Scene Segmentation Driven by Deep Learning and Surface Fitting

This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation

Evidence for a nuclear hexadecapole interaction in the hyperfine spectrum of LiI

The molecular beam electric resonance technique has been used to examine the hyperfine spectrum of 7LiI to determine the nuclear hexadecapole interaction of the iodine nucleus. The nuclear magnetic octupole interaction was also considered but found to be marginally significant. A total of 172 transitions in vibrational states 0-3 and rotational states 1-6 have been included in a fit to determine the iodine nuclear quadrupole, spin-rotation, and hexadecapole interactions, the lithium quadrupole and spin-rotation interactions, and the tensor and scalar parts of the spin-spin interaction. Vibration and rotation dependencies of these constants have been determined