Kernel-based high-dimensional histogram estimation for visual tracking

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at the 15th IEEE International Conference on Image Processing, October 12–15, 2008, San Diego, California, U.S.A.DOI: 10.1109/ICIP.2008.4711862We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches

Estimating the higher symmetric topological complexity of spheres

We study questions of the following type: Can one assign continuously and $\Sigma_m$-equivariantly to any $m$-tuple of distinct points on the sphere $S^n$ a multipath in $S^n$ spanning these points? A \emph{multipath} is a continuous map of the wedge of $m$ segments to the sphere. This question is connected with the \emph{higher symmetric topological complexity} of spheres, introduced and studied by I. Basabe, J. Gonz\'alez, Yu. B. Rudyak, and D. Tamaki. In all cases we can handle, the answer is negative. Our arguments are in the spirit of the definition of the Hopf invariant of a map $f: S^{2n-1} \to S^n$ by means of the mapping cone and the cup product.Comment: This version has minor corrections compared to what published in AG

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Currently, much of the manual labor needed to generate as-built Building Information Models (BIMs) of existing facilities is spent converting raw Point Cloud Datasets (PCDs) to BIMs descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this paper presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via Singular Value Decomposition (SVD) and Maximum Likelihood Estimation Sample Consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. In order to evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.This is the accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/mice.12063/abstract

X-ray pulsar XTE J1858+034: discovery of the cyclotron line and the revised optical identification

We present results of a detailed investigation of the poorly studied X-ray pulsar XTE J1858+034 based on the data obtained with the NuSTAR observatory during the outburst of the source in 2019. The spectral analysis resulted in the discovery of a cyclotron absorption feature in the source spectrum at ~48 keV both in the pulse phase averaged and resolved spectra. Accurate X-ray localization of the source using the NuSTAR and Chandra observatories allowed us to accurately determine the position of the X-ray source and identify the optical companion of the pulsar. The analysis of the counterpart properties suggested that the system is likely a symbiotic binary hosting an X-ray pulsar and a late type companion star of K-M classes rather than Be X-ray binary as previously suggested.Comment: 12 pages, 12 figures, accepted by Ap

X-Ray Pulsar XTE J1858+034: Discovery of the Cyclotron Line and the Revised Optical Identification

We present the results of a detailed investigation of the poorly studied X-ray pulsar (XRP) XTE J1858+034 based on the data obtained with the NuSTAR observatory during the outburst of the source in 2019. The spectral analysis resulted in the discovery of a cyclotron absorption feature in the source spectrum at similar to ~48 keV in both the pulse phase-averaged and resolved spectra. Accurate X-ray localization of the source using the NuSTAR and Chandra observatories allowed us to accurately determine the position of the X-ray source and identify the optical companion of the pulsar. The analysis of the counterpart properties suggested that the system is likely a symbiotic binary hosting an XRP and a late-type companion star of the K-M classes rather than a Be X-ray binary as previously suggested

Feedback augmentation of pde-based image segmentation algorithms using application-specific exogenous data

This thesis is divided into five chapters. The scope of problems considered is defined in chapter I. Next, chapter II provides background material on image processing with partial differential equations and a review of prior work in the field. Chapter III covers the medical imaging portion of the research; the key contribution is a control-based algorithm for interactive image segmentation. Applications of the feedback-augmented level set method to fracture reconstruction and surgical planning are shown. Problems in vision-based control are considered in Chapters IV and V. A method of improving performance in closed-loop target tracking using level set segmentation is developed, with unmanned aerial vehicle or next-generation missile guidance being the primary applications of interest. Throughout this thesis, the two application types are connected into a unified viewpoint of open-loop systems that are augmented by exogenous data.Ph.D