This work addresses challenges related to camera 3D localization while reconstructing a 3D model of an ear. This work explores the potential solution of using a cap, specifically designed not to obstruct the ear, and its efficiency in enhancing the camera localization for structure-from-motion (SfM)-based object reconstruction. The proposed solution is described, and an elaboration of the experimental scenarios used to investigate the background textures is provided; data collection and software tools used in the research are reported. The results show that the proposed method is effective, and using the cap with texture leads to a reduction in the camera localization error. Errors in the 3D location reconstruction of the camera were calculated by comparing cameras localized within typical ear reconstruction situations to those of higher-accuracy reconstructions. The findings also show that caps with sparse dot patterns and a regular knitted patterned winter hat are the preferred patterns. The study provides a contribution to the field of 3D modeling, particularly in the context of creating 3D models of the human ear, and offers a step towards more accurate, reliable, and feasible 3D ear modeling and reconstruction.

The functional MRI (Magnetic Resonance Imaging), fMRI, is today a widespread tool to study and evaluate the brain from a functional point of view. The blood-oxygenation-level-dependent (BOLD) signal is currently used to detect the activation of brain regions with a stimulus application, e.g., visual or auditive. In a block design approach the stimuli (called paradigm in the fMRI scope) are designed to detect activated and non activated brain regions with maximized certainty. However, corrupting noise in MRI volumes acquisition, patient motion and the normal brain activity interference makes this detection a difficult task. The most used activation detection fMRI algorithm, here called SPM-GLM [1] uses a conventional statistical inference methodology based on the t-statisticsIn this paper we propose a new Bayesian approach, by modeling the data acquisition noise as additive white Gaussian noise (AWGN) and the activation indicators as binary unknowns that must be estimated. Monte Carlo tests using both methods have shown that the Bayesian method, here called SPM-MAP, outperforms the traditional one, here called SPM-GLM, for almost all conditions of noise and number of paradigm epochs tested.

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