Polygonal Building Segmentation by Frame Field Learning

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.Comment: CVPR 2021 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2021, Pittsburg / Virtual, United State

Visualizing Scissors Congruence

Consider two simple polygons with equal area. The Wallace-Bolyai-Gerwien theorem states that these polygons are scissors congruent, that is, they can be dissected into finitely many congruent polygonal pieces. We present an interactive application that visualizes this constructive proof

Video compression dataset and benchmark of learning-based video-quality metrics

Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ videos compressed using older standards, such as AVC. In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of about 2,500 streams encoded using different standards, including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using crowdsourced pairwise comparisons. The list of evaluated metrics includes recent ones based on machine learning and neural networks. The results demonstrate that new no-reference metrics exhibit a high correlation with subjective quality and approach the capability of top full-reference metrics.Comment: 10 pages, 4 figures, 6 tables, 1 supplementary materia

Developing a Lawyer's Practical Thinking at Higher Education Institution Stage of the Professionalization

The paper considers the particularities of development of a lawyer's practical thinking at the higher education institution stage of professionalization. In order to achieve the set goal, the block of psychological techniques was used, in particular, several scales of R. Cattell's 16-factor personality questionnaire, a questionnaire for diagnosing the practical orientation of thinking, a questionnaire of creative personal capacities etc. The research was conducted with a sample of 282 students of the faculty of law of P.G. Demidov YarSU, with 143 students of the faculty of economics of P.G. Demidov YarSU as the control group (425 people in total). The study performed has shown that the development of qualities of thinking in lawyers at the higher education institution stage of professionalization is distinguished by essential heterochrony. In particular, a significant reduction of most professionally important indices has been found in year 3 of studying at the faculty of law. These indices are restored in years 4 and 5 either in full (intellect) or in part (practical orientation of thinking, development of creative personal capacities). This allows speaking about a crisis in the development of professional thinking of law students in year 3. The consequences of this crisis turn to not be overcome completely by year 5. In the paper, it is supposed that finally the consequences of the crisis found are eliminated as late as during the graduate's direct performing of the professional activity. DOI: 10.5901/mjss.2015.v6n6s5p31

Research of Radial Forces and Torque of Bearingless Synchronous Machine

Bearingless synchronous machine (BSM) is an electrical machine which rotor is suspended by electromagnetic forces (not ball bearings). It allows achieving ultra-high rotation speed and significantly extending area of electric drive application. Nowadays there are different variants of the machines with the structural design and the searching  of optimal solution is going on. The basic calculation parameters of bearingless machines are radial forces that can withstand the rotor from external load and torque produced on the shaft. This article describes the theoretical results based on a computer model that produces the finite element method and experimental study of the BSM prototype

OWL-ontology visualization tool

This paper describes an idea of visual modeling tool for knowledge bases. The tool is planned to be an interactive multimedia application, which intuitively demonstrates complicated ontological structures and makes cognition process more effective and interesting. This work is a part of project concerning the optics ontology development and building an educational portal to introduce it into Optics Museum of ITMO University. The tool visualizes ontology in form of a graph and is planned to be used on tablet PC-s or info-stands installed in the museum