The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10−7). A linear SVR age predictor performed reasonably well in continuous age prediction (R2 = 0.419, p-value < 1 × 10−8). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.

New cameras such as the Canon EOS 7D and Pointgrey Grasshopper have 14-bit sensors. We present a theoretical analysis and a practical approach that exploit these new cameras with high-resolution quantization for reliable HDR imaging from a moving camera. Specifically, we propose a unified probabilistic formulation that allows us to analytically compare two HDR imaging alternatives: (1) deblurring a single blurry but clean image and (2) denoising a sequence of sharp but noisy images. By analyzing the uncertainty in the estimation of the HDR image, we conclude that multi-image denoising offers a more reliable solution. Our theoretical analysis assumes translational motion and spatially-invariant blur. For practice, we propose an approach that combines optical flow and image denoising algorithms for HDR imaging, which enables capturing sharp HDR images using handheld cameras for complex scenes with large depth variation. Quantitative evaluation on both synthetic and real images is presented.