Cleaning sky survey databases using Hough Transform and Renewal String approaches

Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD. Though relatively small in number these spurious records present a significant problem in many situations where they can become a large proportion of the records potentially of interest to a given astronomer. Accurate and robust techniques are needed for locating and flagging such spurious objects, and we are undertaking a programme investigating the use of machine learning techniques in this context. In this paper we focus on the four most common causes of unwanted records in the SSS: satellite or aeroplane tracks, scratches, fibres and other linear phenomena introduced to the plate, circular halos around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Appropriate techniques are developed for the detection of each of these. The methods are applied to the SSS data to develop a dataset of spurious object detections, along with confidence measures, which can allow these unwanted data to be removed from consideration. These methods are general and can be adapted to other astronomical survey data.Comment: Accepted for MNRAS. 17 pages, latex2e, uses mn2e.bst, mn2e.cls, md706.bbl, shortbold.sty (all included). All figures included here as low resolution jpegs. A version of this paper including the figures can be downloaded from http://www.anc.ed.ac.uk/~amos/publications.html and more details on this project can be found at http://www.anc.ed.ac.uk/~amos/sattrackres.htm

Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

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

畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Research on a modifeied RANSAC and its applications to ellipse detection from a static image and motion detection from active stereo video sequences

制度:新 ; 報告番号:甲3091号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2010/2/24 ; 早大学位記番号:新535

Correspondenceless Structure from Motion

We present a novel approach for the estimation of 3D-motion directly from two images using the Radon transform. The feasibility of any camera motion is computed by integrating over all feature pairs that satisfy the epipolar constraint. This integration is equivalent to taking the inner product of a similarity function on feature pairs with a Dirac function embedding the epipolar constraint. The maxima in this five dimensional motion space will correspond to compatible rigid motions. The main novelty is in the realization that the Radon transform is a filtering operator: If we assume that the similarity and Dirac functions are defined on spheres and the epipolar constraint is a group action of rotations on spheres, then the Radon transform is a correlation integral. We propose a new algorithm to compute this integral from the spherical Fourier transform of the similarity and Dirac functions. Generating the similarity function now becomes a preprocessing step which reduces the complexity of the Radon computation by a factor equal to the number of feature pairs processed. The strength of the algorithm is in avoiding a commitment to correspondences, thus being robust to erroneous feature detection, outliers, and multiple motions

RANSAC for Robotic Applications: A Survey

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.This work has been partially funded by the Basque Government, Spain, under Research Teams Grant number IT1427-22 and under ELKARTEK LANVERSO Grant number KK-2022/00065; the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), the European Regional Development Fund (FEDER), under Grant number PID2021-122402OB-C21 (MCIU/AEI/FEDER, UE); and the Spanish Ministry of Science, Innovation and Universities, under Grant FPU18/04737