perception. The systems find the internal weight coefficients by optimizing an energy function. We describe several energy functions based on second- and fourth-order statistical moments of the computed output values. We also investigate the effects of imposing boundary conditions on the filter coefficients and the performance of the resulting systems for the databases with the reflectance spectra. The experiments show that the weight matrix for one of the systems is very similar to the eigenvector system, whereas the second type of system tries to rotate the eigenvector system in such a way that the resulting filters partition the spectrum into different bands. We also show how the system can be forced to use weight vectors with positive coefficients. Systems consisting of positive weight vectors are then approximated with Gaussian quadrature methods. In the experimental part of the paper we investigate the properties of three databases consisting of reflectance spectra. We compare the statistical structure of the different databases and investigate how these systems can be used to explore the structure of the space of reflectance spectra.

Edge detection in multispectral images using the self-organizing map

In this paper, two new methods for edge detection in multispectral images are presented. They are based on the use of the self-organizing map (SOM) and a grayscale edge detector. With the 2-dimensional SOM the ordering of pixel vectors is obtained by applying the Peano scan, whereas this can be omitted using the 1-dimensional SOM. It is shown that using the R-ordering based methods some parts of the edges may be missed. However, they can be found using the proposed methods. Using them it is also possible to find edges in images which consist of metameric colors. Finally, it is shown that the proposed methods find the edges properly from real multispectral airplane images The size of the SOM determines the amount of found edges. If the SOM is taught using a large color vector database, the same SOM can be utilized for numerous images.

Simulated Multispectral Imagery for Tree Species Classification Using Support Vector Machines

The information content of remotely sensed data depends primarily on the spatial and spectral properties of the imaging device. This paper focuses on the classification performance of the different spectral features (hyper- and multispectral measurements) with respect to three tree species. The Support Vector Machine was chosen as the classification algorithm for these features. A simulated optical radiation model was constructed to evaluate the identification performance of the given multispectral system for the tree species, and the effects of spectral-band selection and data preprocessing were studied in this setting. Simulations were based on the reflectance measurements of the pine (Pinus sylvestris L.), spruce [ Picea abies (L.) H. Karst.], and birch trees (Betula pubescens Ehrh. and Betula pendula Roth). Leica ADS80 airborne sensor with four spectral bands (channels) was used as a fixed multispectral sensor system that leads to response values for the at-sensor radiance signal. Results suggest that this four-band system has inadequate classification performance for the three tree species. The simulations demonstrate on average a 5-15 percentage points improvement in classification performance when the Leica system is combined with one additional spectral band. It is also demonstrated for the Leica data that feature mapping through a Mahalanobis kernel leads to a 5-10 percentage points improvement in classification performance when compared with other kernels.