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A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.

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This paper presents a new, simple and elegant technique to improve the detection of brain regions with increased neuronal activity in functional magnetic resonance imaging (fMRI). This technique is based on the robust anisotropic diffusion (RAD). A direct application of RAD to fMRI does not work, mainly due to the lack of sharp boundaries between activated and non-activated regions. To overcome this difficulty, we propose to estimate the statistical parametric map (SPM) from the noisy fMRI, compute the diffusion coefficients in the SPM-space, and then perform the diffusion in the structural information-removed fMRI data using the coefficients previously computed. These steps are iterated until the convergence. We have tested the new technique in both simulated and real fMRI, obtaining surprisingly sharp and noiseless SPMs with increased statistical significance. We use Receiver Operating Characteristics (ROC) curves to show that the proposed technique is superior than the conventional correlation method.