La liberta´religiosa e la liberta´della scuola

Tbis article tries to demonstrate that freedom of education is an essential part of religious freedom, in agreement witb the conception and exposition found in the Council Declaration on Religious Liberty, whicb refers to it. This is done in successive phases: by placing freedom of education, explicidy referred to in Point 5 of the Council Declaration on Religious Liberty, within the global context of the Declaration¡ by showing the links tbat exist between freedom of education and religious liberty on a purely natural plane¡ finally, by demonstrating the necessary connections that exist between freedom of education and religious freedom on an actual supernatural plane

Is there anything new to say about SIFT matching?

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. The paper then undertakes a comprehensive experimental evaluation of state-of-the-art hand-crafted and data-driven descriptors, also including the most recent deep descriptors. Comparisons are carried out according to several performance parameters, among which accuracy and space-time efficiency. Results are provided for both planar and non-planar scenes, the latter being evaluated with a new benchmark based on the concept of approximated patch overlap. Experimental evidence shows that, despite their age, SIFT and other hand-crafted descriptors, once enhanced through the proposed strategies, are ready to meet the future image matching challenges. We also believe that the lessons learned from this work will inspire the design of better hand-crafted and data-driven descriptors