• Journal of the Korean Society of Radiology
• /
• v.12 no.7
• /
• pp.929-934
• /
• 2018

Studies for counting and detecting X-rays for the improvement of image quality and material analysis are active. In this work, the detector for X-ray photon counting was designed using Multi-pixel photon counter (MPPC) array and the detector characteristics were evaluated through simulation. Geant4 Application for Tomographic Emission (GATE) was used to obtain the position where the X-ray and the scintillation interacted, and this position was used as the light generation position of DETECT2000. 0.5 mm and 1 mm thick Gadolinium Aluminium Gallium Garnet (GAGG) scintillators were used and the light generated through a $4{\times}4$ array of MPPCs was acquired. The spatial resolution of the designed detector was evaluated by reconstructed image using the light signal acquired for each channel. We obtained images of more than 2 lp/mm in both 0.5 mm and 1 mm thick GAGG scintillation. When this detector is used in a X-ray system, a low-cost system capable of photon counting can be made.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.64-69
• /
• 2008

This paper discusses low resolution face recognition using the photon-counting linear discriminant analysis (LDA). The photon-counting LDA asymptotically realizes the Fisher criterion without dimensionality reduction since it does not suffer from the singularity problem of the fisher LDA. The linear discriminant function for optimal projection is determined in high dimensional space to classify unknown objects, thus, it is more efficient in dealing with low resolution facial images as well as conventional face distortions. The simulation results show that the proposed method is superior to Eigen face and Fisher face in terms of the accuracy and false alarm rates.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.4
• /
• pp.77-84
• /
• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.