high performance computing on graphics processing units: hgpu.org

This thesis describes typical spectral imaging techniques and spectral image analysis algorithms that are in general use. Three developed spectral imaging systems are proposed. The first imaging system consists of two line scanning based spectral cameras. These cameras are combined in one simultaneous measuring process, which can be used for capturing a wide range of spectral information that cannot be obtained by a single sensor system. The second imaging system is proposed for heartwood detection in Scots Pine (Pinus sylvestris). The detection is done using fluorescence and this work proposes a prototype system for online measurements using fluorescence imaging. The third spectral imaging system is proposed for medical applications. This system is small and lightweight and has been connected to a medical microscope and used for neurosurgical operations. The system was also used to collect a database of biological tissues and the resulting images have been tested for correct identification of healthy and neoplastic tissues. This thesis proposes the implementation of two statistical methods for spectral image analysis. Implementations are done using Graphical Processing Units (GPUs) and computational speed-up of the analyzing algorithms was compared against ordinary (nonGPU) implementations. The first implementation used Principal Component Analysis (PCA), which produced about 7x speed-up for the total computational efficiency. The second implementation used Non-negative Tensor Factorization (NTF), which produced a 60 – 100x speed-up for the total computational efficiency. The imaging systems and software implementations have been used in research projects that are presented as case studies in the thesis.