Timo Jääskeläinen

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The arm includes a large number of nerve fibres that transfer information between the central nervous system and the receptors, muscles and glands of the arm. In the nervous system there is continuous traffic. At rest, when only the receptors send information continuously towards the central nervous system, the traffic is not as intensive as during stress,(More)
Multi-spectral images are becoming more common in industrial inspection tasks where the colour is used as a quality measure. In this paper we propose a spectral cooccurrence matrix-based method to analyse multi-spectral texture images, in which every pixel contains a measured colour spectrum. We first quantise the spectral domain of the multi-spectral(More)
The problem of estimating spectral reflectances from the responses of a digital camera has received considerable attention recently. This problem can be cast as a regularized regression problem or as a statistical inversion problem. We discuss some previously suggested estimation methods based on critically undersampled RGB measurements and describe some(More)
We present a new cooccurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is class$ed by the averaged learning subspace method (ALSM). In the spatial domain we use a generalized(More)
Determination of blood glucose level is a frequently occurring procedure in diabetes care. As the most common method involves collecting blood drops for chemical analysis, it is invasive and liable to afflict a degree of pain and cause a skin injury. To eliminate these disadvantages, this thesis focuses on pulsed photoacoustic techniques, which have(More)
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 (hyperand multispectral measurements) with respect to three tree species. The Support Vector Machine was chosen as the classification algorithm(More)
The neural-network model based on the theory proposed by Wilson and Cowan has been simulated by using digitized real images. Mathematically, the model is based on coupled nonlinear differential equations that describe the functional dynamics of cortical nervous tissue, and the model can operate in different dynamical modes, depending on coupling strengths.(More)