Browsing by Author "Habib, Ayman"
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- ItemOpen AccessA photogrammetric system for 3d reconstruction of a scoliotic torso(2008) Chang, Yu-Chuan; Habib, Ayman
- ItemOpen AccessAlgorithms for automatic vectorization of scanned maps(2005) Dharmaraj, Girija; Mioc, Darka; Habib, Ayman
- ItemOpen AccessAlternative Methodologies for LiDAR System Calibration(2010) Bang, Ki In; Habib, Ayman
- ItemOpen AccessAlternative methodologies for the quality control of LiDAR systems(2007) Al-Durgham, Mohannad M.; Habib, AymanThe ever improving capabilities of GNSS/INS direct geo-referencing technology is having a positive impact on the widespread adoption of LiDAR systems for the acquisition of dense and accurate surface models over extended areas. Unlike with photogrammetric techniques, derived footprints from a LiDAR system are not based on redundant measurements, which are manipulated in an adjustment procedure. Consequently, there are no associated measures (e.g., variance component of unit weight and variance-covariance matrices of the derived parameters) that can be used to evaluate the quality of the final product. In this regard, a LiDAR system is usually viewed as a black box that lacks a well defined set of quality control procedures. This research introduces alternative procedures for evaluating the internal quality of LiDAR data. However, these procedures could be used for external quality control as well. The main premise of the proposed methodologies is that overlapping LiDAR strips will represent the same surface if and only if there are no biases in the derived surfaces. Therefore, the quality of coincidence between overlapping strips will be used as the basis for deriving the quality control measures.
- ItemOpen AccessAutomated road network extraction from high spatial resolution multi-spectral imagery(2006) Zhang, Qiaoping; Couloigner, Isabelle; Habib, Ayman
- ItemOpen AccessCalibration of Multi-Sensor Laser Scanning Systems(2014-01-06) Hassan, Essam; Habib, AymanOver the past few years, LiDAR or laser scanning systems (airborne, static and terrestrial mobile mapping systems) are considered as well-accepted technologies that can quickly acquire precise 3D point clouds of the terrain surfaces. On the functional level, any mobile system could be defined as an integration of several subsystems such as: Global Positioning System (GPS), an Inertial Navigation System (INS), and the laser scanner. To ensure the geometric quality of the collected point cloud, LiDAR systems should undergo a rigorous calibration procedure. LiDAR system calibration comprises individual sensor calibration (i.e., laser ranging and scanning unit) and mounting parameters calibration (i.e., lever arm offset and boresight angles) relating the system components such as the GPS, INS, and laser scanner. In this research work, a new method for automatic selection of suitable overlapping strip pairs/regions for optimized LiDAR system calibration is introduced. The experimental results have shown that the quality of the estimated parameters using the automatic selection is quite similar to the estimated parameters using the manual selection while the processing time of automatic selection is 3 times faster than the manual selection. In the field of surveying and mapping in recent years, the development of airborne LiDAR systems is characterized by the use of multiple laser scanners for accurate and efficient capture of 3D data along ground and above ground features. In this research, new calibration procedure for dealing with airborne multi-laser scanning systems is presented. The experimental results have shown that accurate estimation of the calibration parameters of each laser scanner can be obtained using the proposed method. Terrestrial mobile laser scanning (TMLS) is the latest approach towards fast and cost-effective acquisition of 3-dimensional spatial data. Accurately evaluating the intrinsic and mounting parameters of TMLS systems is an obvious necessity. However, available systems on the market may lack suitable and efficient practical workflows on how to perform this calibration. This research introduces an innovative method for accurately determining the intrinsic and mounting parameters of multi-TMLS systems. The proposed calibration method investigates a two-step (indoor followed by outdoor) calibration procedure for calibrating terrestrial mobile multi-laser scanning systems. In this research work, a simulation program is developed for generating 3D LiDAR data such as a Velodyne-based Mobile laser scanning system (HDL-32E). The experimental results are performed using a simulated dataset for investigating the one-step and two-step calibration procedures. The experimental results have shown that the estimated parameters using the two-step calibration procedure are better than the estimated parameters derived from the one-step calibration procedure.
- ItemOpen AccessCentroid of Volume: A Surface Topography Measure used to Detect Changes in Adolescent Idiopathic Scoliosis(2016-02-03) Melia, Alexandra; Ronsky, Janet; Joughin, Elaine; Habib, AymanAdolescent Idiopathic Scoliosis (AIS) is a complex three-dimensional (3D) deformity that affects 2-3% of the population. Current methods of diagnosing and monitoring AIS are repeated two-dimensional X-rays every 6-12 months. Repeated radiographs have been shown to increase cancer risk and do not account for 3D changes in the spine. To overcome these drawbacks, the University of Calgary Scoliosis group has developed a non-radiographic imaging technique to analyze 3D deformities using Surface Topography (ST). The overarching objective is to validate a set of ST indices to reliably detect scoliosis. A new ST index, Centroid of Volume (COV), that is a 3D measure of torso balance, is investigated in this thesis. This work found that COV detected differences between healthy and AIS groups and was moderately correlated to the Cobb angle. This index shows promise to detect AIS, which could help advance ST as a clinical tool for monitoring or detecting scoliosis.
- ItemOpen AccessConstrained Motion Estimation for a Multi-Camera System(2015-09-30) Mazaheri Tehrani, Mehdi; Habib, AymanCompared to a single camera, multi-camera systems view a larger field and collect more data for motion estimation and 3D reconstruction. The constant rigid body transformation between the cameras mounted (mounting parameters) is valuable knowledge for outlier detection and estimation of the system motion in real scale. In this research, the mounting parameters of the cameras are estimated in single step through bundle adjustment. Taking advantage of the mounting parameters, a methodology is proposed to estimate the system motion between successive data acquisition epochs. In this method, several estimates for the system rotation are obtained by constraining the relative rotation of the individual cameras. The outliers within the rotation estimates are filtered out and the inliers are averaged. Then, the system translation is estimated by solving a system of linear equations. The experimental results show that the proposed methodology can successfully recover the multi-camera system motion.
- ItemOpen AccessEpipolar resampling of linear array scanner scenes(2004) Morgan, Michel Fawzy; Habib, Ayman
- ItemOpen AccessGeometric Features Extraction and Automated Registration of Static Laser Scans Using Linear Features(2014-08-05) Al-Durgham, Kaleel; Habib, AymanLaser scanning systems are employed in a wide variety of applications, such as digital building model generation, industrial site modeling, cultural heritage documentation, and other civilian and military needs. Depending on the mounting platform used, laser scanners can be classified into two groups: static or kinematic. A static laser scanner usually refers to a terrestrial laser scanner that is mounted on a tripod, while a kinematic laser scanner could be mounted on a fixed-wing plane, helicopter, or ground/terrestrial vehicle. A complete 3D model for a given site typically cannot be derived from a single scan. Therefore, several scans with significant overlap are needed to cover the entire site and also to obtain better information about the site than is possible from a single scan. When a static laser scanner is utilized to collect several scans to achieve complete coverage of a site of interest, the collected scans are referenced to different local frames that are associated with the individual scanner locations. Hence, a registration process must be performed in order to combine the several laser scans. The main goal of the registration process is to estimate the transformation parameters which determine the geometric variations between the reference frames of the collected datasets from different locations. The scale, shifts, and rotation parameters are generally used to describe such variations. The main objective of this thesis is a registration procedure for two overlapping static terrestrial laser scans using linear features that have been automatically extracted and matched in the available scans. The invariant characteristics of linear features in 3D space are utilized for establishing hypothesized matches of linear features between the overlapping scans; hence, the registration is performed in a pairwise scenario. Three alternative matching strategies are proposed to form correspondences between the linear features in overlapping laser scans. Another objective of this research is a segmentation/extraction procedure for low-level geometric features, such as planes,3D lines, and cylinders, from static and mobile laser scanning data, which is conducted through a region-growing segmentation process. The main characteristics of the proposed segmentation procedure are utilization of the proper parameterization models for the features of interest as well as consideration of the noise level and the point density variation within the laser scanning data. Experiments were performed to verify the outcome of the proposed segmentation and registration procedures. The segmentation experiments were conducted on static-terrestrial, mobile-terrestrial, and airborne laser scans, and an existing quality control procedure was used to evaluate the segmentation results. In the registration experiments, the proposed matching strategies were tested with real datasets of static laser scans captured over sites that are rich with linear features.
- ItemOpen AccessImage-based Fine-scale Infrastructure Monitoring(2016) Detchev, Ivan; Habib, Ayman; Lichti, Derek; El-Badry, Mamdouh; El-Sheimy, Naser; Sadeghpour, Farnaz; Sohn, GunhoMonitoring the physical health of civil infrastructure systems is an important task that must be performed frequently in order to ensure their serviceability and sustainability. Additionally, laboratory experiments where individual system components are tested on the fine-scale level provide essential information during the structural design process. This type of inspection, i.e., measurements of deflections and/or cracks, has traditionally been performed with instrumentation that requires access to, or contact with, the structural element being tested; performs deformation measurements in only one dimension or direction; and/or provides no permanent visual record. To avoid the downsides of such instrumentation, this dissertation proposes a remote sensing approach based on a photogrammetric system capable of three-dimensional reconstruction. The proposed system is low-cost, consists of off-the-shelf components, and is capable of reconstructing objects or surfaces with homogeneous texture. The scientific contributions of this research work address the drawbacks in currently existing literature. Methods for in-situ multi-camera system calibration and system stability analysis are proposed in addition to methods for deflection/displacement monitoring, and crack detection and characterization in three dimensions. The mathematical model for the system calibration is based on a single or multiple reference camera(s) and built-in relative orientation constraints where the interior orientation and the mounting parameters for all cameras are explicitly estimated. The methods for system stability analysis can be used to comprehensively check for the cumulative impact of any changes in the system parameters. They also provide a quantitative measure of this impact on the reconstruction process in terms of image space units. Deflection/displacement monitoring of dynamic surfaces in three dimensions is achieved with the system by performing an innovative sinusoidal fitting adjustment. The input data for the adjustment comes from either model-based image fitting or full surface fitting procedures. The crack characterization, i.e., estimation of the average crack width, approximate length and overall orientation, is achieved directly in three dimensions by detecting cracks in a region of interest in a truly-rectified photo via image processing techniques. This hybrid approach combines the use of both geometric and radiometric data, and it performs best in a multi-epoch setting.
- ItemOpen AccessImplementation of a close range photogrammetric system for 3D reconstruction of a scoliotic torso(2010) Detchev, Ivan Denislavov; Habib, Ayman
- ItemOpen AccessIntegration of photogrammetric and LiDAR data for accurate reconstruction and visualization of urban environments(2008) Jarvis, Anna; Habib, Ayman
- ItemOpen AccessIntegration of photogrammetry and LIDAR(2006) Ghanma, Mwafag; Habib, Ayman
- ItemOpen AccessLow-Cost digital cameras: calibration, stability analysis and applications(2005) Pullivelli, Anoop Manohar; Habib, AymanIncreasing resolution and reducing cost of off-the-shelf digital cameras are giving rise to their utilization in traditional and new photogrammetric activities, and allowing amateur users to generate high-quality photogrammetric products. For most, if not all photogrammetric applications, the internal metric characteristics of the implemented camera, customarily known as the Interior Orientation Parameters (IOP), need to be determined and analyzed. The derivation of these parameters is usually achieved by implementing a bundle adjustment with self-calibration procedure. The stability of the IOP is an issue in digital cameras since they are not built with photogrammetric applications in mind. This thesis introduces four quantitative methods for testing camera stability, where the degree of similarity between reconstructed bundles from two sets of IOP is evaluated. The experiments conducted in this research demonstrate the stability of several digital cameras. In addition, the need for different stability analysis measures for different geo-referencing techniques will be demonstrated. Some potential applications of low-cost digital cameras involving 3-D object space reconstruction will also be discussed.
- ItemOpen AccessNon-parametric Spatial-Domain Algorithm for Analysis and Mapping of Urban Scenes Using 3D Point Clouds(2016) Al-Gurrani, Hussein; Habib, Ayman; El-Sheimy, NaserThe world population is expected to grow to nine billion by 2050. With half of the current seven billion people living in urban areas, urbanization most certainly will continue in the future. Such a steep urbanization growth curve requires and justifies the growing interest not only in urban sustainability but also in sustainable development in general, which explains the growing interest as well in spatial information as it can provide sound support for decision-making. Geomatics engineering, which is the core of spatial information collection and processing, provides a multitude of tools for spatial information end-users, such as planners. The research of this dissertation developed a new geomatics-based tool for the urban data collection process, which is the first and most time-consuming step in any urban planning project. Using 3D point clouds obtained using state-of-the-art surveying equipment, which consisted of stationary lasers scanners, a mobile mapping platform, and a UAV, an automatic comprehensive classification algorithm was developed for urban scenes. This algorithm identifies and provides essential information about basic features that can be used in analyzing and understanding a surveyed urban scene, including 1) building locations, dimensions, and other design details; 2) road inventory (e.g., traffic signs, overhanging traffic signals, poles, and powerlines); 3) greenery details (e.g., individual trees, separated canopies, and bushes); and 4) identification of parked cars. The algorithm developed in this dissertation is comprised of four modules to process the input data. In Module 1 conducts ground detection and removal. Then, points clustering and characterization using principal components analysis is accomplished in Module 2. The data are further classified in Module 3 into four main categories: buildings, cars, road inventory, and greenery. Fine classification and analysis of the candidate classes and information extraction is conducted in Module 4, which consists of several subroutines for each process. The new algorithm was evaluated using four different datasets from different types of sensors and different urban sites. The experimental results revealed that the algorithm successfully classified the input datasets into the targeted classes and provided accurate information about the recognized objects.
- ItemOpen AccessObject based integration of photogrammetric and LiDAR data for accurate reconstruction and visualization of building models(2008) Kim, Changjae; Habib, Ayman
- ItemOpen AccessPlanar and Linear Feature-Based Registration of Terrestrial Laser Scans with Minimum Overlap Using Photogrammetric Data(2012-12-06) Canaz, Sibel; Habib, AymanThree-dimensional (3D) modeling is crucial for studying, analyzing, reconstructing, and documenting our environment, in general, and man-made structures, in particular. 3D data for a surveyed structure can be directly collected by a Terrestrial Laser Scanner (TLS). However, several TLS scans are necessary to obtain a complete coverage of the surveyed structures. Transformation of the collected scans into a common coordinate system with a registration procedure is necessary in order to acquire a meaningful 3D model of the structure in question. The registration process requires a large overlap area among the TLS scans for reliable results. In this research, this large overlap area requirement between the TLS scans is reduced using a photogrammetric model as additional information for the registration process. Planar and linear features, which can be easily identified in photogrammetric data and TLS scans, were chosen as the registration primitives. Quantitative quality analysis is proposed in this research by calculating the point-to-plane normal distances between the registered surfaces. The experimental results from real datasets show the ability of the proposed technique, where less than 10 cm point-to-plane normal distances between the registered surfaces were observed, which confirmed the reliability of the registration results.
- ItemOpen AccessPractical In Situ Implementation of a Multicamera Multisystem Calibration(2018-02-07) Detchev, Ivan; Habib, Ayman; Mazaheri, Mehdi; Lichti, DerekConsumer-grade cameras are generally low-cost and available off-the-shelf, so having multicamera photogrammetric systems for 3D reconstruction is both financially feasible and practical. Such systems can be deployed in many different types of applications: infrastructure health monitoring, cultural heritage documentation, bio-medicine, as-built surveys, and indoor or outdoor mobile mapping for example. A geometric system calibration is usually necessary before a data acquisition mission in order for the results to have optimal accuracy. A typical system calibration must address the estimation of both the interior and the exterior, or relative, orientation parameters for each camera in the system. This article reviews different ways of performing a calibration of a photogrammetric system consisting of multiple cameras. It then proposes a methodology for the simultaneous estimation of both the interior and the relative orientation parameters which can work in several different types of scenarios including a multicamera multisystem calibration. A rigorous in situ system calibration was successfully implemented and tested. The same algorithm is able to handle the equivalent to a traditional-style bundle adjustment, that is, a network solution without constraints, for a single or multicamera calibrations, and the proposed bundle adjustment with built-in relative orientation constraints for the calibration of a system or multiple systems of cameras.
- ItemOpen AccessQuality assurance of multi-sensor systems(2011) Kersting, Ana Paula Baungarten; Habib, AymanThe demand for fast and cost-effective geo-spatial data collection along with technological advances in the last few decades have triggered considerable changes in the mapping survey practices. Currently, modern mapping systems consist of multi-sensor systems, typically encompassing navigation sensors and two types of imaging sensors: digital camera (one or multiple) and a laser scanning system. In order to fully attain the potential accuracy of the system sensors and guarantee accurate multi-sensor integration, careful system Quality Assurance (QA) should be carried out. Although several activities are involved in the QA of a multi-sensor system, the system calibration is the crucial activity to ensure the attainment of the expected accuracy and the most complex activity as well. System calibration involves individual sensor calibration and the mounting parameters calibration (i.e., lever arm offset and boresight angles) relating the system components such as the Global Positioning System (GPS), the Inertial Navigation System (INS), and the imaging sensors. In this research work, new calibration procedures for photogramrnetric and Light Detection and Ranging (LiDAR) systems are introduced. The proposed procedures are based on similar point positioning equations and the system parameters are estimated while minimizing flight and control requirements. More specifically, a rigorous analysis, based on the mathematical analysis of the GPS/INSassisted photogrammetric/LiDAR point positioning equation, is carried out to devise the minimum control and flight configuration requirements for the calibration of airborne single-camera photogrammetric and LiDAR systems. The proposed photogrammetric system calibration is a single-step procedure, which can be used for airborne/terrestrial directly geo-referenced single or multi-camera systems. The proposed procedure also has the ability to estimate the Relative Orientation Parameters (ROP) among the cameras in the absence of GPS/INS data. Furthermore, a general model, which allows for the incorporation of prior information about the ROP among the cameras in the calibration process, is devised. From such general model, the previous models ( calibration without prior ROP information and the estimation of the ROP among the cameras) can be derived as special cases. The proposed LiDAR system calibration is an automated procedure and doesn't require specific features (e.g., planes or lines) in the covered area. Suitable primitives, which do not involve pre-processing of the data, are implemented. The correspondence between conjugate primitives is determined using a robust matching procedure. A modification to the Gauss Markov model is introduced to keep the implementation of the calibration procedure simple while utilizing higher order primitives. Experimental results using simulated and real datasets have demonstrated the feasibility/effectiveness of the proposed methodologies for the calibration of photogrammetric and LiDAR systems.