A Markov Chain Monte Carlo approach for measurement of jet precession in radio-loud active galactic nuclei

© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Jet precession can reveal the presence of binary systems of supermassive black holes. The ability to accurately measure the parameters of jet precession from radio-loud AGN is important for constraining the binary supermassive black hole population, which are expected as a result of hierarchical galaxy evolution. The age, morphology, and orientation along the line of sight of a given source often result in uncertainties regarding jet path. This paper presents a new approach for efficient determination of precession parameters using a 2D MCMC curve-fitting algorithm which provides us a full posterior probability distribution on the fitted parameters. Applying the method to Cygnus A, we find evidence for previous suggestions that the source is precessing. Interpreted in the context of binary black holes leads to a constraint of parsec scale and likely sub-parsec orbital separation for the putative supermassive binary.Peer reviewe

Corner detector based on global and local curvature properties

This paper proposes a curvature-based corner detector that detects both fine and coarse features accurately at low computational cost. First, it extracts contours from a Canny edge map. Second, it computes the absolute value of curvature of each point on a contour at a low scale and regards local maxima of absolute curvature as initial corner candidates. Third, it uses an adaptive curvature threshold to remove round corners from the initial list. Finally, false corners due to quantization noise and trivial details are eliminated by evaluating the angles of corner candidates in a dynamic region of support. The proposed detector was compared with popular corner detectors on planar curves and gray-level images, respectively, in a subjective manner as well as with a feature correspondence test. Results reveal that the proposed detector performs extremely well in both fields. © 2008 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

A heuristical method of corner points detection on an image boundary

A heuristics-based method of corner points detection on an image boundary is proposed and implemented. The method uses the sampled boundary distances to find all of the candidate corner points along the image boundary. Then the curvature characteristical value is used to measure the severity of curvature change of the candidate points. Those candidates whose curvature characteristical value is under some threshold are eliminated. The paper also proposed some mechanism to reduce the effect of noises on the boundary. Experiments show that it is an efficient method and it gives satisfactory results on some image boundaries

Faster and better: a machine learning approach to corner detection

The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and very high quality.Comment: 35 pages, 11 figure

Edge and corner identification for tracking the line of sight

This article presents an edge-corner detector, implemented in the realm of the GEIST project (an Computer Aided Touristic Information System) to extract the information of straight edges and their intersections (image corners) from camera-captured (real world) and computer-generated images (from the database of Historical Monuments, using observer position and orientation data) -- Camera and computer-generated images are processed for reduction of detail, skeletonization and corner-edge detection -- The corners surviving the detection and skeletonization process from both images are treated as landmarks and fed to a matching algorithm, which estimates the sampling errors which usually contaminate GPS and pose tracking data (fed to the computer-image generatator) -- In this manner, a closed loop control is implemented, by which the system converges to exact determination of position and orientation of an observer traversing a historical scenario (in this case the city of Heidelberg) -- With this exact position and orientation, in the GEIST project other modules are able to project history tales on the view field of the observer, which have the exact intended scenario (the real image seen by the observer) -- In this way, the tourist “sees” tales developing in actual, material historical sites of the city -- To achieve these goals this article presents the modification and articulation of algorithms such as the Canny Edge Detector, SUSAN Corner Detector, 1-D and 2-D filters, etceter