Homography from two orientation- and scale-covariant features

This paper proposes a geometric interpretation of the angles and scales which the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks. Using these formulas, two new constraints on homography estimation are introduced. Exploiting the derived equations, a solver for estimating the homography from the minimal number of two correspondences is proposed. Also, it is shown how the normalization of the point correspondences affects the rotation and scale parameters, thus achieving numerically stable results. Due to requiring merely two feature pairs, robust estimators, e.g. RANSAC, do significantly fewer iterations than by using the four-point algorithm. When using covariant features, e.g. SIFT, the information about the scale and orientation is given at no cost. The proposed homography estimation method is tested in a synthetic environment and on publicly available real-world datasets

Globally Optimal Relative Pose Estimation with Gravity Prior

Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the $y$-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least-squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions, and ground truth 3D reconstructions

Improvement of clinical and immunological parameters after allergen specific immunotherapy in atopic dermatitis

Background: Allergen immunotherapy (AIT) is considered a curative treatment in some atopic diseases, but in AD contradictory clinical results exist and the action of AIT has not been elucidated. In the literature there is no evidence for parallel investigations of permeability barrier, cutaneous, and blood immune responses after AIT in AD. Objectives: The objective was to investigate immune parameters in the blood and skin and to detect clinical, and barrier changes after AIT in AD. Methods: Mild-to-moderate AD patients (n=14) with concomitant allergic rhinitis to house dust mites were selected. All patients received topical treatment, while eight patients were randomly selected for adjuvant AIT also. At baseline and after 6 months clinical, barrier and immunological investigations (serum and skin tests) were performed. In selected patients, biopsies from atopy patch tests (APT) were analysed by immunohistochemistry for ADrelevant immune cells and mediators. Results: In the adjuvant AIT-group, clinical parameters and barrier functions improved significantly. Blood immune parameters displayed no significant changes. Post-AIT APT became negative in all patients in the AIT-group, but remained positive in the non-AIT group. Cutaneous dendritic cell and T cell recruitment decreased significantly after allergen challenge in the AIT-group, but no significant changes in skin or serum immunoglobulin E levels or prick test (SPT) reactivity were detected. Conclusions: AIT is a beneficial adjuvant treatment for sensitized AD patients. AIT improves not only clinical symptoms, but also permeability barrier functions. The effect of AIT on sensitization should be detected by APT, not by SPT