The quality of wood is of increasing importance in wood industry. One important quality aspect is the average annual
ring width and its standard deviation that is related to the wood strength and stiffness. We present a camera based
measurement system for annual ring measurements. The camera system is designed for outdoor use in forest harvesters. Several challenges arise, such as the quality of cutting process, camera positioning and the light variations. In the freshly cut surface of log end the annual rings are somewhat unclear due to small splinters and saw marks. In the harvester the optical axis of camera cannot be set orthogonally to the log end causing non-constant resolution of the image. The amount of natural light in forest varies from total winter darkness to midsummer brightness. In our approach the image is first geometrically transformed to orthogonal geometry. The annual ring width is measured with two-dimensional power spectra. The two-dimensional power spectra combined with the transformation provide a robust method for estimating the mean and the standard deviation of annual ring width. With laser lighting the variability due to natural lighting can be minimized.
Surveillance camera automation and camera network development are growing areas of interest. This paper
proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS)
when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class
model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account
spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines
visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation.
Image processing is aided by predicting certain advance features of visible terrain. The features include distance
from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The
performance of the approach is studied by comparing a photograph of Finnish forested landscape with the
prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes
An approach for estimating the distribution of a synchronized budding
yeast (<i>Saccharomyces cerevisiae</i>) cell population is discussed. This involves estimation of the phase of the cell cycle for each cell. The approach is based on counting the number of buds of different sizes in budding yeast images. An image processing procedure is presented for the bud-counting task. The procedure employs clustering of the local mean-variance space for segmentation of the images. The subsequent bud-detection step is based on an object separation method which utilizes the chain code representation of objects as well as labeling of connected components. The procedure is tested with microscopic images that were obtained in a time-series experiment of a synchronized budding yeast cell population. The use of the distribution estimate of the cell population for inverse filtering of signals that are obtained in time-series microarray measurements is discussed as well.
In many applications involving measuring a physical phenomenon, the output data contains a mixture of different type of distributions. The data set consists often of unimodal distributions, which overlap, i.e. the ranges of the corresponding random variables have a significant intersection. After observing a multimodal histogram that has several partially overlapping distributions the aim is to separate them by inferring the correct types of the probability density functions (PDFs) and their parameters. The method is based on the non-linear least squares estimation, where several types of PDFs are fitted to the region mostly affected by a single distribution. The possible candidate PDFs are those of the Pearson system, Weibull, Fisher, chi-squared and Rayleigh distributions. This method can be extended to multidimensional cases in certain situations. The methods developed earlier for this task are based for example on the QQ-plot technique and on order statistic filter banks. The found distribution types and their parameters can be applied to different tasks in image processing and system analysis. This algorithm can be used e.g. to the estimation of PDFs of certain phenomena and to global thresholding of images. The method is applied to real two-dimensional data sets having values coming from several distributions.
Two different thermal imagers are tested to find out their system noise properties such as the noise variance, the distribution of the system noise, the effect of the scanning element in the image and the possible uneven distribution of the temperature caused by the optics or other phenomena. The obtained results can be used for comparing the properties of different thermal imagers and in the process of designing optimal image processing algorithms. The system noise estimation is done with three different methods under certain assumptions. These methods are; the use of the two-dimensional autocorrelation-function and the fitted polynomial, the use of suitable high frequencies of the two-dimensional spectrum and the use of stable image series. The first two methods are closely related and can give the noise variance only. The shape of the system noise histogram can be approximated somewhat from the image series under suitable conditions. The variability between the even and the odd lines in image and other, possibly stable phenomena, are also analysed. These methods are first tested with simulated data sets and comparison between the methods is performed. Also real image series from two different cameras are used and conclusions regarding their performance are drawn.
Local entropy estimates can be useful in segmentation of Particle Image Velocimeter (PIV) images. Image intensity combined with local entropy estimates forms basis for bubble detection. The acquired images are corrupted by additive noise with fixed density function. Local entropy estimate of the original image can be extracted from the noisy image if noise distribution is known a priori. A new approach to the problem of local entropy estimation in noisy images is presented. We presume that our original image is corrupted by additive i.i.d. noise from an ergodic source. The noise thus comes from a source that has a fixed density function, which can be approximated by taking histogram over the noise image. Now the histogram of the observed image, can be approximated by convolving the histogram of the original with the noise density (or its approximation). Now, in principle, it is possible extract the histogram of the original image by a blind deconvolution and removing the effect of noise. In many cases, it is also possible to utilize a priori information of the noise process. Local histogram deconvolutions with different window sizes and histogram bin numbers are performed. It is found that with careful implementation the resulting entropy estimates improve the estimates based on noisy image. We expect that the proposed method will prove to be useful with higher dimensional input data. With multidimensional data the number samples grows rapidly with the window size which improves significantly the density estimates.