Neuromorphic Event-Based 3D Pose Estimation
Reverter Valeiras, David; Orchard, Garrick; Ieng, Sio-Hoi; Benosman, Ryad B.
2016-01-01
Pose estimation is a fundamental step in many artificial vision tasks. It consists of estimating the 3D pose of an object with respect to a camera from the object's 2D projection. Current state of the art implementations operate on images. These implementations are computationally expensive, especially for real-time applications. Scenes with fast dynamics exceeding 30–60 Hz can rarely be processed in real-time using conventional hardware. This paper presents a new method for event-based 3D object pose estimation, making full use of the high temporal resolution (1 μs) of asynchronous visual events output from a single neuromorphic camera. Given an initial estimate of the pose, each incoming event is used to update the pose by combining both 3D and 2D criteria. We show that the asynchronous high temporal resolution of the neuromorphic camera allows us to solve the problem in an incremental manner, achieving real-time performance at an update rate of several hundreds kHz on a conventional laptop. We show that the high temporal resolution of neuromorphic cameras is a key feature for performing accurate pose estimation. Experiments are provided showing the performance of the algorithm on real data, including fast moving objects, occlusions, and cases where the neuromorphic camera and the object are both in motion. PMID:26834547
SIFT algorithm-based 3D pose estimation of femur.
Zhang, Xuehe; Zhu, Yanhe; Li, Changle; Zhao, Jie; Li, Ge
2014-01-01
To address the lack of 3D space information in the digital radiography of a patient femur, a pose estimation method based on 2D-3D rigid registration is proposed in this study. The method uses two digital radiography images to realize the preoperative 3D visualization of a fractured femur. Compared with the pure Digital Radiography or Computed Tomography imaging diagnostic methods, the proposed method has the advantages of low cost, high precision, and minimal harmful radiation. First, stable matching point pairs in the frontal and lateral images of the patient femur and the universal femur are obtained by using the Scale Invariant Feature Transform method. Then, the 3D pose estimation registration parameters of the femur are calculated by using the Iterative Closest Point (ICP) algorithm. Finally, based on the deviation between the six degrees freedom parameter calculated by the proposed method, preset posture parameters are calculated to evaluate registration accuracy. After registration, the rotation error is less than l.5°, and the translation error is less than 1.2 mm, which indicate that the proposed method has high precision and robustness. The proposed method provides 3D image information for effective preoperative orthopedic diagnosis and surgery planning. PMID:25226990
Viewpoint Invariant Gesture Recognition and 3D Hand Pose Estimation Using RGB-D
ERIC Educational Resources Information Center
Doliotis, Paul
2013-01-01
The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…
Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors
Ge, Song; Fan, Guoliang
2015-01-01
We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registration techniques that have a complementary nature to cope with non-rigid and articulated deformations of the human body under a variety of poses. This unique combination allows us to handle point sets of complex body motion and large pose variation without any initial conditions, as required by most existing approaches. Second, we introduce an efficient pose tracking strategy to deal with sequential depth data, where the major challenge is the incomplete data due to self-occlusions and view changes. We introduce a visible point extraction method to initialize a new template for the current frame from the previous frame, which effectively reduces the ambiguity and uncertainty during registration. Third, to support robust and stable pose tracking, we develop a segment volume validation technique to detect tracking failures and to re-initialize pose registration if needed. The experimental results on both benchmark 3D laser scan and depth datasets demonstrate the effectiveness of the proposed framework when compared with state-of-the-art algorithms. PMID:26131673
Head pose estimation from a 2D face image using 3D face morphing with depth parameters.
Kong, Seong G; Mbouna, Ralph Oyini
2015-06-01
This paper presents estimation of head pose angles from a single 2D face image using a 3D face model morphed from a reference face model. A reference model refers to a 3D face of a person of the same ethnicity and gender as the query subject. The proposed scheme minimizes the disparity between the two sets of prominent facial features on the query face image and the corresponding points on the 3D face model to estimate the head pose angles. The 3D face model used is morphed from a reference model to be more specific to the query face in terms of the depth error at the feature points. The morphing process produces a 3D face model more specific to the query image when multiple 2D face images of the query subject are available for training. The proposed morphing process is computationally efficient since the depth of a 3D face model is adjusted by a scalar depth parameter at feature points. Optimal depth parameters are found by minimizing the disparity between the 2D features of the query face image and the corresponding features on the morphed 3D model projected onto 2D space. The proposed head pose estimation technique was evaluated on two benchmarking databases: 1) the USF Human-ID database for depth estimation and 2) the Pointing'04 database for head pose estimation. Experiment results demonstrate that head pose estimation errors in nodding and shaking angles are as low as 7.93° and 4.65° on average for a single 2D input face image. PMID:25706638
NASA Astrophysics Data System (ADS)
Li-Chee-Ming, J.; Armenakis, C.
2016-06-01
This paper presents a novel application of the Visual Servoing Platform's (ViSP) for pose estimation in indoor and GPS-denied outdoor environments. Our proposed solution integrates the trajectory solution from RGBD-SLAM into ViSP's pose estimation process. Li-Chee-Ming and Armenakis (2015) explored the application of ViSP in mapping large outdoor environments, and tracking larger objects (i.e., building models). Their experiments revealed that tracking was often lost due to a lack of model features in the camera's field of view, and also because of rapid camera motion. Further, the pose estimate was often biased due to incorrect feature matches. This work proposes a solution to improve ViSP's pose estimation performance, aiming specifically to reduce the frequency of tracking losses and reduce the biases present in the pose estimate. This paper explores the integration of ViSP with RGB-D SLAM. We discuss the performance of the combined tracker in mapping indoor environments and tracking 3D wireframe indoor building models, and present preliminary results from our experiments.
System for conveyor belt part picking using structured light and 3D pose estimation
NASA Astrophysics Data System (ADS)
Thielemann, J.; Skotheim, Ø.; Nygaard, J. O.; Vollset, T.
2009-01-01
Automatic picking of parts is an important challenge to solve within factory automation, because it can remove tedious manual work and save labor costs. One such application involves parts that arrive with random position and orientation on a conveyor belt. The parts should be picked off the conveyor belt and placed systematically into bins. We describe a system that consists of a structured light instrument for capturing 3D data and robust methods for aligning an input 3D template with a 3D image of the scene. The method uses general and robust pre-processing steps based on geometric primitives that allow the well-known Iterative Closest Point algorithm to converge quickly and robustly to the correct solution. The method has been demonstrated for localization of car parts with random position and orientation. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm
NASA Astrophysics Data System (ADS)
Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne
2010-02-01
Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
ILIAD Testing; and a Kalman Filter for 3-D Pose Estimation
NASA Technical Reports Server (NTRS)
Richardson, A. O.
1996-01-01
This report presents the results of a two-part project. The first part presents results of performance assessment tests on an Internet Library Information Assembly Data Base (ILIAD). It was found that ILLAD performed best when queries were short (one-to-three keywords), and were made up of rare, unambiguous words. In such cases as many as 64% of the typically 25 returned documents were found to be relevant. It was also found that a query format that was not so rigid with respect to spelling errors and punctuation marks would be more user-friendly. The second part of the report shows the design of a Kalman Filter for estimating motion parameters of a three dimensional object from sequences of noisy data derived from two-dimensional pictures. Given six measured deviation values represendng X, Y, Z, pitch, yaw, and roll, twelve parameters were estimated comprising the six deviations and their time rate of change. Values for the state transiton matrix, the observation matrix, the system noise covariance matrix, and the observation noise covariance matrix were determined. A simple way of initilizing the error covariance matrix was pointed out.
Nosrati, Masoud S; Abugharbieh, Rafeef; Peyrat, Jean-Marc; Abinahed, Julien; Al-Alao, Osama; Al-Ansari, Abdulla; Hamarneh, Ghassan
2016-01-01
In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views. In this paper, we propose a multi-modal approach to segmentation where preoperative 3D computed tomography scans and intraoperative stereo-endoscopic video data are jointly analyzed. The idea is to segment multiple poorly visible structures in the stereo/multichannel endoscopic videos by fusing reliable prior knowledge captured from the preoperative 3D scans. More specifically, we estimate and track the pose of the preoperative models in 3D and consider the models' non-rigid deformations to match with corresponding visual cues in multi-channel endoscopic video and segment the objects of interest. Further, contrary to most augmented reality frameworks in endoscopic surgery that assume known camera parameters, an assumption that is often violated during surgery due to non-optimal camera calibration and changes in camera focus/zoom, our method embeds these parameters into the optimization hence correcting the calibration parameters within the segmentation process. We evaluate our technique on synthetic data, ex vivo lamb kidney datasets, and in vivo clinical partial nephrectomy surgery with results demonstrating high accuracy and robustness. PMID:26151933
NASA Astrophysics Data System (ADS)
Munkelt, C.; Kleiner, B.; Thorhallsson, T.; Mendoza, C.; Bräuer-Burchardt, C.; Kühmstedt, P.; Notni, G.
2013-05-01
Portable 3D scanners with low measurement uncertainty are ideally suited for capturing the 3D shape of objects right in their natural environment. However, elaborate manual post processing was usually necessary to build a complete 3D model from several overlapping scans (multiple views), or expensive or complex additional hardware (like trackers etc.) was needed. On the contrary, the NavOScan project[1] aims at fully automatic multi-view 3D scan assembly through a Navigation Unit attached to the scanner. This light weight device combines an optical tracking system with an inertial measurement unit (IMU) for robust relative scanner position estimation. The IMU provides robustness against swift scanner movements during view changes, while the wide angle, high dynamic range (HDR) optical tracker focused on the measurement object and its background ensures accurate sensor position estimations. The underlying software framework, partly implemented in hardware (FPGA) for performance reasons, fusions both data streams in real time and estimates the navigation unit's current pose. Using this pose to calculate the starting solution of the Iterative Closest Point registration approach allows for automatic registration of multiple 3D scans. After finishing the individual scans required to fully acquire the object in question, the operator is readily presented with its finalized complete 3D model! The paper presents an overview over the NavOScan architecture, highlights key aspects of the registration and navigation pipeline and shows several measurement examples obtained with the Navigation Unit attached to a hand held structured-light 3D scanner.
3-D Pose Presentation for Training Applications
ERIC Educational Resources Information Center
Fox, Kaitlyn; Whitehead, Anthony
2011-01-01
Purpose: In the authors' experience, the biggest issue with pose-based exergames is the difficulty in effectively communicating a three-dimensional pose to a user to facilitate a thorough understanding for accurate pose replication. The purpose of this paper is to examine options for pose presentation. Design/methodology/approach: The authors…
3D sensor algorithms for spacecraft pose determination
NASA Astrophysics Data System (ADS)
Trenkle, John M.; Tchoryk, Peter, Jr.; Ritter, Greg A.; Pavlich, Jane C.; Hickerson, Aaron S.
2006-05-01
Researchers at the Michigan Aerospace Corporation have developed accurate and robust 3-D algorithms for pose determination (position and orientation) of satellites as part of an on-going effort supporting autonomous rendezvous, docking and space situational awareness activities. 3-D range data from a LAser Detection And Ranging (LADAR) sensor is the expected input; however, the approach is unique in that the algorithms are designed to be sensor independent. Parameterized inputs allow the algorithms to be readily adapted to any sensor of opportunity. The cornerstone of our approach is the ability to simulate realistic range data that may be tailored to the specifications of any sensor. We were able to modify an open-source raytracing package to produce point cloud information from which high-fidelity simulated range images are generated. The assumptions made in our experimentation are as follows: 1) we have access to a CAD model of the target including information about the surface scattering and reflection characteristics of the components; 2) the satellite of interest may appear at any 3-D attitude; 3) the target is not necessarily rigid, but does have a limited number of configurations; and, 4) the target is not obscured in any way and is the only object in the field of view of the sensor. Our pose estimation approach then involves rendering a large number of exemplars (100k to 5M), extracting 2-D (silhouette- and projection-based) and 3-D (surface-based) features, and then training ensembles of decision trees to predict: a) the 4-D regions on a unit hypersphere into which the unit quaternion that represents the vehicle [Q X, Q Y, Q Z, Q W] is pointing, and, b) the components of that unit quaternion. Results have been quite promising and the tools and simulation environment developed for this application may also be applied to non-cooperative spacecraft operations, Autonomous Hazard Detection and Avoidance (AHDA) for landing craft, terrain mapping, vehicle
3D face recognition under expressions, occlusions, and pose variations.
Drira, Hassen; Ben Amor, Boulbaba; Srivastava, Anuj; Daoudi, Mohamed; Slama, Rim
2013-09-01
We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both--empirical and theoretical--perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes. PMID:23868784
NASA Astrophysics Data System (ADS)
Otake, Y.; Murphy, R. J.; Kutzer, M. D.; Taylor, R. H.; Armand, M.
2014-03-01
Background: Snake-like dexterous manipulators may offer significant advantages in minimally-invasive surgery in areas not reachable with conventional tools. Precise control of a wire-driven manipulator is challenging due to factors such as cable deformation, unknown internal (cable friction) and external forces, thus requiring correcting the calibration intraoperatively by determining the actual pose of the manipulator. Method: A method for simultaneously estimating pose and kinematic configuration of a piecewise-rigid object such as a snake-like manipulator from a single x-ray projection is presented. The method parameterizes kinematics using a small number of variables (e.g., 5), and optimizes them simultaneously with the 6 degree-of-freedom pose parameter of the base link using an image similarity between digitally reconstructed radiographs (DRRs) of the manipulator's attenuation model and the real x-ray projection. Result: Simulation studies assumed various geometric magnifications (1.2-2.6) and out-of-plane angulations (0°-90°) in a scenario of hip osteolysis treatment, which demonstrated the median joint angle error was 0.04° (for 2.0 magnification, +/-10° out-of-plane rotation). Average computation time was 57.6 sec with 82,953 function evaluations on a mid-range GPU. The joint angle error remained lower than 0.07° while out-of-plane rotation was 0°-60°. An experiment using video images of a real manipulator demonstrated a similar trend as the simulation study except for slightly larger error around the tip attributed to accumulation of errors induced by deformation around each joint not modeled with a simple pin joint. Conclusions: The proposed approach enables high precision tracking of a piecewise-rigid object (i.e., a series of connected rigid structures) using a single projection image by incorporating prior knowledge about the shape and kinematic behavior of the object (e.g., each rigid structure connected by a pin joint parameterized by a
Accurate pose estimation for forensic identification
NASA Astrophysics Data System (ADS)
Merckx, Gert; Hermans, Jeroen; Vandermeulen, Dirk
2010-04-01
In forensic authentication, one aims to identify the perpetrator among a series of suspects or distractors. A fundamental problem in any recognition system that aims for identification of subjects in a natural scene is the lack of constrains on viewing and imaging conditions. In forensic applications, identification proves even more challenging, since most surveillance footage is of abysmal quality. In this context, robust methods for pose estimation are paramount. In this paper we will therefore present a new pose estimation strategy for very low quality footage. Our approach uses 3D-2D registration of a textured 3D face model with the surveillance image to obtain accurate far field pose alignment. Starting from an inaccurate initial estimate, the technique uses novel similarity measures based on the monogenic signal to guide a pose optimization process. We will illustrate the descriptive strength of the introduced similarity measures by using them directly as a recognition metric. Through validation, using both real and synthetic surveillance footage, our pose estimation method is shown to be accurate, and robust to lighting changes and image degradation.
Determination of vertebral pose in 3D by minimization of vertebral asymmetry
NASA Astrophysics Data System (ADS)
Vrtovec, Tomaž; Pernuš, Franjo; Likar, Boštjan
2011-03-01
The vertebral pose in three dimensions (3D) may provide valuable information for quantitative clinical measurements or aid the initialization of image analysis techniques. We propose a method for automated determination of the vertebral pose in 3D that, in an iterative registration scheme, estimates the position and rotation of the vertebral coordinate system in 3D images. By searching for the hypothetical points, which are located where the boundaries of anatomical structures would have maximal symmetrical correspondences when mirrored over the vertebral planes, the asymmetry of vertebral anatomical structures is minimized. The method was evaluated on 14 normal and 14 scoliotic vertebrae in images acquired by computed tomography (CT). For each vertebra, 1000 randomly initialized experiments were performed. The results show that the vertebral pose can be successfully determined in 3D with mean accuracy of 0.5mm and 0.6° and mean precision of 0.17mm and 0.17. according to the 3D position and 3D rotation, respectively.
Particle Filters and Occlusion Handling for Rigid 2D-3D Pose Tracking.
Lee, Jehoon; Sandhu, Romeil; Tannenbaum, Allen
2013-08-01
In this paper, we address the problem of 2D-3D pose estimation. Specifically, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose (position and orientation) in 3D space. We revisit a joint 2D segmentation/3D pose estimation technique, and then extend the framework by incorporating a particle filter to robustly track the object in a challenging environment, and by developing an occlusion detection and handling scheme to continuously track the object in the presence of occlusions. In particular, we focus on partial occlusions that prevent the tracker from extracting an exact region properties of the object, which plays a pivotal role for region-based tracking methods in maintaining the track. To this end, a dynamical choice of how to invoke the objective functional is performed online based on the degree of dependencies between predictions and measurements of the system in accordance with the degree of occlusion and the variation of the object's pose. This scheme provides the robustness to deal with occlusions of an obstacle with different statistical properties from that of the object of interest. Experimental results demonstrate the practical applicability and robustness of the proposed method in several challenging scenarios. PMID:24058277
Aircraft recognition and pose estimation
NASA Astrophysics Data System (ADS)
Hmam, Hatem; Kim, Jijoong
2000-05-01
This work presents a geometry based vision system for aircraft recognition and pose estimation using single images. Pose estimation improves the tracking performance of guided weapons with imaging seekers, and is useful in estimating target manoeuvres and aim-point selection required in the terminal phase of missile engagements. After edge detection and straight-line extraction, a hierarchy of geometric reasoning algorithms is applied to form line clusters (or groupings) for image interpretation. Assuming a scaled orthographic projection and coplanar wings, lateral symmetry inherent in the airframe provides additional constraints to further reject spurious line clusters. Clusters that accidentally pass all previous tests are checked against the original image and are discarded. Valid line clusters are then used to deduce aircraft viewing angles. By observing that the leading edges of wings of a number of aircraft of interest are within 45 to 65 degrees from the symmetry axis, a bounded range of aircraft viewing angles can be found. This generic property offers the advantage of not requiring the storage of complete aircraft models viewed from all aspects, and can handle aircraft with flexible wings (e.g. F111). Several aircraft images associated with various spectral bands (i.e. visible and infra-red) are finally used to evaluate the system's performance.
Global regular solutions for the 3D Kawahara equation posed on unbounded domains
NASA Astrophysics Data System (ADS)
Larkin, Nikolai A.; Simões, Márcio Hiran
2016-08-01
An initial boundary value problem for the 3D Kawahara equation posed on a channel-type domain was considered. The existence and uniqueness results for global regular solutions as well as exponential decay of small solutions in the H 2-norm were established.
Global regular solutions for the 3D Zakharov-Kuznetsov equation posed on unbounded domains
NASA Astrophysics Data System (ADS)
Larkin, N. A.
2015-09-01
An initial-boundary value problem for the 3D Zakharov-Kuznetsov equation posed on unbounded domains is considered. Existence and uniqueness of a global regular solution as well as exponential decay of the H2-norm for small initial data are proven.
Human Pose Estimation Using Consistent Max Covering.
Jiang, Hao
2011-09-01
A novel consistent max-covering method is proposed for human pose estimation. We focus on problems in which a rough foreground estimation is available. Pose estimation is formulated as a jigsaw puzzle problem in which the body part tiles maximally cover the foreground region, match local image features, and satisfy body plan and color constraints. This method explicitly imposes a global shape constraint on the body part assembly. It anchors multiple body parts simultaneously and introduces hyperedges in the part relation graph, which is essential for detecting complex poses. Using multiple cues in pose estimation, our method is resistant to cluttered foregrounds. We propose an efficient linear method to solve the consistent max-covering problem. A two-stage relaxation finds the solution in polynomial time. Our experiments on a variety of images and videos show that the proposed method is more robust than previous locally constrained methods. PMID:21576747
Combining focused MACE filters for pose estimation
NASA Astrophysics Data System (ADS)
Al-Ghoneim, Khaled A.; Vijaya Kumar, Bhagavatula
1998-03-01
In this paper we introduce the notion of a focused filter and discuss its application to the problem of pose estimation. A focused filter is a correlation filter designed to give a maximum response at one pose of the target. This pose is called the focus of the filter. As the actual pose of the target deviates from the focus, the filter's response should exhibit a graceful (and controlled) degradation. When presented with a test image, the responses of all focused filters are collected in a vector. This new vector will have a peak with the vector elements exhibiting the same shape as that used in designing one focused filter. This similarity is exploited for pose estimation by matching the filter responses to the designed shape. Simulation experiments are used to illustrate the potential of the new design method.
Particle swarm optimization on low dimensional pose manifolds for monocular human pose estimation
NASA Astrophysics Data System (ADS)
Brauer, Jürgen; Hübner, Wolfgang; Arens, Michael
2013-10-01
Automatic assessment of situations with modern security and surveillance systems requires sophisticated discrimination capabilities. Therefore, action recognition, e.g. in terms of person-person or person-object interactions, is an essential core component of any surveillance system. A subclass of recent action recognition approaches are based on space time volumes, which are generated from trajectories of multiple anatomical landmarks like hands or shoulders. A general prerequisite of these methods is the robust estimation of the body pose, i.e. a simplified body model consisting of several anatomical landmarks. In this paper we address the problem of estimating 3D poses from monocular person image sequences. The first stage of our algorithm is the localization of body parts in the 2D image. For this, a part based object detection method is used, which in previous work has been shown to provide a sufficient basis for person detection and landmark estimation in a single step. The output of this processing step is a probability distribution for each landmark and image indicating possible locations of this landmark in image coordinates. The second stage of our algorithm searches for 3D pose estimates that best t to the 15 landmark probability distributions. For resolving ambiguities introduced by uncertainty in the locations of the landmarks, we perform an optimization within a Particle Swarm Optimization (PSO) framework, where each pose hypothesis is represented by a particle. Since the search in the high-dimensional 3D pose search space needs further guidance to deal with the inherently restricted 2D input information, we propose a new compact representation of motion sequences provided by motion capture databases. Poses of a motion sequence are embedded in a low-dimensional manifold. We represent each motion sequence by a compact representation referred to as pose splines using a small number of supporting point poses. The PSO algorithm can be extended to perform
Factoring Algebraic Error for Relative Pose Estimation
Lindstrom, P; Duchaineau, M
2009-03-09
We address the problem of estimating the relative pose, i.e. translation and rotation, of two calibrated cameras from image point correspondences. Our approach is to factor the nonlinear algebraic pose error functional into translational and rotational components, and to optimize translation and rotation independently. This factorization admits subproblems that can be solved using direct methods with practical guarantees on global optimality. That is, for a given translation, the corresponding optimal rotation can directly be determined, and vice versa. We show that these subproblems are equivalent to computing the least eigenvector of second- and fourth-order symmetric tensors. When neither translation or rotation is known, alternating translation and rotation optimization leads to a simple, efficient, and robust algorithm for pose estimation that improves on the well-known 5- and 8-point methods.
Joint 3d Estimation of Vehicles and Scene Flow
NASA Astrophysics Data System (ADS)
Menze, M.; Heipke, C.; Geiger, A.
2015-08-01
driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.
Pose estimation of non-cooperative targets without feature tracking
NASA Astrophysics Data System (ADS)
Liu, Jie; Liu, Zongming; Lu, Shan; Sang, Nong
2015-03-01
Pose estimation is playing the vital role in the final approach phase of two spacecraft, one is the target spacecraft and the other one is the observation spacecraft. Traditional techniques are usually based on feature tracking, which will not work when sufficient features are unavailable. To deal with this problem, we present a stereo camera-based pose estimation method without feature tracking. First, stereo vision is used to reconstruct 2.5D of the target spacecraft and a 3D reconstruction is presented by merged all the point cloud of each viewpoint. Then a target-coordinate system is built using the reconstruction results. Finally, point cloud registration algorithm is used to solve the current pose between the observation spacecraft and the target spacecraft. Experimental results show that both the position errors and the attitude errors satisfy the requirements of pose estimation. The method provides a solution for pose estimation without knowing the information of the targets and this algorithm is with wider application range compared with the other algorithms based on feature tracking.
Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation
NASA Technical Reports Server (NTRS)
Rakoczy, John; Herren, Kenneth
2007-01-01
A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.
Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation
NASA Technical Reports Server (NTRS)
Rakoczy, John M.; Herren, Kenneth A.
2008-01-01
A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.
Maximal likelihood correspondence estimation for face recognition across pose.
Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang
2014-10-01
Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database. PMID:25163062
Pose Estimation and Mapping Using Catadioptric Cameras with Spherical Mirrors
NASA Astrophysics Data System (ADS)
Ilizirov, Grigory; Filin, Sagi
2016-06-01
Catadioptric cameras have the advantage of broadening the field of view and revealing otherwise occluded object parts. However, they differ geometrically from standard central perspective cameras because of light reflection from the mirror surface which alters the collinearity relation and introduces severe non-linear distortions of the imaged scene. Accommodating for these features, we present in this paper a novel modeling for pose estimation and reconstruction while imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which we estimate the system's parameters. Our model yields a resection-like solution which can be developed into a linear one. We show that accurate estimates can be derived with only a small set of control points. Analysis shows that control configuration in the orientation scheme is rather flexible and that high levels of accuracy can be reached in both pose estimation and mapping. Clearly, the ability to model objects which fall outside of the immediate camera field-of-view offers an appealing means to supplement 3-D reconstruction and modeling.
NASA Astrophysics Data System (ADS)
Miao, Shun; Lucas, Joseph; Liao, Rui
2012-02-01
Minimally invasive abdominal aortic aneurysm (AAA) stenting can be greatly facilitated by overlaying the preoperative 3-D model of the abdominal aorta onto the intra-operative 2-D X-ray images. Accurate 2-D/3-D registration in 3-D space makes the 2-D/3-D overlay robust to the change of C-Arm angulations. By far, the 2-D/3-D registration methods based on simulated X-ray projection images using multiple image planes have been shown to be able to provide satisfactory 3-D registration accuracy. However, one drawback of the intensity-based 2-D/3-D registration methods is that the similarity measure is usually highly non-convex and hence the optimizer can easily be trapped into local minima. User interaction therefore is often needed in the initialization of the position of the 3-D model in order to get a successful 2-D/3-D registration. In this paper, a novel 3-D pose initialization technique is proposed, as an extension of our previously proposed bi-plane 2-D/3-D registration method for AAA intervention [4]. The proposed method detects vessel bifurcation points and spine centerline in both 2-D and 3-D images, and utilizes landmark information to bring the 3-D volume into a 15mm capture range. The proposed landmark detection method was validated on real dataset, and is shown to be able to provide a good initialization for 2-D/3-D registration in [4], thus making the workflow fully automatic.
Spatio-Temporal Matching for Human Pose Estimation in Video.
Zhou, Feng; Torre, Fernando De la
2016-08-01
Detection and tracking humans in videos have been long-standing problems in computer vision. Most successful approaches (e.g., deformable parts models) heavily rely on discriminative models to build appearance detectors for body joints and generative models to constrain possible body configurations (e.g., trees). While these 2D models have been successfully applied to images (and with less success to videos), a major challenge is to generalize these models to cope with camera views. In order to achieve view-invariance, these 2D models typically require a large amount of training data across views that is difficult to gather and time-consuming to label. Unlike existing 2D models, this paper formulates the problem of human detection in videos as spatio-temporal matching (STM) between a 3D motion capture model and trajectories in videos. Our algorithm estimates the camera view and selects a subset of tracked trajectories that matches the motion of the 3D model. The STM is efficiently solved with linear programming, and it is robust to tracking mismatches, occlusions and outliers. To the best of our knowledge this is the first paper that solves the correspondence between video and 3D motion capture data for human pose detection. Experiments on the CMU motion capture, Human3.6M, Berkeley MHAD and CMU MAD databases illustrate the benefits of our method over state-of-the-art approaches. PMID:26863647
Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization
NASA Technical Reports Server (NTRS)
Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.
2012-01-01
The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.
Exhaustive linearization for robust camera pose and focal length estimation.
Penate-Sanchez, Adrian; Andrade-Cetto, Juan; Moreno-Noguer, Francesc
2013-10-01
We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera. PMID:23969384
Interferometric synthetic aperture radar detection and estimation based 3D image reconstruction
NASA Astrophysics Data System (ADS)
Austin, Christian D.; Moses, Randolph L.
2006-05-01
This paper explores three-dimensional (3D) interferometric synthetic aperture radar (IFSAR) image reconstruction when multiple scattering centers and noise are present in a radar resolution cell. We introduce an IFSAR scattering model that accounts for both multiple scattering centers and noise. The problem of 3D image reconstruction is then posed as a multiple hypothesis detection and estimation problem; resolution cells containing a single scattering center are detected and the 3D location of these cells' pixels are estimated; all other pixels are rejected from the image. Detection and estimation statistics are derived using the multiple scattering center IFSAR model. A 3D image reconstruction algorithm using these statistics is then presented, and its performance is evaluated for a 3D reconstruction of a backhoe from noisy IFSAR data.
A Survey on Model Based Approaches for 2D and 3D Visual Human Pose Recovery
Perez-Sala, Xavier; Escalera, Sergio; Angulo, Cecilio; Gonzàlez, Jordi
2014-01-01
Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. PMID:24594613
NASA Astrophysics Data System (ADS)
Gorbatsevich, V.; Vizilter, Yu.; Knyaz, V.; Zheltov, S.
2014-06-01
A technique for automated face detection and its pose estimation using single image is developed. The algorithm includes: face detection, facial features localization, face/background segmentation, face pose estimation, image transformation to frontal view. Automatic face/background segmentation is performed by original graph-cut technique based on detected feature points. The precision of face orientation estimation based on monocular digital imagery is addressed. The approach for precision estimation is developed based on comparison of synthesized facial 2D images and scanned face 3D model. The software for modelling and measurement is developed. The special system for non-contact measurements is created. Required set of 3D real face models and colour facial textures is obtained using this system. The precision estimation results demonstrate the precision of face pose estimation enough for further successful face recognition.
Efficient human pose estimation from single depth images.
Shotton, Jamie; Girshick, Ross; Fitzgibbon, Andrew; Sharp, Toby; Cook, Mat; Finocchio, Mark; Moore, Richard; Kohli, Pushmeet; Criminisi, Antonio; Kipman, Alex; Blake, Andrew
2013-12-01
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities. PMID:24136424
Efficient Human Pose Estimation from Single Depth Images.
Shotton, Jamie; Girshick, Ross; Fitzgibbon, Andrew; Sharp, Toby; Cook, Mat; Finocchio, Mark; Moore, Richard; Kohli, Pushmeet; Criminisi, Antonio; Kipman, Alex; Blake, Andrew
2012-10-26
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image, without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features, and parallelizable decision forests, both approaches can run super-realtime on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities. PMID:23109523
A model-based 3D template matching technique for pose acquisition of an uncooperative space object.
Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele
2015-01-01
This paper presents a customized three-dimensional template matching technique for autonomous pose determination of uncooperative targets. This topic is relevant to advanced space applications, like active debris removal and on-orbit servicing. The proposed technique is model-based and produces estimates of the target pose without any prior pose information, by processing three-dimensional point clouds provided by a LIDAR. These estimates are then used to initialize a pose tracking algorithm. Peculiar features of the proposed approach are the use of a reduced number of templates and the idea of building the database of templates on-line, thus significantly reducing the amount of on-board stored data with respect to traditional techniques. An algorithm variant is also introduced aimed at further accelerating the pose acquisition time and reducing the computational cost. Technique performance is investigated within a realistic numerical simulation environment comprising a target model, LIDAR operation and various target-chaser relative dynamics scenarios, relevant to close-proximity flight operations. Specifically, the capability of the proposed techniques to provide a pose solution suitable to initialize the tracking algorithm is demonstrated, as well as their robustness against highly variable pose conditions determined by the relative dynamics. Finally, a criterion for autonomous failure detection of the presented techniques is presented. PMID:25785309
A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object
Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele
2015-01-01
This paper presents a customized three-dimensional template matching technique for autonomous pose determination of uncooperative targets. This topic is relevant to advanced space applications, like active debris removal and on-orbit servicing. The proposed technique is model-based and produces estimates of the target pose without any prior pose information, by processing three-dimensional point clouds provided by a LIDAR. These estimates are then used to initialize a pose tracking algorithm. Peculiar features of the proposed approach are the use of a reduced number of templates and the idea of building the database of templates on-line, thus significantly reducing the amount of on-board stored data with respect to traditional techniques. An algorithm variant is also introduced aimed at further accelerating the pose acquisition time and reducing the computational cost. Technique performance is investigated within a realistic numerical simulation environment comprising a target model, LIDAR operation and various target-chaser relative dynamics scenarios, relevant to close-proximity flight operations. Specifically, the capability of the proposed techniques to provide a pose solution suitable to initialize the tracking algorithm is demonstrated, as well as their robustness against highly variable pose conditions determined by the relative dynamics. Finally, a criterion for autonomous failure detection of the presented techniques is presented. PMID:25785309
Human Pose Estimation from Video and IMUs.
Marcard, Timo von; Pons-Moll, Gerard; Rosenhahn, Bodo
2016-08-01
In this work, we present an approach to fuse video with sparse orientation data obtained from inertial sensors to improve and stabilize full-body human motion capture. Even though video data is a strong cue for motion analysis, tracking artifacts occur frequently due to ambiguities in the images, rapid motions, occlusions or noise. As a complementary data source, inertial sensors allow for accurate estimation of limb orientations even under fast motions. However, accurate position information cannot be obtained in continuous operation. Therefore, we propose a hybrid tracker that combines video with a small number of inertial units to compensate for the drawbacks of each sensor type: on the one hand, we obtain drift-free and accurate position information from video data and, on the other hand, we obtain accurate limb orientations and good performance under fast motions from inertial sensors. In several experiments we demonstrate the increased performance and stability of our human motion tracker. PMID:26829774
Pose estimation quality assessment for intra-operative image guidance systems
NASA Astrophysics Data System (ADS)
Egli, Adrian; Kleinszig, Gerhard; John, Adrian; Fernandez, Alberto; Cardelino, Juan
2013-03-01
In trauma and orthopedic surgery screw assessment and trajectory prediction using two-dimensional X-ray images is very difficult due to projected 3D information. However screw assessment can be done with multiple X-ray images. If the X-ray image contains the projected implant geometry it can be used as global coordinate reference. Thereby multiple independent X-ray images can be synchronized by estimating the implant pose in each single image. Consequently high accuracy pose estimation is fundamental. To measure the outcome quality an evaluation process has been designed. The evaluation process investigates in its first step several clinical intra-operative anterior-posterior (AP) and medio-lateral (ML) X-ray images which have been analyzed using a manual pose estimation method. With the manual method the six 3D parameters of the implant pose are estimated. These parameters define as well the camera pose relative to the implant. Based on the pose parameters of all clinical cases the capturing range for typical AP and ML images is statistically defined. The implant was attached to a phantom with 16 steel balls which allows to calculate the ground truth pose. Afterwards several X-ray images of the phantom are taken within the statistically defined capturing range. With the known ground truth different pose estimation methods can be compared. For each method the estimation quality can be calculated. In addition this error calculation can be used to adjust the initial manually determined capturing range. This paper explains the error evaluation process and describes how to validate pose estimation methods for clinical applications.
Uncooperative pose estimation with a LIDAR-based system
NASA Astrophysics Data System (ADS)
Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele
2015-05-01
This paper aims at investigating the performance of a LIDAR-based system for pose determination of uncooperative targets. This problem is relevant to both debris removal and on-orbit servicing missions, and requires the adoption of suitable electro-optical sensors on board of a chaser platform, as well as model-based techniques for target detection and pose estimation. In this paper, a three dimensional approach is pursued in which the point cloud generated by a LIDAR is exploited for pose estimation. Specifically, the condition of close proximity flight to a large debris is considered, in which the relative motion determines a large variation of debris appearance and coverage in the sensor field of view, thus producing challenging conditions for pose estimation. A customized three dimensional Template Matching approach is proposed for fast and reliable pose initial acquisition, while pose tracking is carried out with an Iterative Closest Point algorithm exploiting different measurement-model matching techniques. Specific solutions are envisaged to speed algorithm convergence and limit the size of the point clouds used for pose initial acquisition and tracking to allow autonomous on-board operation. To investigate proposed approach effectiveness and achievable pose accuracy, a numerical simulation environment is developed implementing realistic debris geometry, debris-chaser close-proximity flight, and sensor operation. Results demonstrate algorithm capability of operating with sparse point clouds and large pose variations, while achieving sub-degree and sub-centimeter accuracy in relative attitude and position, respectively.
Developing rigid constraint for the estimation of pose and structure from a single image.
Wei, Bao-Gang; Liu, Yong-Huai
2004-07-01
Pose and structure estimation from a single image is a fundamental problem in machine vision and multiple sensor fusion and integration. In this paper we propose using rigid constraints described in different coordinate frames to iteratively estimate structural and camera pose parameters. Using geometric properties of reflected correspondences we put forward a new concept, the reflected pole of a rigid transformation. The reflected pole represents a general analysis of transformations that can be applied to both 2D and 3D transformations. We demonstrate how the concept is applied to calibration by proposing an iterative method to estimate the structural parameters of objects. The method is based on a coarse-to-fine strategy in which initial estimation is obtained through a classical linear algorithm which is then refined by iteration. For a comparative study of performance, we also implemented an extended motion estimation algorithm (from 2D-2D to 3D-2D case) based on epipolar geometry. PMID:15495305
Accurate pose estimation using single marker single camera calibration system
NASA Astrophysics Data System (ADS)
Pati, Sarthak; Erat, Okan; Wang, Lejing; Weidert, Simon; Euler, Ekkehard; Navab, Nassir; Fallavollita, Pascal
2013-03-01
Visual marker based tracking is one of the most widely used tracking techniques in Augmented Reality (AR) applications. Generally, multiple square markers are needed to perform robust and accurate tracking. Various marker based methods for calibrating relative marker poses have already been proposed. However, the calibration accuracy of these methods relies on the order of the image sequence and pre-evaluation of pose-estimation errors, making the method offline. Several studies have shown that the accuracy of pose estimation for an individual square marker depends on camera distance and viewing angle. We propose a method to accurately model the error in the estimated pose and translation of a camera using a single marker via an online method based on the Scaled Unscented Transform (SUT). Thus, the pose estimation for each marker can be estimated with highly accurate calibration results independent of the order of image sequences compared to cases when this knowledge is not used. This removes the need for having multiple markers and an offline estimation system to calculate camera pose in an AR application.
Vision-based pose estimation for cooperative space objects
NASA Astrophysics Data System (ADS)
Zhang, Haopeng; Jiang, Zhiguo; Elgammal, Ahmed
2013-10-01
Imaging sensors are widely used in aerospace recently. In this paper, a vision-based approach for estimating the pose of cooperative space objects is proposed. We learn generative model for each space object based on homeomorphic manifold analysis. Conceptual manifold is used to represent pose variation of captured images of the object in visual space, and nonlinear functions mapping between conceptual manifold representation and visual inputs are learned. Given such learned model, we estimate the pose of a new image by minimizing a reconstruction error via a traversal procedure along the conceptual manifold. Experimental results on the simulated image dataset show that our approach is effective for 1D and 2D pose estimation.
Pose estimation and frontal face detection for face recognition
NASA Astrophysics Data System (ADS)
Lim, Eng Thiam; Wang, Jiangang; Xie, Wei; Ronda, Venkarteswarlu
2005-05-01
This paper proposes a pose estimation and frontal face detection algorithm for face recognition. Considering it's application in a real-world environment, the algorithm has to be robust yet computationally efficient. The main contribution of this paper is the efficient face localization, scale and pose estimation using color models. Simulation results showed very low computational load when compare to other face detection algorithm. The second contribution is the introduction of low dimensional statistical face geometrical model. Compared to other statistical face model the proposed method models the face geometry efficiently. The algorithm is demonstrated on a real-time system. The simulation results indicate that the proposed algorithm is computationally efficient.
A pose estimation method for unmanned ground vehicles in GPS denied environments
NASA Astrophysics Data System (ADS)
Tamjidi, Amirhossein; Ye, Cang
2012-06-01
This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ~1.9% of the path length.
Coevrage Estimation of Geosensor in 3d Vector Environments
NASA Astrophysics Data System (ADS)
Afghantoloee, A.; Doodman, S.; Karimipour, F.; Mostafavi, M. A.
2014-10-01
Sensor deployment optimization to achieve the maximum spatial coverage is one of the main issues in Wireless geoSensor Networks (WSN). The model of the environment is an imperative parameter that influences the accuracy of geosensor coverage. In most of recent studies, the environment has been modeled by Digital Surface Model (DSM). However, the advances in technology to collect 3D vector data at different levels, especially in urban models can enhance the quality of geosensor deployment in order to achieve more accurate coverage estimations. This paper proposes an approach to calculate the geosensor coverage in 3D vector environments. The approach is applied on some case studies and compared with DSM based methods.
Vision based object pose estimation for mobile robots
NASA Technical Reports Server (NTRS)
Wu, Annie; Bidlack, Clint; Katkere, Arun; Feague, Roy; Weymouth, Terry
1994-01-01
Mobile robot navigation using visual sensors requires that a robot be able to detect landmarks and obtain pose information from a camera image. This paper presents a vision system for finding man-made markers of known size and calculating the pose of these markers. The algorithm detects and identifies the markers using a weighted pattern matching template. Geometric constraints are then used to calculate the position of the markers relative to the robot. The selection of geometric constraints comes from the typical pose of most man-made signs, such as the sign standing vertical and the dimensions of known size. This system has been tested successfully on a wide range of real images. Marker detection is reliable, even in cluttered environments, and under certain marker orientations, estimation of the orientation has proven accurate to within 2 degrees, and distance estimation to within 0.3 meters.
Hand surface area estimation formula using 3D anthropometry.
Hsu, Yao-Wen; Yu, Chi-Yuang
2010-11-01
Hand surface area is an important reference in occupational hygiene and many other applications. This study derives a formula for the palm surface area (PSA) and hand surface area (HSA) based on three-dimensional (3D) scan data. Two-hundred and seventy subjects, 135 males and 135 females, were recruited for this study. The hand was measured using a high-resolution 3D hand scanner. Precision and accuracy of the scanner is within 0.67%. Both the PSA and HSA were computed using the triangular mesh summation method. A comparison between this study and previous textbook values (such as in the U.K. teaching text and Lund and Browder chart discussed in the article) was performed first to show that previous textbooks overestimated the PSA by 12.0% and HSA by 8.7% (for the male, PSA 8.5% and HSA 4.7%, and for the female, PSA 16.2% and HSA 13.4%). Six 1D measurements were then extracted semiautomatically for use as candidate estimators for the PSA and HSA estimation formula. Stepwise regressions on these six 1D measurements and variable dependency test were performed. Results show that a pair of measurements (hand length and hand breadth) were able to account for 96% of the HSA variance and up to 98% of the PSA variance. A test of the gender-specific formula indicated that gender is not a significant factor in either the PSA or HSA estimation. PMID:20865628
Joint tracking, pose estimation, and identification using HRRR data
NASA Astrophysics Data System (ADS)
Mahler, Ronald P. S.; Rago, Constantino; Zajic, Tim; Musick, Stanton; Mehra, Raman K.
2000-08-01
The work presented here is pat of a generalization of Bayesian filtering and estimation theory to the problem of multisource, multitarget, multi-evidence unified joint detection, tracking, and target ID developed by Lockheed Martin Tactical Defense Systems and Scientific Systems Co., Inc. Our approach to robust joint target identification and tracking was to take the StaF algorithm and integrate it with a Bayesian nonlinear filter, where target position, velocity, pose, and type could then be determined simultaneously via maximum a posteriori estimation. The basis for the integration between the tracker and classifier is base don 'finite-set statistics' (FISST). The theoretical development of FISST is a Lockheed Martin ongoing project since 1994. The specific problem addressed in this paper is that of robust joint target identification and tracking via fusion of high range resolution radar (HRRR) - from the automatic radar target identification (ARTI) data base - signatures and radar track data. A major problem in HRRR ATR is the computational load created by having to match observations against target models for every feasible pose. If pose could be estimated efficiently by a filtering algorithm from track data, the ATR search space would be greatly reduced. On the other hand, HRRR ATR algorithms produce useful information about pose which could potentially aid the track-filtering process as well. We have successfully demonstrated the former concept of 'loose integration' integrating the tracker and classifier for three different type of targets moving on 2D tracks.
An anti-disturbing real time pose estimation method and system
NASA Astrophysics Data System (ADS)
Zhou, Jian; Zhang, Xiao-hu
2011-08-01
Pose estimation relating two-dimensional (2D) images to three-dimensional (3D) rigid object need some known features to track. In practice, there are many algorithms which perform this task in high accuracy, but all of these algorithms suffer from features lost. This paper investigated the pose estimation when numbers of known features or even all of them were invisible. Firstly, known features were tracked to calculate pose in the current and the next image. Secondly, some unknown but good features to track were automatically detected in the current and the next image. Thirdly, those unknown features which were on the rigid and could match each other in the two images were retained. Because of the motion characteristic of the rigid object, the 3D information of those unknown features on the rigid could be solved by the rigid object's pose at the two moment and their 2D information in the two images except only two case: the first one was that both camera and object have no relative motion and camera parameter such as focus length, principle point, and etc. have no change at the two moment; the second one was that there was no shared scene or no matched feature in the two image. Finally, because those unknown features at the first time were known now, pose estimation could go on in the followed images in spite of the missing of known features in the beginning by repeating the process mentioned above. The robustness of pose estimation by different features detection algorithms such as Kanade-Lucas-Tomasi (KLT) feature, Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) were compared and the compact of the different relative motion between camera and the rigid object were discussed in this paper. Graphic Processing Unit (GPU) parallel computing was also used to extract and to match hundreds of features for real time pose estimation which was hard to work on Central Processing Unit (CPU). Compared with other pose estimation methods, this new
Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation.
Ding, Meng; Fan, Guoliang
2016-02-01
In this paper, we propose an articulated and generalized Gaussian kernel correlation (GKC)-based framework for human pose estimation. We first derive a unified GKC representation that generalizes the previous sum of Gaussians (SoG)-based methods for the similarity measure between a template and an observation both of which are represented by various SoG variants. Then, we develop an articulated GKC (AGKC) by integrating a kinematic skeleton in a multivariate SoG template that supports subject-specific shape modeling and articulated pose estimation for both the full body and the hands. We further propose a sequential (body/hand) pose tracking algorithm by incorporating three regularization terms in the AGKC function, including visibility, intersection penalty, and pose continuity. Our tracking algorithm is simple yet effective and computationally efficient. We evaluate our algorithm on two benchmark depth data sets. The experimental results are promising and competitive when compared with the state-of-the-art algorithms. PMID:26672042
A novel regularization method for optical flow-based head pose estimation
NASA Astrophysics Data System (ADS)
Vater, Sebastian; Mann, Guillermo; Puente León, Fernando
2015-05-01
This paper presents a method for appearance-based 3D head pose tracking utilizing optical flow computation. The task is to recover the head pose parameters for extreme head pose angles based on 2D images. A novel method is presented that enables a robust recovery of the full motion by employing a motion-dependent regulatory term within the optical flow algorithm. Thereby, the rigid motion parameters are coupled directly with a regulatory term in the image alignment method affecting translation and rotation independently. The ill-conditioned, nonlinear optimization problem is stabilized by the proposed regulatory term yielding suitable conditioning of the Hessian matrix. It is shown that the regularization corresponding to the motion parameters can be extended to full 3D motion consisting of six parameters. Experiments on the Boston University head pose dataset demonstrate the enhancement of robustness in head pose estimation compared to conventional regularization methods. Using well-defined values for the regulatory parameters, the proposed method shows significant improvement in headtracking scenarios in terms of accuracy compared to existing methods.
Relative pose estimation of satellites using PMD-/CCD-sensor data fusion
NASA Astrophysics Data System (ADS)
Tzschichholz, Tristan; Boge, Toralf; Schilling, Klaus
2015-04-01
Rendezvous & Docking to passive objects, as of relevance for space debris removal, raises new challenges with respect to relative navigation. Whenever the position and orientation (pose) of an object is required in terrestrial and in space applications, sensor systems such as laser scanners and stereo vision systems are often employed. This paper presents an approach to pose estimation using a 3D time-of-flight camera for ranging information in combination with a high resolution grayscale camera. We have designed a pose estimation method that fuses the data streams of the two sensors in order to benefit from each sensors' advantages. A rigorous test campaign on a Real-Time Hardware-In-The-Loop Rendezvous and Docking Simulator - the European Proximity Operations Simulator (EPOS) - was performed in order to evaluate the performance of the resulting algorithm. The proposed pose estimation method does not exceed an average distance error of 3 cm while being capable of providing pose estimates at up to 60 FPS on recent hardware. Thus, when regarding proximity operations, an attractive sensor system is used to characterize the dynamics of the target object for safe approach results.
Marker detection evaluation by phantom and cadaver experiments for C-arm pose estimation pattern
NASA Astrophysics Data System (ADS)
Steger, Teena; Hoßbach, Martin; Wesarg, Stefan
2013-03-01
C-arm fluoroscopy is used for guidance during several clinical exams, e.g. in bronchoscopy to locate the bronchoscope inside the airways. Unfortunately, these images provide only 2D information. However, if the C-arm pose is known, it can be used to overlay the intrainterventional fluoroscopy images with 3D visualizations of airways, acquired from preinterventional CT images. Thus, the physician's view is enhanced and localization of the instrument at the correct position inside the bronchial tree is facilitated. We present a novel method for C-arm pose estimation introducing a marker-based pattern, which is placed on the patient table. The steel markers form a pattern, allowing to deduce the C-arm pose by use of the projective invariant cross-ratio. Simulations show that the C-arm pose estimation is reliable and accurate for translations inside an imaging area of 30 cm x 50 cm and rotations up to 30°. Mean error values are 0.33 mm in 3D space and 0.48 px in the 2D imaging plane. First tests on C-arm images resulted in similarly compelling accuracy values and high reliability in an imaging area of 30 cm x 42.5 cm. Even in the presence of interfering structures, tested both with anatomy phantoms and a turkey cadaver, high success rates over 90% and fully satisfying execution times below 4 sec for 1024 px × 1024 px images could be achieved.
Pose estimation for one-dimensional object with general motion
NASA Astrophysics Data System (ADS)
Liu, Jinbo; Song, Ge; Zhang, Xiaohu
2014-11-01
Our primary interest is in real-time one-dimensional object's pose estimation. In this paper, a method to estimate general motion one-dimensional object's pose, that is, the position and attitude parameters, using a single camera is proposed. Centroid-movement is necessarily continuous and orderly in temporal space, which means it follows at least approximately certain motion law in a short period of time. Therefore, the centroid trajectory in camera frame can be described as a combination of temporal polynomials. Two endpoints on one-dimensional object, A and B, at each time are projected on the corresponding image plane. With the relationship between A, B and centroid C, we can obtain a linear equation system related to the temporal polynomials' coefficients, in which the camera has been calibrated and the image coordinates of A and B are known. Then in the cases that object moves continuous in natural temporal space within the view of a stationary camera, the position of endpoints on the one-dimensional object can be located and also the attitude can be estimated using two end points. Moreover the position of any other point aligned on one-dimensional object can also be solved. Scene information is not needed in the proposed method. If the distance between the endpoints is not known, a scale factor between the object's real positions and the estimated results will exist. In order to improve the algorithm's performance from accuracy and robustness, we derive a pain of linear and optimal algorithms. Simulations' and experiments' results show that the method is valid and robust with respect to various Gaussian noise levels. The paper's work contributes to making self-calibration algorithms using one-dimensional objects applicable to practice. Furthermore, the method can also be used to estimate the pose and shape parameters of parallelogram, prism or cylinder objects.
Shape recognition and pose estimation for mobile Augmented Reality.
Hagbi, Nate; Bergig, Oriel; El-Sana, Jihad; Billinghurst, Mark
2011-10-01
Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class library. Nestor performs shape recognition by analyzing contour structures and generating projective-invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone. PMID:21041876
Rate-constrained 3D surface estimation from noise-corrupted multiview depth videos.
Sun, Wenxiu; Cheung, Gene; Chou, Philip A; Florencio, Dinei; Zhang, Cha; Au, Oscar C
2014-07-01
Transmitting compactly represented geometry of a dynamic 3D scene from a sender can enable a multitude of imaging functionalities at a receiver, such as synthesis of virtual images at freely chosen viewpoints via depth-image-based rendering. While depth maps—projections of 3D geometry onto 2D image planes at chosen camera viewpoints-can nowadays be readily captured by inexpensive depth sensors, they are often corrupted by non-negligible acquisition noise. Given depth maps need to be denoised and compressed at the encoder for efficient network transmission to the decoder, in this paper, we consider the denoising and compression problems jointly, arguing that doing so will result in a better overall performance than the alternative of solving the two problems separately in two stages. Specifically, we formulate a rate-constrained estimation problem, where given a set of observed noise-corrupted depth maps, the most probable (maximum a posteriori (MAP)) 3D surface is sought within a search space of surfaces with representation size no larger than a prespecified rate constraint. Our rate-constrained MAP solution reduces to the conventional unconstrained MAP 3D surface reconstruction solution if the rate constraint is loose. To solve our posed rate-constrained estimation problem, we propose an iterative algorithm, where in each iteration the structure (object boundaries) and the texture (surfaces within the object boundaries) of the depth maps are optimized alternately. Using the MVC codec for compression of multiview depth video and MPEG free viewpoint video sequences as input, experimental results show that rate-constrained estimated 3D surfaces computed by our algorithm can reduce coding rate of depth maps by up to 32% compared with unconstrained estimated surfaces for the same quality of synthesized virtual views at the decoder. PMID:24876124
Efficient intensity-based camera pose estimation in presence of depth
NASA Astrophysics Data System (ADS)
El Choubassi, Maha; Nestares, Oscar; Wu, Yi; Kozintsev, Igor; Haussecker, Horst
2013-03-01
The widespread success of Kinect enables users to acquire both image and depth information with satisfying accuracy at relatively low cost. We leverage the Kinect output to efficiently and accurately estimate the camera pose in presence of rotation, translation, or both. The applications of our algorithm are vast ranging from camera tracking, to 3D points clouds registration, and video stabilization. The state-of-the-art approach uses point correspondences for estimating the pose. More explicitly, it extracts point features from images, e.g., SURF or SIFT, and builds their descriptors, and matches features from different images to obtain point correspondences. However, while features-based approaches are widely used, they perform poorly in scenes lacking texture due to scarcity of features or in scenes with repetitive structure due to false correspondences. Our algorithm is intensity-based and requires neither point features' extraction, nor descriptors' generation/matching. Due to absence of depth, the intensity-based approach alone cannot handle camera translation. With Kinect capturing both image and depth frames, we extend the intensity-based algorithm to estimate the camera pose in case of both 3D rotation and translation. The results are quite promising.
Robust feature tracking for endoscopic pose estimation and structure recovery
NASA Astrophysics Data System (ADS)
Speidel, S.; Krappe, S.; Röhl, S.; Bodenstedt, S.; Müller-Stich, B.; Dillmann, R.
2013-03-01
Minimally invasive surgery is a highly complex medical discipline with several difficulties for the surgeon. To alleviate these difficulties, augmented reality can be used for intraoperative assistance. For visualization, the endoscope pose must be known which can be acquired with a SLAM (Simultaneous Localization and Mapping) approach using the endoscopic images. In this paper we focus on feature tracking for SLAM in minimally invasive surgery. Robust feature tracking and minimization of false correspondences is crucial for localizing the endoscope. As sensory input we use a stereo endoscope and evaluate different feature types in a developed SLAM framework. The accuracy of the endoscope pose estimation is validated with synthetic and ex vivo data. Furthermore we test the approach with in vivo image sequences from da Vinci interventions.
Using glint to perform geometric signature prediction and pose estimation
NASA Astrophysics Data System (ADS)
Paulson, Christopher; Zelnio, Edmund; Gorham, LeRoy; Wu, Dapeng
2012-05-01
We consider two problems in this paper. The rst problem is to construct a dictionary of elements without using synthetic data or a subset of the data collection; the second problem is to estimate the orientation of the vehicle, independent of the elevation angle. These problems are important to the SAR community because it will alleviate the cost to create the dictionary and reduce the number of elements in the dictionary needed for classication. In order to accomplish these tasks, we utilize the glint phenomenology, which is usually viewed as a hindrance in most algorithms but is valuable information in our research. One way to capitalize on the glint information is to predict the location of the int by using geometry of the single and double bounce phenomenology. After qualitative examination of the results, we were able to deduce that the geometry information was sucient for accurately predicting the location of the glint. Another way that we exploited the glint characteristics was by using it to extract the angle feature which we will use to do the pose estimation. Using this technique we were able to predict the cardinal heading of the vehicle within +/-2° with 96:6% having 0° error. Now this research will have an impact on the classication of SAR images because the geometric prediction will reduce the cost and time to develop and maintain the database for SAR ATR systems and the pose estimation will reduce the computational time and improve accuracy of vehicle classication.
Estimating Density Gradients and Drivers from 3D Ionospheric Imaging
NASA Astrophysics Data System (ADS)
Datta-Barua, S.; Bust, G. S.; Curtis, N.; Reynolds, A.; Crowley, G.
2009-12-01
The transition regions at the edges of the ionospheric storm-enhanced density (SED) are important for a detailed understanding of the mid-latitude physical processes occurring during major magnetic storms. At the boundary, the density gradients are evidence of the drivers that link the larger processes of the SED, with its connection to the plasmasphere and prompt-penetration electric fields, to the smaller irregularities that result in scintillations. For this reason, we present our estimates of both the plasma variation with horizontal and vertical spatial scale of 10 - 100 km and the plasma motion within and along the edges of the SED. To estimate the density gradients, we use Ionospheric Data Assimilation Four-Dimensional (IDA4D), a mature data assimilation algorithm that has been developed over several years and applied to investigations of polar cap patches and space weather storms [Bust and Crowley, 2007; Bust et al., 2007]. We use the density specification produced by IDA4D with a new tool for deducing ionospheric drivers from 3D time-evolving electron density maps, called Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE). The EMPIRE technique has been tested on simulated data from TIMEGCM-ASPEN and on IDA4D-based density estimates with ongoing validation from Arecibo ISR measurements [Datta-Barua et al., 2009a; 2009b]. We investigate the SED that formed during the geomagnetic super storm of November 20, 2003. We run IDA4D at low-resolution continent-wide, and then re-run it at high (~10 km horizontal and ~5-20 km vertical) resolution locally along the boundary of the SED, where density gradients are expected to be highest. We input the high-resolution estimates of electron density to EMPIRE to estimate the ExB drifts and field-aligned plasma velocities along the boundaries of the SED. We expect that these drivers contribute to the density structuring observed along the SED during the storm. Bust, G. S. and G. Crowley (2007
Robust endoscopic pose estimation for intraoperative organ-mosaicking
NASA Astrophysics Data System (ADS)
Reichard, Daniel; Bodenstedt, Sebastian; Suwelack, Stefan; Wagner, Martin; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie
2016-03-01
The number of minimally invasive procedures is growing every year. These procedures are highly complex and very demanding for the surgeons. It is therefore important to provide intraoperative assistance to alleviate these difficulties. For most computer-assistance systems, like visualizing target structures with augmented reality, a registration step is required to map preoperative data (e.g. CT images) to the ongoing intraoperative scene. Without additional hardware, the (stereo-) endoscope is the prime intraoperative data source and with it, stereo reconstruction methods can be used to obtain 3D models from target structures. To link reconstructed parts from different frames (mosaicking), the endoscope movement has to be known. In this paper, we present a camera tracking method that uses dense depth and feature registration which are combined with a Kalman Filter scheme. It provides a robust position estimation that shows promising results in ex vivo and in silico experiments.
A combined vision-inertial fusion approach for 6-DoF object pose estimation
NASA Astrophysics Data System (ADS)
Li, Juan; Bernardos, Ana M.; Tarrío, Paula; Casar, José R.
2015-02-01
The estimation of the 3D position and orientation of moving objects (`pose' estimation) is a critical process for many applications in robotics, computer vision or mobile services. Although major research efforts have been carried out to design accurate, fast and robust indoor pose estimation systems, it remains as an open challenge to provide a low-cost, easy to deploy and reliable solution. Addressing this issue, this paper describes a hybrid approach for 6 degrees of freedom (6-DoF) pose estimation that fuses acceleration data and stereo vision to overcome the respective weaknesses of single technology approaches. The system relies on COTS technologies (standard webcams, accelerometers) and printable colored markers. It uses a set of infrastructure cameras, located to have the object to be tracked visible most of the operation time; the target object has to include an embedded accelerometer and be tagged with a fiducial marker. This simple marker has been designed for easy detection and segmentation and it may be adapted to different service scenarios (in shape and colors). Experimental results show that the proposed system provides high accuracy, while satisfactorily dealing with the real-time constraints.
Hand Pose Estimation by Fusion of Inertial and Magnetic Sensing Aided by a Permanent Magnet.
Kortier, Henk G; Antonsson, Jacob; Schepers, H Martin; Gustafsson, Fredrik; Veltink, Peter H
2015-09-01
Tracking human body motions using inertial sensors has become a well-accepted method in ambulatory applications since the subject is not confined to a lab-bounded volume. However, a major drawback is the inability to estimate relative body positions over time because inertial sensor information only allows position tracking through strapdown integration, but does not provide any information about relative positions. In addition, strapdown integration inherently results in drift of the estimated position over time. We propose a novel method in which a permanent magnet combined with 3-D magnetometers and 3-D inertial sensors are used to estimate the global trunk orientation and relative pose of the hand with respect to the trunk. An Extended Kalman Filter is presented to fuse estimates obtained from inertial sensors with magnetic updates such that the position and orientation between the human hand and trunk as well as the global trunk orientation can be estimated robustly. This has been demonstrated in multiple experiments in which various hand tasks were performed. The most complex task in which simultaneous movements of both trunk and hand were performed resulted in an average rms position difference with an optical reference system of 19.7±2.2 mm whereas the relative trunk-hand and global trunk orientation error was 2.3±0.9 and 8.6±8.7 deg respectively. PMID:25222952
NASA Astrophysics Data System (ADS)
Baumhauer, M.; Simpfendörfer, T.; Schwarz, R.; Seitel, M.; Müller-Stich, B. P.; Gutt, C. N.; Rassweiler, J.; Meinzer, H.-P.; Wolf, I.
2007-03-01
We introduce a novel navigation system to support minimally invasive prostate surgery. The system utilizes transrectal ultrasonography (TRUS) and needle-shaped navigation aids to visualize hidden structures via Augmented Reality. During the intervention, the navigation aids are segmented once from a 3D TRUS dataset and subsequently tracked by the endoscope camera. Camera Pose Estimation methods directly determine position and orientation of the camera in relation to the navigation aids. Accordingly, our system does not require any external tracking device for registration of endoscope camera and ultrasonography probe. In addition to a preoperative planning step in which the navigation targets are defined, the procedure consists of two main steps which are carried out during the intervention: First, the preoperatively prepared planning data is registered with an intraoperatively acquired 3D TRUS dataset and the segmented navigation aids. Second, the navigation aids are continuously tracked by the endoscope camera. The camera's pose can thereby be derived and relevant medical structures can be superimposed on the video image. This paper focuses on the latter step. We have implemented several promising real-time algorithms and incorporated them into the Open Source Toolkit MITK (www.mitk.org). Furthermore, we have evaluated them for minimally invasive surgery (MIS) navigation scenarios. For this purpose, a virtual evaluation environment has been developed, which allows for the simulation of navigation targets and navigation aids, including their measurement errors. Besides evaluating the accuracy of the computed pose, we have analyzed the impact of an inaccurate pose and the resulting displacement of navigation targets in Augmented Reality.
Ferrell, R.K.; Jatko, W.B.; Sitter, D.N. Jr.
1996-03-01
Engineers at Oak Ridge National Laboratory have been investigating the feasibility of computer-controlled docking in resupply missions, sponsored by the US Army. The goal of this program is to autonomously dock an articulating robotic boom with a special receiving port. A video camera mounted on the boom provides video images of the docking port to an image processing computer that calculates the position and orientation (pose) of the target relative to the camera. The control system can then move the boom into docking position. This paper describes a method of uniquely identifying and segmenting the receiving port from its background in a sequence of video images. An array of light-emitting diodes was installed to mark the vertices of the port. The markers have a fixed geometric pattern and are modulated at a fixed frequency. An asynchronous demodulation technique to segment flashing markers from an image of the port was developed and tested under laboratory conditions. The technique acquires a sequence of images and digitally processes them in the time domain to suppress all image features except the flashing markers. Pixels that vary at frequencies within the filter bandwidth are passed unattenuated, while variations outside the passband are suppressed. The image coordinates of the segmented markers are computed and then used to calculate the pose of the receiving port. The technique has been robust and reliable in a laboratory demonstration of autodocking.
Eigenvalue Contributon Estimator for Sensitivity Calculations with TSUNAMI-3D
Rearden, Bradley T; Williams, Mark L
2007-01-01
Since the release of the Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) codes in SCALE [1], the use of sensitivity and uncertainty analysis techniques for criticality safety applications has greatly increased within the user community. In general, sensitivity and uncertainty analysis is transitioning from a technique used only by specialists to a practical tool in routine use. With the desire to use the tool more routinely comes the need to improve the solution methodology to reduce the input and computational burden on the user. This paper reviews the current solution methodology of the Monte Carlo eigenvalue sensitivity analysis sequence TSUNAMI-3D, describes an alternative approach, and presents results from both methodologies.
Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha
2014-10-01
Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors. PMID:25046176
Stress Recovery and Error Estimation for 3-D Shell Structures
NASA Technical Reports Server (NTRS)
Riggs, H. R.
2000-01-01
The C1-continuous stress fields obtained from finite element analyses are in general lower- order accurate than are the corresponding displacement fields. Much effort has focussed on increasing their accuracy and/or their continuity, both for improved stress prediction and especially error estimation. A previous project developed a penalized, discrete least squares variational procedure that increases the accuracy and continuity of the stress field. The variational problem is solved by a post-processing, 'finite-element-type' analysis to recover a smooth, more accurate, C1-continuous stress field given the 'raw' finite element stresses. This analysis has been named the SEA/PDLS. The recovered stress field can be used in a posteriori error estimators, such as the Zienkiewicz-Zhu error estimator or equilibrium error estimators. The procedure was well-developed for the two-dimensional (plane) case involving low-order finite elements. It has been demonstrated that, if optimal finite element stresses are used for the post-processing, the recovered stress field is globally superconvergent. Extension of this work to three dimensional solids is straightforward. Attachment: Stress recovery and error estimation for shell structure (abstract only). A 4-node, shear-deformable flat shell element developed via explicit Kirchhoff constraints (abstract only). A novel four-node quadrilateral smoothing element for stress enhancement and error estimation (abstract only).
Integration of a Generalised Building Model Into the Pose Estimation of Uas Images
NASA Astrophysics Data System (ADS)
Unger, J.; Rottensteiner, F.; Heipke, C.
2016-06-01
A hybrid bundle adjustment is presented that allows for the integration of a generalised building model into the pose estimation of image sequences. These images are captured by an Unmanned Aerial System (UAS) equipped with a camera flying in between the buildings. The relation between the building model and the images is described by distances between the object coordinates of the tie points and building model planes. Relations are found by a simple 3D distance criterion and are modelled as fictitious observations in a Gauss-Markov adjustment. The coordinates of model vertices are part of the adjustment as directly observed unknowns which allows for changes in the model. Results of first experiments using a synthetic and a real image sequence demonstrate improvements of the image orientation in comparison to an adjustment without the building model, but also reveal limitations of the current state of the method.
Distributed observers for pose estimation in the presence of inertial sensory soft faults.
Sadeghzadeh-Nokhodberiz, Nargess; Poshtan, Javad; Wagner, Achim; Nordheimer, Eugen; Badreddin, Essameddin
2014-07-01
Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method. PMID:24852356
Impact of Building Heights on 3d Urban Density Estimation from Spaceborne Stereo Imagery
NASA Astrophysics Data System (ADS)
Peng, Feifei; Gong, Jianya; Wang, Le; Wu, Huayi; Yang, Jiansi
2016-06-01
In urban planning and design applications, visualization of built up areas in three dimensions (3D) is critical for understanding building density, but the accurate building heights required for 3D density calculation are not always available. To solve this problem, spaceborne stereo imagery is often used to estimate building heights; however estimated building heights might include errors. These errors vary between local areas within a study area and related to the heights of the building themselves, distorting 3D density estimation. The impact of building height accuracy on 3D density estimation must be determined across and within a study area. In our research, accurate planar information from city authorities is used during 3D density estimation as reference data, to avoid the errors inherent to planar information extracted from remotely sensed imagery. Our experimental results show that underestimation of building heights is correlated to underestimation of the Floor Area Ratio (FAR). In local areas, experimental results show that land use blocks with low FAR values often have small errors due to small building height errors for low buildings in the blocks; and blocks with high FAR values often have large errors due to large building height errors for high buildings in the blocks. Our study reveals that the accuracy of 3D density estimated from spaceborne stereo imagery is correlated to heights of buildings in a scene; therefore building heights must be considered when spaceborne stereo imagery is used to estimate 3D density to improve precision.
Real-time Human Pose and Shape Estimation for Virtual Try-On Using a Single Commodity Depth Camera.
Ye, Mao; Wang, Huamin; Deng, Nianchen; Yang, Xubo; Yang, Ruigang
2014-04-01
We present a system that allows the user to virtually try on new clothes. It uses a single commodity depth camera to capture the user in 3D. Both the pose and the shape of the user are estimated with a novel real-time template-based approach that performs tracking and shape adaptation jointly. The result is then used to drive realistic cloth simulation, in which the synthesized clothes are overlayed on the input image. The main challenge is to handle missing data and pose ambiguities due to the monocular setup, which captures less than 50 percent of the full body. Our solution is to incorporate automatic shape adaptation and novel constraints in pose tracking. The effectiveness of our system is demonstrated with a number of examples. PMID:24650982
Point Cloud Based Relative Pose Estimation of a Satellite in Close Range
Liu, Lujiang; Zhao, Gaopeng; Bo, Yuming
2016-01-01
Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective. PMID:27271633
Point Cloud Based Relative Pose Estimation of a Satellite in Close Range.
Liu, Lujiang; Zhao, Gaopeng; Bo, Yuming
2016-01-01
Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective. PMID:27271633
Model-based 3D human shape estimation from silhouettes for virtual fitting
NASA Astrophysics Data System (ADS)
Saito, Shunta; Kouchi, Makiko; Mochimaru, Masaaki; Aoki, Yoshimitsu
2014-03-01
We propose a model-based 3D human shape reconstruction system from two silhouettes. Firstly, we synthesize a deformable body model from 3D human shape database consists of a hundred whole body mesh models. Each mesh model is homologous, so that it has the same topology and same number of vertices among all models. We perform principal component analysis (PCA) on the database and synthesize an Active Shape Model (ASM). ASM allows changing the body type of the model with a few parameters. The pose changing of our model can be achieved by reconstructing the skeleton structures from implanted joints of the model. By applying pose changing after body type deformation, our model can represents various body types and any pose. We apply the model to the problem of 3D human shape reconstruction from front and side silhouette. Our approach is simply comparing the contours between the model's and input silhouettes', we then use only torso part contour of the model to reconstruct whole shape. We optimize the model parameters by minimizing the difference between corresponding silhouettes by using a stochastic, derivative-free non-linear optimization method, CMA-ES.
Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors
Duong, Pham Duy; Suh, Young Soo
2015-01-01
There are many inertial sensor-based foot pose estimation algorithms. In this paper, we present a methodology to improve the accuracy of foot pose estimation using two low-cost distance sensors (VL6180) in addition to an inertial sensor unit. The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm. A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose. Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose. Through four activities (walking, dancing step, ball kicking, jumping), it is shown that the proposed algorithm significantly improves the vertical position estimation. PMID:26151205
Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors.
Duong, Pham Duy; Suh, Young Soo
2015-01-01
There are many inertial sensor-based foot pose estimation algorithms. In this paper, we present a methodology to improve the accuracy of foot pose estimation using two low-cost distance sensors (VL6180) in addition to an inertial sensor unit. The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm. A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose. Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose. Through four activities (walking, dancing step, ball kicking, jumping), it is shown that the proposed algorithm significantly improves the vertical position estimation. PMID:26151205
Point cloud modeling using the homogeneous transformation for non-cooperative pose estimation
NASA Astrophysics Data System (ADS)
Lim, Tae W.
2015-06-01
A modeling process to simulate point cloud range data that a lidar (light detection and ranging) sensor produces is presented in this paper in order to support the development of non-cooperative pose (relative attitude and position) estimation approaches which will help improve proximity operation capabilities between two adjacent vehicles. The algorithms in the modeling process were based on the homogeneous transformation, which has been employed extensively in robotics and computer graphics, as well as in recently developed pose estimation algorithms. Using a flash lidar in a laboratory testing environment, point cloud data of a test article was simulated and compared against the measured point cloud data. The simulated and measured data sets match closely, validating the modeling process. The modeling capability enables close examination of the characteristics of point cloud images of an object as it undergoes various translational and rotational motions. Relevant characteristics that will be crucial in non-cooperative pose estimation were identified such as shift, shadowing, perspective projection, jagged edges, and differential point cloud density. These characteristics will have to be considered in developing effective non-cooperative pose estimation algorithms. The modeling capability will allow extensive non-cooperative pose estimation performance simulations prior to field testing, saving development cost and providing performance metrics of the pose estimation concepts and algorithms under evaluation. The modeling process also provides "truth" pose of the test objects with respect to the sensor frame so that the pose estimation error can be quantified.
Plantard, Pierre; Auvinet, Edouard; Pierres, Anne-Sophie Le; Multon, Franck
2015-01-01
Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to predict the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging in classical experimental setups. To tackle this problem, we propose a new evaluation method based on a virtual mannequin. In this study, we apply this method to the evaluation of joint positions (shoulder, elbow, and wrist), joint angles (shoulder and elbow), and the corresponding RULA (a popular ergonomics assessment grid) upper-limb score for a large set of poses and sensor placements. Thanks to this evaluation method, more than 500,000 configurations have been automatically tested, which would be almost impossible to evaluate with classical protocols. The results show that the kinematic information obtained by the Kinect software is generally accurate enough to fill in ergonomic assessment grids. However inaccuracy strongly increases for some specific poses and sensor positions. Using this evaluation method enabled us to report configurations that could lead to these high inaccuracies. As a supplementary material, we provide a software tool to help designers to evaluate the expected accuracy of this sensor for a set of upper-limb configurations. Results obtained with the virtual mannequin are in accordance with those obtained from a real subject for a limited set of poses and sensor placements. PMID:25599426
Pose estimation using linearized rotations and quaternion algebra
NASA Astrophysics Data System (ADS)
Barfoot, Timothy; Forbes, James R.; Furgale, Paul T.
2011-01-01
In this paper we revisit the topic of how to formulate error terms for estimation problems that involve rotational state variables. We present a first-principles linearization approach that yields multiplicative error terms for unit-length quaternion representations of rotations, as well as for canonical rotation matrices. Quaternion algebra is employed throughout our derivations. We show the utility of our approach through two examples: (i) linearizing a sun sensor measurement error term, and (ii) weighted-least-squares point-cloud alignment.
Swimmer detection and pose estimation for continuous stroke-rate determination
NASA Astrophysics Data System (ADS)
Zecha, Dan; Greif, Thomas; Lienhart, Rainer
2012-02-01
In this work we propose a novel approach to automatically detect a swimmer and estimate his/her pose continuously in order to derive an estimate of his/her stroke rate given that we observe the swimmer from the side. We divide a swimming cycle of each stroke into several intervals. Each interval represents a pose of the stroke. We use specifically trained object detectors to detect each pose of a stroke within a video and count the number of occurrences per time unit of the most distinctive poses (so-called key poses) of a stroke to continuously infer the stroke rate. We extensively evaluate the overall performance and the influence of the selected poses for all swimming styles on a data set consisting of a variety of swimmers.
Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness
NASA Technical Reports Server (NTRS)
Ivanov, Tonislav; Matthies, Larry; Vasilescu, M. Alex O.
2009-01-01
Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.
Head Pose Estimation on Eyeglasses Using Line Detection and Classification Approach
NASA Astrophysics Data System (ADS)
Setthawong, Pisal; Vannija, Vajirasak
This paper proposes a unique approach for head pose estimation of subjects with eyeglasses by using a combination of line detection and classification approaches. Head pose estimation is considered as an important non-verbal form of communication and could also be used in the area of Human-Computer Interface. A major improvement of the proposed approach is that it allows estimation of head poses at a high yaw/pitch angle when compared with existing geometric approaches, does not require expensive data preparation and training, and is generally fast when compared with other approaches.
Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation.
Airò Farulla, Giuseppe; Pianu, Daniele; Cempini, Marco; Cortese, Mario; Russo, Ludovico O; Indaco, Marco; Nerino, Roberto; Chimienti, Antonio; Oddo, Calogero M; Vitiello, Nicola
2016-01-01
Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master-slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator's hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers' hands movements. PMID:26861333
Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation
Airò Farulla, Giuseppe; Pianu, Daniele; Cempini, Marco; Cortese, Mario; Russo, Ludovico O.; Indaco, Marco; Nerino, Roberto; Chimienti, Antonio; Oddo, Calogero M.; Vitiello, Nicola
2016-01-01
Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master–slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator’s hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers’ hands movements. PMID:26861333
Estimating satellite pose and motion parameters using a novelty filter and neural net tracker
NASA Technical Reports Server (NTRS)
Lee, Andrew J.; Casasent, David; Vermeulen, Pieter; Barnard, Etienne
1989-01-01
A system for determining the position, orientation and motion of a satellite with respect to a robotic spacecraft using video data is advanced. This system utilizes two levels of pose and motion estimation: an initial system which provides coarse estimates of pose and motion, and a second system which uses the coarse estimates and further processing to provide finer pose and motion estimates. The present paper emphasizes the initial coarse pose and motion estimation sybsystem. This subsystem utilizes novelty detection and filtering for locating novel parts and a neural net tracker to track these parts over time. Results of using this system on a sequence of images of a spin stabilized satellite are presented.
Estimation of daily dietary fluoride intake: 3-d food diary v. 2-d duplicate plate.
Omid, N; Maguire, A; O'Hare, W T; Zohoori, F V
2015-12-28
The 3-d food diary method (3-d FD) or the 2-d duplicate plate (2-d DP) method have been used to measure dietary fluoride (F) intake by many studies. This study aimed to compare daily dietary F intake (DDFI) estimated by the 3-d FD and 2-d DP methods at group and individual levels. Dietary data for sixty-one healthy children aged 4-6 years were collected using 3-d FD and 2-d DP methods with a 1-week gap between each collection. Food diary data were analysed for F using the Weighed Intake Analysis Software Package, whereas duplicate diets were analysed by an acid diffusion method using an F ion-selective electrode. Paired t test and linear regression were used to compare dietary data at the group and individual levels, respectively. At the group level, mean DDFI was 0·025 (sd 0·016) and 0·028 (sd 0·013) mg/kg body weight (bw) per d estimated by 3-d FD and 2-d DP, respectively. No statistically significant difference (P=0·10) was observed in estimated DDFI by each method at the group level. At an individual level, the agreement in estimating F intake (mg/kg bw per d) using the 3-d FD method compared with the 2-d DP method was within ±0·011 (95 % CI 0·009, 0·013) mg/kg bw per d. At the group level, DDFI data obtained by either the 2-d DP method or the 3-d FD method can be replaced. At an individual level, the typical error and the narrow margin between optimal and excessive F intake suggested that the DDFI data obtained by one method cannot replace the dietary data estimated from the other method. PMID:26568435
Estimating aquatic hazards posed by prescription pharmaceutical residues from municipal wastewater
Risks posed by pharmaceuticals in the environment are hard to estimate due to limited monitoring capacity and difficulty interpreting monitoring results. In order to partially address these issues, we suggest a method for prioritizing pharmaceuticals for monitoring, and a framewo...
Incorporating structure from motion uncertainty into image-based pose estimation
NASA Astrophysics Data System (ADS)
Ludington, Ben T.; Brown, Andrew P.; Sheffler, Michael J.; Taylor, Clark N.; Berardi, Stephen
2015-05-01
A method for generating and utilizing structure from motion (SfM) uncertainty estimates within image-based pose estimation is presented. The method is applied to a class of problems in which SfM algorithms are utilized to form a geo-registered reference model of a particular ground area using imagery gathered during flight by a small unmanned aircraft. The model is then used to form camera pose estimates in near real-time from imagery gathered later. The resulting pose estimates can be utilized by any of the other onboard systems (e.g. as a replacement for GPS data) or downstream exploitation systems, e.g., image-based object trackers. However, many of the consumers of pose estimates require an assessment of the pose accuracy. The method for generating the accuracy assessment is presented. First, the uncertainty in the reference model is estimated. Bundle Adjustment (BA) is utilized for model generation. While the high-level approach for generating a covariance matrix of the BA parameters is straightforward, typical computing hardware is not able to support the required operations due to the scale of the optimization problem within BA. Therefore, a series of sparse matrix operations is utilized to form an exact covariance matrix for only the parameters that are needed at a particular moment. Once the uncertainty in the model has been determined, it is used to augment Perspective-n-Point pose estimation algorithms to improve the pose accuracy and to estimate the resulting pose uncertainty. The implementation of the described method is presented along with results including results gathered from flight test data.
Human Body 3D Posture Estimation Using Significant Points and Two Cameras
Juang, Chia-Feng; Chen, Teng-Chang; Du, Wei-Chin
2014-01-01
This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures. PMID:24883422
NASA Astrophysics Data System (ADS)
Woods, Jack; Armstrong, Ernest E.; Armbruster, Walter; Richmond, Richard
2010-04-01
The primary purpose of this research was to develop an effective means of creating a 3D terrain map image (point-cloud) in GPS denied regions from a sequence of co-bore sighted visible and 3D LIDAR images. Both the visible and 3D LADAR cameras were hard mounted to a vehicle. The vehicle was then driven around the streets of an abandoned village used as a training facility by the German Army and imagery was collected. The visible and 3D LADAR images were then fused and 3D registration performed using a variation of the Iterative Closest Point (ICP) algorithm. The ICP algorithm is widely used for various spatial and geometric alignment of 3D imagery producing a set of rotation and translation transformations between two 3D images. ICP rotation and translation information obtain from registering the fused visible and 3D LADAR imagery was then used to calculate the x-y plane, range and intensity (xyzi) coordinates of various structures (building, vehicles, trees etc.) along the driven path. The xyzi coordinates information was then combined to create a 3D terrain map (point-cloud). In this paper, we describe the development and application of 3D imaging techniques (most specifically the ICP algorithm) used to improve spatial, range and intensity estimates of imagery collected during urban terrain mapping using a co-bore sighted, commercially available digital video camera with focal plan of 640×480 pixels and a 3D FLASH LADAR. Various representations of the reconstructed point-clouds for the drive through data will also be presented.
Comparative assessment of techniques for initial pose estimation using monocular vision
NASA Astrophysics Data System (ADS)
Sharma, Sumant; D`Amico, Simone
2016-06-01
This work addresses the comparative assessment of initial pose estimation techniques for monocular navigation to enable formation-flying and on-orbit servicing missions. Monocular navigation relies on finding an initial pose, i.e., a coarse estimate of the attitude and position of the space resident object with respect to the camera, based on a minimum number of features from a three dimensional computer model and a single two dimensional image. The initial pose is estimated without the use of fiducial markers, without any range measurements or any apriori relative motion information. Prior work has been done to compare different pose estimators for terrestrial applications, but there is a lack of functional and performance characterization of such algorithms in the context of missions involving rendezvous operations in the space environment. Use of state-of-the-art pose estimation algorithms designed for terrestrial applications is challenging in space due to factors such as limited on-board processing power, low carrier to noise ratio, and high image contrasts. This paper focuses on performance characterization of three initial pose estimation algorithms in the context of such missions and suggests improvements.
Motion field estimation for a dynamic scene using a 3D LiDAR.
Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington
2014-01-01
This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868
Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington
2014-01-01
This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868
Volume estimation of tonsil phantoms using an oral camera with 3D imaging.
Das, Anshuman J; Valdez, Tulio A; Vargas, Jose Arbouin; Saksupapchon, Punyapat; Rachapudi, Pushyami; Ge, Zhifei; Estrada, Julio C; Raskar, Ramesh
2016-04-01
Three-dimensional (3D) visualization of oral cavity and oropharyngeal anatomy may play an important role in the evaluation for obstructive sleep apnea (OSA). Although computed tomography (CT) and magnetic resonance (MRI) imaging are capable of providing 3D anatomical descriptions, this type of technology is not readily available in a clinic setting. Current imaging of the oropharynx is performed using a light source and tongue depressors. For better assessment of the inferior pole of the tonsils and tongue base flexible laryngoscopes are required which only provide a two dimensional (2D) rendering. As a result, clinical diagnosis is generally subjective in tonsillar hypertrophy where current physical examination has limitations. In this report, we designed a hand held portable oral camera with 3D imaging capability to reconstruct the anatomy of the oropharynx in tonsillar hypertrophy where the tonsils get enlarged and can lead to increased airway resistance. We were able to precisely reconstruct the 3D shape of the tonsils and from that estimate airway obstruction percentage and volume of the tonsils in 3D printed realistic models. Our results correlate well with Brodsky's classification of tonsillar hypertrophy as well as intraoperative volume estimations. PMID:27446667
Estimating the complexity of 3D structural models using machine learning methods
NASA Astrophysics Data System (ADS)
Mejía-Herrera, Pablo; Kakurina, Maria; Royer, Jean-Jacques
2016-04-01
Quantifying the complexity of 3D geological structural models can play a major role in natural resources exploration surveys, for predicting environmental hazards or for forecasting fossil resources. This paper proposes a structural complexity index which can be used to help in defining the degree of effort necessary to build a 3D model for a given degree of confidence, and also to identify locations where addition efforts are required to meet a given acceptable risk of uncertainty. In this work, it is considered that the structural complexity index can be estimated using machine learning methods on raw geo-data. More precisely, the metrics for measuring the complexity can be approximated as the difficulty degree associated to the prediction of the geological objects distribution calculated based on partial information on the actual structural distribution of materials. The proposed methodology is tested on a set of 3D synthetic structural models for which the degree of effort during their building is assessed using various parameters (such as number of faults, number of part in a surface object, number of borders, ...), the rank of geological elements contained in each model, and, finally, their level of deformation (folding and faulting). The results show how the estimated complexity in a 3D model can be approximated by the quantity of partial data necessaries to simulated at a given precision the actual 3D model without error using machine learning algorithms.
Volume estimation of tonsil phantoms using an oral camera with 3D imaging
Das, Anshuman J.; Valdez, Tulio A.; Vargas, Jose Arbouin; Saksupapchon, Punyapat; Rachapudi, Pushyami; Ge, Zhifei; Estrada, Julio C.; Raskar, Ramesh
2016-01-01
Three-dimensional (3D) visualization of oral cavity and oropharyngeal anatomy may play an important role in the evaluation for obstructive sleep apnea (OSA). Although computed tomography (CT) and magnetic resonance (MRI) imaging are capable of providing 3D anatomical descriptions, this type of technology is not readily available in a clinic setting. Current imaging of the oropharynx is performed using a light source and tongue depressors. For better assessment of the inferior pole of the tonsils and tongue base flexible laryngoscopes are required which only provide a two dimensional (2D) rendering. As a result, clinical diagnosis is generally subjective in tonsillar hypertrophy where current physical examination has limitations. In this report, we designed a hand held portable oral camera with 3D imaging capability to reconstruct the anatomy of the oropharynx in tonsillar hypertrophy where the tonsils get enlarged and can lead to increased airway resistance. We were able to precisely reconstruct the 3D shape of the tonsils and from that estimate airway obstruction percentage and volume of the tonsils in 3D printed realistic models. Our results correlate well with Brodsky’s classification of tonsillar hypertrophy as well as intraoperative volume estimations. PMID:27446667
Ui, Atsushi; Miyaji, Takamasa
2004-10-15
The best-estimate coupled three-dimensional (3-D) core and thermal-hydraulic code system TRAC-BF1/COS3D has been developed. COS3D, based on a modified one-group neutronic model, is a 3-D core simulator used for licensing analyses and core management of commercial boiling water reactor (BWR) plants in Japan. TRAC-BF1 is a plant simulator based on a two-fluid model. TRAC-BF1/COS3D is a coupled system of both codes, which are connected using a parallel computing tool. This code system was applied to the OECD/NRC BWR Turbine Trip Benchmark. Since the two-group cross-section tables are provided by the benchmark team, COS3D was modified to apply to this specification. Three best-estimate scenarios and four hypothetical scenarios were calculated using this code system. In the best-estimate scenario, the predicted core power with TRAC-BF1/COS3D is slightly underestimated compared with the measured data. The reason seems to be a slight difference in the core boundary conditions, that is, pressure changes and the core inlet flow distribution, because the peak in this analysis is sensitive to them. However, the results of this benchmark analysis show that TRAC-BF1/COS3D gives good precision for the prediction of the actual BWR transient behavior on the whole. Furthermore, the results with the modified one-group model and the two-group model were compared to verify the application of the modified one-group model to this benchmark. This comparison shows that the results of the modified one-group model are appropriate and sufficiently precise.
3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models
Dhou, Salam; Hurwitz, Martina; Mishra, Pankaj; Cai, Weixing; Rottmann, Joerg; Li, Ruijiang; Williams, Christopher; Wagar, Matthew; Berbeco, Ross; Ionascu, Dan; Lewis, John H.
2015-01-01
3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we develop and perform initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and use these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparing to ground truth digital and physical phantom images. The performance of 4DCBCT- and 4DCT- based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms, and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery. PMID:25905722
3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models
NASA Astrophysics Data System (ADS)
Dhou, S.; Hurwitz, M.; Mishra, P.; Cai, W.; Rottmann, J.; Li, R.; Williams, C.; Wagar, M.; Berbeco, R.; Ionascu, D.; Lewis, J. H.
2015-05-01
3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we developed and performed initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and used these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparison to ground truth digital and physical phantom images. The performance of 4DCBCT-based and 4DCT-based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.
Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method
NASA Astrophysics Data System (ADS)
Guerrero, Thomas; Zhang, Geoffrey; Huang, Tzung-Chi; Lin, Kang-Ping
2004-09-01
The purpose of this work was to develop and validate an automated method for intrathoracic tumour motion estimation from breath-hold computed tomography (BH CT) imaging using the three-dimensional optical flow method (3D OFM). A modified 3D OFM algorithm provided 3D displacement vectors for each voxel which were used to map tumour voxels on expiration BH CT onto inspiration BH CT images. A thoracic phantom and simulated expiration/inspiration BH CT pairs were used for validation. The 3D OFM was applied to the measured inspiration and expiration BH CT images from one lung cancer and one oesophageal cancer patient. The resulting displacements were plotted in histogram format and analysed to provide insight regarding the tumour motion. The phantom tumour displacement was measured as 1.20 and 2.40 cm with full-width at tenth maximum (FWTM) for the distribution of displacement estimates of 0.008 and 0.006 cm, respectively. The maximum error of any single voxel's motion estimate was 1.1 mm along the z-dimension or approximately one-third of the z-dimension voxel size. The simulated BH CT pairs revealed an rms error of less than 0.25 mm. The displacement of the oesophageal tumours was nonuniform and up to 1.4 cm, this was a new finding. A lung tumour maximum displacement of 2.4 cm was found in the case evaluated. In conclusion, 3D OFM provided an accurate estimation of intrathoracic tumour motion, with estimated errors less than the voxel dimension in a simulated motion phantom study. Surprisingly, oesophageal tumour motion was large and nonuniform, with greatest motion occurring at the gastro-oesophageal junction. Presented at The IASTED Second International Conference on Biomedical Engineering (BioMED 2004), Innsbruck, Austria, 16-18 February 2004.
Ye, Mao; Shen, Yang; Du, Chao; Pan, Zhigeng; Yang, Ruigang
2016-08-01
In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Benefiting from this probabilistic measurement model, our algorithm requires no explicit point correspondences as opposed to most existing methods. Consequently, our approach is less sensitive to local minimum and handles fast and complex motions well. Moreover, our novel shape adaptation algorithm based on the same probabilistic model automatically captures the shape of the subjects during the dynamic pose estimation process. The personalized shape model in turn improves the tracking accuracy. Furthermore, we propose novel approaches to use either a mesh model or a sphere-set model as the template for both pose and shape estimation under this unified framework. Extensive evaluations on publicly available data sets demonstrate that our method outperforms most state-of-the-art pose estimation algorithms with large margin, especially in the case of challenging motions. Furthermore, our shape estimation method achieves comparable accuracy with state of the arts, yet requires neither statistical shape model nor extra calibration procedure. Our algorithm is not only accurate but also fast, we have implemented the entire processing pipeline on GPU. It can achieve up to 60 frames per second on a middle-range graphics card. PMID:27116732
An eye model for uncalibrated eye gaze estimation under variable head pose
NASA Astrophysics Data System (ADS)
Hnatow, Justin; Savakis, Andreas
2007-04-01
Gaze estimation is an important component of computer vision systems that monitor human activity for surveillance, human-computer interaction, and various other applications including iris recognition. Gaze estimation methods are particularly valuable when they are non-intrusive, do not require calibration, and generalize well across users. This paper presents a novel eye model that is employed for efficiently performing uncalibrated eye gaze estimation. The proposed eye model was constructed from a geometric simplification of the eye and anthropometric data about eye feature sizes in order to circumvent the requirement of calibration procedures for each individual user. The positions of the two eye corners and the midpupil, the distance between the two eye corners, and the radius of the eye sphere are required for gaze angle calculation. The locations of the eye corners and midpupil are estimated via processing following eye detection, and the remaining parameters are obtained from anthropometric data. This eye model is easily extended to estimating eye gaze under variable head pose. The eye model was tested on still images of subjects at frontal pose (0 °) and side pose (34 °). An upper bound of the model's performance was obtained by manually selecting the eye feature locations. The resulting average absolute error was 2.98 ° for frontal pose and 2.87 ° for side pose. The error was consistent across subjects, which indicates that good generalization was obtained. This level of performance compares well with other gaze estimation systems that utilize a calibration procedure to measure eye features.
Hsu, Chi-Pin; Lin, Shang-Chih; Shih, Kao-Shang; Huang, Chang-Hung; Lee, Chian-Her
2014-12-01
After total knee replacement, the model-based Roentgen stereophotogrammetric analysis (RSA) technique has been used to monitor the status of prosthetic wear, misalignment, and even failure. However, the overlap of the prosthetic outlines inevitably increases errors in the estimation of prosthetic poses due to the limited amount of available outlines. In the literature, quite a few studies have investigated the problems induced by the overlapped outlines, and manual adjustment is still the mainstream. This study proposes two methods to automate the image processing of overlapped outlines prior to the pose registration of prosthetic models. The outline-separated method defines the intersected points and segments the overlapped outlines. The feature-recognized method uses the point and line features of the remaining outlines to initiate registration. Overlap percentage is defined as the ratio of overlapped to non-overlapped outlines. The simulated images with five overlapping percentages are used to evaluate the robustness and accuracy of the proposed methods. Compared with non-overlapped images, overlapped images reduce the number of outlines available for model-based RSA calculation. The maximum and root mean square errors for a prosthetic outline are 0.35 and 0.04 mm, respectively. The mean translation and rotation errors are 0.11 mm and 0.18°, respectively. The errors of the model-based RSA results are increased when the overlap percentage is beyond about 9%. In conclusion, both outline-separated and feature-recognized methods can be seamlessly integrated to automate the calculation of rough registration. This can significantly increase the clinical practicability of the model-based RSA technique. PMID:25293422
3D position estimation using an artificial neural network for a continuous scintillator PET detector
NASA Astrophysics Data System (ADS)
Wang, Y.; Zhu, W.; Cheng, X.; Li, D.
2013-03-01
Continuous crystal based PET detectors have features of simple design, low cost, good energy resolution and high detection efficiency. Through single-end readout of scintillation light, direct three-dimensional (3D) position estimation could be another advantage that the continuous crystal detector would have. In this paper, we propose to use artificial neural networks to simultaneously estimate the plane coordinate and DOI coordinate of incident γ photons with detected scintillation light. Using our experimental setup with an ‘8 + 8’ simplified signal readout scheme, the training data of perpendicular irradiation on the front surface and one side surface are obtained, and the plane (x, y) networks and DOI networks are trained and evaluated. The test results show that the artificial neural network for DOI estimation is as effective as for plane estimation. The performance of both estimators is presented by resolution and bias. Without bias correction, the resolution of the plane estimator is on average better than 2 mm and that of the DOI estimator is about 2 mm over the whole area of the detector. With bias correction, the resolution at the edge area for plane estimation or at the end of the block away from the readout PMT for DOI estimation becomes worse, as we expect. The comprehensive performance of the 3D positioning by a neural network is accessed by the experimental test data of oblique irradiations. To show the combined effect of the 3D positioning over the whole area of the detector, the 2D flood images of oblique irradiation are presented with and without bias correction.
Effects of scatter on model parameter estimates in 3D PET studies of the human brain
Cherry, S.R.; Huang, S.C.
1995-08-01
Phantom measurements and simulated data were used to characterize the effects of scatter on 3D PET projection data, reconstructed images and model parameter estimates. Scatter distributions were estimated form studies of the 3D Hoffman brain phantom by the 2D/3D difference method. The total scatter fraction in the projection data was 40%, but reduces to 27% when only those counts within the boundary of the brain are considered. After reconstruction, the whole brain scatter fraction is 20%, averaging 10% in cortical gray matter, 21% in basal ganglia and 40% in white matter. The scatter contribution varies by almost a factor of two from the edge to the center of the brain due to the shape of the scatter distribution and the effects of attenuation correction. The effect of scatter on estimates of cerebral metabolic rate for glucose (CMRGI) and cerebral blood flow (CBF) is evaluated by simulating typical gray matter time activity curves (TAC`s) and adding a scatter component based on whole-brain activity. Both CMRGI and CBF change in a linear fashion with scatter fraction. Efforts of between 10 and 30% will typically result if 3D studies are not corrected for scatter. The authors also present results from a simple and fast scatter correction which fits a gaussian function to the scattered events outside the brain. This reduced the scatter fraction to <2% in a range of phantom studies with different activity distributions. Using this correction, quantitative errors in 3D PET studies of CMRGI and CBF can be reduced to well below 10%.
Xu, Gang; Xing, Mengdao; Xia, Xiang-Gen; Zhang, Lei; Chen, Qianqian; Bao, Zheng
2016-05-01
In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm. PMID:26930684
Solav, Dana; Rubin, M B; Cereatti, Andrea; Camomilla, Valentina; Wolf, Alon
2016-04-01
Accurate estimation of the position and orientation (pose) of a bone from a cluster of skin markers is limited mostly by the relative motion between the bone and the markers, which is known as the soft tissue artifact (STA). This work presents a method, based on continuum mechanics, to describe the kinematics of a cluster affected by STA. The cluster is characterized by triangular cosserat point elements (TCPEs) defined by all combinations of three markers. The effects of the STA on the TCPEs are quantified using three parameters describing the strain in each TCPE and the relative rotation and translation between TCPEs. The method was evaluated using previously collected ex vivo kinematic data. Femur pose was estimated from 12 skin markers on the thigh, while its reference pose was measured using bone pins. Analysis revealed that instantaneous subsets of TCPEs exist which estimate bone position and orientation more accurately than the Procrustes Superimposition applied to the cluster of all markers. It has been shown that some of these parameters correlate well with femur pose errors, which suggests that they can be used to select, at each instant, subsets of TCPEs leading an improved estimation of the underlying bone pose. PMID:26194039
Empirical mode decomposition-based facial pose estimation inside video sequences
NASA Astrophysics Data System (ADS)
Qing, Chunmei; Jiang, Jianmin; Yang, Zhijing
2010-03-01
We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.
Pose Estimation of Unmanned Aerial Vehicles Based on a Vision-Aided Multi-Sensor Fusion
NASA Astrophysics Data System (ADS)
Abdi, G.; Samadzadegan, F.; Kurz, F.
2016-06-01
GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.
Motion-induced phase error estimation and correction in 3D diffusion tensor imaging.
Van, Anh T; Hernando, Diego; Sutton, Bradley P
2011-11-01
A multishot data acquisition strategy is one way to mitigate B0 distortion and T2∗ blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method. PMID:21652284
Estimation of 3D reconstruction errors in a stereo-vision system
NASA Astrophysics Data System (ADS)
Belhaoua, A.; Kohler, S.; Hirsch, E.
2009-06-01
The paper presents an approach for error estimation for the various steps of an automated 3D vision-based reconstruction procedure of manufactured workpieces. The process is based on a priori planning of the task and built around a cognitive intelligent sensory system using so-called Situation Graph Trees (SGT) as a planning tool. Such an automated quality control system requires the coordination of a set of complex processes performing sequentially data acquisition, its quantitative evaluation and the comparison with a reference model (e.g., CAD object model) in order to evaluate quantitatively the object. To ensure efficient quality control, the aim is to be able to state if reconstruction results fulfill tolerance rules or not. Thus, the goal is to evaluate independently the error for each step of the stereo-vision based 3D reconstruction (e.g., for calibration, contour segmentation, matching and reconstruction) and then to estimate the error for the whole system. In this contribution, we analyze particularly the segmentation error due to localization errors for extracted edge points supposed to belong to lines and curves composing the outline of the workpiece under evaluation. The fitting parameters describing these geometric features are used as quality measure to determine confidence intervals and finally to estimate the segmentation errors. These errors are then propagated through the whole reconstruction procedure, enabling to evaluate their effect on the final 3D reconstruction result, specifically on position uncertainties. Lastly, analysis of these error estimates enables to evaluate the quality of the 3D reconstruction, as illustrated by the shown experimental results.
Efficient dense blur map estimation for automatic 2D-to-3D conversion
NASA Astrophysics Data System (ADS)
Vosters, L. P. J.; de Haan, G.
2012-03-01
Focus is an important depth cue for 2D-to-3D conversion of low depth-of-field images and video. However, focus can be only reliably estimated on edges. Therefore, Bea et al. [1] first proposed an optimization based approach to propagate focus to non-edge image portions, for single image focus editing. While their approach produces accurate dense blur maps, the computational complexity and memory requirements for solving the resulting sparse linear system with standard multigrid or (multilevel) preconditioning techniques, are infeasible within the stringent requirements of the consumer electronics and broadcast industry. In this paper we propose fast, efficient, low latency, line scanning based focus propagation, which mitigates the need for complex multigrid or (multilevel) preconditioning techniques. In addition we propose facial blur compensation to compensate for false shading edges that cause incorrect blur estimates in people's faces. In general shading leads to incorrect focus estimates, which may lead to unnatural 3D and visual discomfort. Since visual attention mostly tends to faces, our solution solves the most distracting errors. A subjective assessment by paired comparison on a set of challenging low-depth-of-field images shows that the proposed approach achieves equal 3D image quality as optimization based approaches, and that facial blur compensation results in a significant improvement.
Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae
2012-01-01
Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454
Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae
2012-01-01
Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454
Detecting and estimating errors in 3D restoration methods using analog models.
NASA Astrophysics Data System (ADS)
José Ramón, Ma; Pueyo, Emilio L.; Briz, José Luis
2015-04-01
Some geological scenarios may be important for a number of socio-economic reasons, such as water or energy resources, but the available underground information is often limited, scarce and heterogeneous. A truly 3D reconstruction, which is still necessary during the decision-making process, may have important social and economic implications. For this reason, restoration methods were developed. By honoring some geometric or mechanical laws, they help build a reliable image of the subsurface. Pioneer methods were firstly applied in 2D (balanced and restored cross-sections) during the sixties and seventies. Later on, and due to the improvements of computational capabilities, they were extended to 3D. Currently, there are some academic and commercial restoration solutions; Unfold by the Université de Grenoble, Move by Midland Valley Exploration, Kine3D (on gOcad code) by Paradigm, Dynel3D by igeoss-Schlumberger. We have developed our own restoration method, Pmag3Drest (IGME-Universidad de Zaragoza), which is designed to tackle complex geometrical scenarios using paleomagnetic vectors as a pseudo-3D indicator of deformation. However, all these methods have limitations based on the assumptions they need to establish. For this reason, detecting and estimating uncertainty in 3D restoration methods is of key importance to trust the reconstructions. Checking the reliability and the internal consistency of every method, as well as to compare the results among restoration tools, is a critical issue never tackled so far because of the impossibility to test out the results in Nature. To overcome this problem we have developed a technique using analog models. We built complex geometric models inspired in real cases of superposed and/or conical folding at laboratory scale. The stratigraphic volumes were modeled using EVA sheets (ethylene vinyl acetate). Their rheology (tensile and tear strength, elongation, density etc) and thickness can be chosen among a large number of values
Maximum likelihood estimation of parameterized 3-D surfaces using a moving camera
NASA Technical Reports Server (NTRS)
Hung, Y.; Cernuschi-Frias, B.; Cooper, D. B.
1987-01-01
A new approach is introduced to estimating object surfaces in three-dimensional space from a sequence of images. A surface of interest here is modeled as a 3-D function known up to the values of a few parameters. The approach will work with any parameterization. However, in work to date researchers have modeled objects as patches of spheres, cylinders, and planes - primitive objects. These primitive surfaces are special cases of 3-D quadric surfaces. Primitive surface estimation is treated as the general problem of maximum likelihood parameter estimation based on two or more functionally related data sets. In the present case, these data sets constitute a sequence of images taken at different locations and orientations. A simple geometric explanation is given for the estimation algorithm. Though various techniques can be used to implement this nonlinear estimation, researches discuss the use of gradient descent. Experiments are run and discussed for the case of a sphere of unknown location. These experiments graphically illustrate the various advantages of using as many images as possible in the estimation and of distributing camera positions from first to last over as large a baseline as possible. Researchers introduce the use of asymptotic Bayesian approximations in order to summarize the useful information in a sequence of images, thereby drastically reducing both the storage and amount of processing required.
Toward 3D-guided prostate biopsy target optimization: an estimation of tumor sampling probabilities
NASA Astrophysics Data System (ADS)
Martin, Peter R.; Cool, Derek W.; Romagnoli, Cesare; Fenster, Aaron; Ward, Aaron D.
2014-03-01
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy aims to reduce the ~23% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still yields false negatives. Therefore, we propose optimization of biopsy targeting to meet the clinician's desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. We obtained multiparametric MRI and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy. Given an RMS needle delivery error of 3.5 mm for a contemporary fusion biopsy system, P >= 95% for 21 out of 81 tumors when the point of optimal sampling probability was targeted. Therefore, more than one biopsy core must be taken from 74% of the tumors to achieve P >= 95% for a biopsy system with an error of 3.5 mm. Our experiments indicated that the effect of error along the needle axis on the percentage of core involvement (and thus the measured tumor burden) was mitigated by the 18 mm core length.
Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees.
Omasa, Kenji; Qiu, Guo Yu; Watanuki, Kenichi; Yoshimi, Kenji; Akiyama, Yukihide
2003-03-15
Forests are one of the most important carbon sinks on Earth. However, owing to the complex structure, variable geography, and large area of forests, accurate estimation of forest carbon stocks is still a challenge for both site surveying and remote sensing. For these reasons, the Kyoto Protocol requires the establishment of methodologies for estimating the carbon stocks of forests (Kyoto Protocol, Article 5). A possible solution to this challenge is to remotely measure the carbon stocks of every tree in an entire forest. Here, we present a methodology for estimating carbon stocks of a Japanese cedar forest by using a high-resolution, helicopter-borne 3-dimensional (3-D) scanning lidar system that measures the 3-D canopy structure of every tree in a forest. Results show that a digital image (10-cm mesh) of woody canopy can be acquired. The treetop can be detected automatically with a reasonable accuracy. The absolute error ranges for tree height measurements are within 42 cm. Allometric relationships of height to carbon stocks then permit estimation of total carbon storage by measurement of carbon stocks of every tree. Thus, we suggest that our methodology can be used to accurately estimate the carbon stocks of Japanese cedar forests at a stand scale. Periodic measurements will reveal changes in forest carbon stocks. PMID:12680675
Sun, Pengfei; Sun, Changku; Li, Wenqiang; Wang, Peng
2015-01-01
Pose estimation aims at measuring the position and orientation of a calibrated camera using known image features. The pinhole model is the dominant camera model in this field. However, the imaging precision of this model is not accurate enough for an advanced pose estimation algorithm. In this paper, a new camera model, called incident ray tracking model, is introduced. More importantly, an advanced pose estimation algorithm based on the perspective ray in the new camera model, is proposed. The perspective ray, determined by two positioning points, is an abstract mathematical equivalent of the incident ray. In the proposed pose estimation algorithm, called perspective-ray-based scaled orthographic projection with iteration (PRSOI), an approximate ray-based projection is calculated by a linear system and refined by iteration. Experiments on the PRSOI have been conducted, and the results demonstrate that it is of high accuracy in the six degrees of freedom (DOF) motion. And it outperforms three other state-of-the-art algorithms in terms of accuracy during the contrast experiment. PMID:26197272
3D visualization and biovolume estimation of motile cells by digital holography
NASA Astrophysics Data System (ADS)
Merola, F.; Miccio, L.; Memmolo, P.; Di Caprio, G.; Coppola, G.; Netti, P.
2014-05-01
For the monitoring of biological samples, physical parameters such as size, shape and refractive index are of crucial importance. However, up to now the morphological in-vitro analysis of in-vitro cells has been limited to 2D analysis by classical optical microscopy such as phase-contrast or DIC. Here we show an approach that exploits the capability of optical tweezers to trap and put in self-rotation bovine spermatozoa flowing into a microfluidic channel. At same time, digital holographic microscopy allows to image the cell in phase-contrast modality for each different angular position, during the rotation. From the collected information about the cell's phase-contrast signature, we demonstrate that it is possible to reconstruct the 3D shape of the cell and estimate its volume. The method can open new pathways for rapid measurement of in-vitro cells volume in microfluidic lab-on-a-chip platform, thus having access to 3D shape of the object avoiding tomography microscopy, that is an overwhelmed and very complex approach for measuring 3D shape and biovolume estimation.
Parametric estimation of 3D tubular structures for diffuse optical tomography
Larusson, Fridrik; Anderson, Pamela G.; Rosenberg, Elizabeth; Kilmer, Misha E.; Sassaroli, Angelo; Fantini, Sergio; Miller, Eric L.
2013-01-01
We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction. PMID:23411913
3D Porosity Estimation of the Nankai Trough Sediments from Core-log-seismic Integration
NASA Astrophysics Data System (ADS)
Park, J. O.
2015-12-01
The Nankai Trough off southwest Japan is one of the best subduction-zone to study megathrust earthquake fault. Historic, great megathrust earthquakes with a recurrence interval of 100-200 yr have generated strong motion and large tsunamis along the Nankai Trough subduction zone. At the Nankai Trough margin, the Philippine Sea Plate (PSP) is being subducted beneath the Eurasian Plate to the northwest at a convergence rate ~4 cm/yr. The Shikoku Basin, the northern part of the PSP, is estimated to have opened between 25 and 15 Ma by backarc spreading of the Izu-Bonin arc. The >100-km-wide Nankai accretionary wedge, which has developed landward of the trench since the Miocene, mainly consists of offscraped and underplated materials from the trough-fill turbidites and the Shikoku Basin hemipelagic sediments. Particularly, physical properties of the incoming hemipelagic sediments may be critical for seismogenic behavior of the megathrust fault. We have carried out core-log-seismic integration (CLSI) to estimate 3D acoustic impedance and porosity for the incoming sediments in the Nankai Trough. For the CLSI, we used 3D seismic reflection data, P-wave velocity and density data obtained during IODP (Integrated Ocean Drilling Program) Expeditions 322 and 333. We computed acoustic impedance depth profiles for the IODP drilling sites from P-wave velocity and density data. We constructed seismic convolution models with the acoustic impedance profiles and a source wavelet which is extracted from the seismic data, adjusting the seismic models to observed seismic traces with inversion method. As a result, we obtained 3D acoustic impedance volume and then converted it to 3D porosity volume. In general, the 3D porosities show decrease with depth. We found a porosity anomaly zone with alteration of high and low porosities seaward of the trough axis. In this talk, we will show detailed 3D porosity of the incoming sediments, and present implications of the porosity anomaly zone for the
A multi-camera system for real-time pose estimation
NASA Astrophysics Data System (ADS)
Savakis, Andreas; Erhard, Matthew; Schimmel, James; Hnatow, Justin
2007-04-01
This paper presents a multi-camera system that performs face detection and pose estimation in real-time and may be used for intelligent computing within a visual sensor network for surveillance or human-computer interaction. The system consists of a Scene View Camera (SVC), which operates at a fixed zoom level, and an Object View Camera (OVC), which continuously adjusts its zoom level to match objects of interest. The SVC is set to survey the whole filed of view. Once a region has been identified by the SVC as a potential object of interest, e.g. a face, the OVC zooms in to locate specific features. In this system, face candidate regions are selected based on skin color and face detection is accomplished using a Support Vector Machine classifier. The locations of the eyes and mouth are detected inside the face region using neural network feature detectors. Pose estimation is performed based on a geometrical model, where the head is modeled as a spherical object that rotates upon the vertical axis. The triangle formed by the mouth and eyes defines a vertical plane that intersects the head sphere. By projecting the eyes-mouth triangle onto a two dimensional viewing plane, equations were obtained that describe the change in its angles as the yaw pose angle increases. These equations are then combined and used for efficient pose estimation. The system achieves real-time performance for live video input. Testing results assessing system performance are presented for both still images and video.
Robust 3D Position Estimation in Wide and Unconstrained Indoor Environments
Mossel, Annette
2015-01-01
In this paper, a system for 3D position estimation in wide, unconstrained indoor environments is presented that employs infrared optical outside-in tracking of rigid-body targets with a stereo camera rig. To overcome limitations of state-of-the-art optical tracking systems, a pipeline for robust target identification and 3D point reconstruction has been investigated that enables camera calibration and tracking in environments with poor illumination, static and moving ambient light sources, occlusions and harsh conditions, such as fog. For evaluation, the system has been successfully applied in three different wide and unconstrained indoor environments, (1) user tracking for virtual and augmented reality applications, (2) handheld target tracking for tunneling and (3) machine guidance for mining. The results of each use case are discussed to embed the presented approach into a larger technological and application context. The experimental results demonstrate the system’s capabilities to track targets up to 100 m. Comparing the proposed approach to prior art in optical tracking in terms of range coverage and accuracy, it significantly extends the available tracking range, while only requiring two cameras and providing a relative 3D point accuracy with sub-centimeter deviation up to 30 m and low-centimeter deviation up to 100 m. PMID:26694388
A Multi-Task Learning Framework for Head Pose Estimation under Target Motion.
Yan, Yan; Ricci, Elisa; Subramanian, Ramanathan; Liu, Gaowen; Lanz, Oswald; Sebe, Nicu
2016-06-01
Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings. PMID:26372209
Efficient 3D movement-based kernel density estimator and application to wildlife ecology
Tracey-PR, Jeff; Sheppard, James K.; Lockwood, Glenn K.; Chourasia, Amit; Tatineni, Mahidhar; Fisher, Robert N.; Sinkovits, Robert S.
2014-01-01
We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.
Image-driven, model-based 3D abdominal motion estimation for MR-guided radiotherapy
NASA Astrophysics Data System (ADS)
Stemkens, Bjorn; Tijssen, Rob H. N.; de Senneville, Baudouin Denis; Lagendijk, Jan J. W.; van den Berg, Cornelis A. T.
2016-07-01
Respiratory motion introduces substantial uncertainties in abdominal radiotherapy for which traditionally large margins are used. The MR-Linac will open up the opportunity to acquire high resolution MR images just prior to radiation and during treatment. However, volumetric MRI time series are not able to characterize 3D tumor and organ-at-risk motion with sufficient temporal resolution. In this study we propose a method to estimate 3D deformation vector fields (DVFs) with high spatial and temporal resolution based on fast 2D imaging and a subject-specific motion model based on respiratory correlated MRI. In a pre-beam phase, a retrospectively sorted 4D-MRI is acquired, from which the motion is parameterized using a principal component analysis. This motion model is used in combination with fast 2D cine-MR images, which are acquired during radiation, to generate full field-of-view 3D DVFs with a temporal resolution of 476 ms. The geometrical accuracies of the input data (4D-MRI and 2D multi-slice acquisitions) and the fitting procedure were determined using an MR-compatible motion phantom and found to be 1.0–1.5 mm on average. The framework was tested on seven healthy volunteers for both the pancreas and the kidney. The calculated motion was independently validated using one of the 2D slices, with an average error of 1.45 mm. The calculated 3D DVFs can be used retrospectively for treatment simulations, plan evaluations, or to determine the accumulated dose for both the tumor and organs-at-risk on a subject-specific basis in MR-guided radiotherapy.
Image-driven, model-based 3D abdominal motion estimation for MR-guided radiotherapy.
Stemkens, Bjorn; Tijssen, Rob H N; de Senneville, Baudouin Denis; Lagendijk, Jan J W; van den Berg, Cornelis A T
2016-07-21
Respiratory motion introduces substantial uncertainties in abdominal radiotherapy for which traditionally large margins are used. The MR-Linac will open up the opportunity to acquire high resolution MR images just prior to radiation and during treatment. However, volumetric MRI time series are not able to characterize 3D tumor and organ-at-risk motion with sufficient temporal resolution. In this study we propose a method to estimate 3D deformation vector fields (DVFs) with high spatial and temporal resolution based on fast 2D imaging and a subject-specific motion model based on respiratory correlated MRI. In a pre-beam phase, a retrospectively sorted 4D-MRI is acquired, from which the motion is parameterized using a principal component analysis. This motion model is used in combination with fast 2D cine-MR images, which are acquired during radiation, to generate full field-of-view 3D DVFs with a temporal resolution of 476 ms. The geometrical accuracies of the input data (4D-MRI and 2D multi-slice acquisitions) and the fitting procedure were determined using an MR-compatible motion phantom and found to be 1.0-1.5 mm on average. The framework was tested on seven healthy volunteers for both the pancreas and the kidney. The calculated motion was independently validated using one of the 2D slices, with an average error of 1.45 mm. The calculated 3D DVFs can be used retrospectively for treatment simulations, plan evaluations, or to determine the accumulated dose for both the tumor and organs-at-risk on a subject-specific basis in MR-guided radiotherapy. PMID:27362636
NASA Astrophysics Data System (ADS)
Chakraborty, Bidisha; Heyde, Brecht; Alessandrini, Martino; D'hooge, Jan
2016-04-01
Image registration techniques using free-form deformation models have shown promising results for 3D myocardial strain estimation from ultrasound. However, the use of this technique has mostly been limited to research institutes due to the high computational demand, which is primarily due to the computational load of the regularization term ensuring spatially smooth cardiac strain estimates. Indeed, this term typically requires evaluating derivatives of the transformation field numerically in each voxel of the image during every iteration of the optimization process. In this paper, we replace this time-consuming step with a closed-form solution directly associated with the transformation field resulting in a speed up factor of ~10-60,000, for a typical 3D B-mode image of 2503 and 5003 voxels, depending upon the size and the parametrization of the transformation field. The performance of the numeric and the analytic solutions was contrasted by computing tracking and strain accuracy on two realistic synthetic 3D cardiac ultrasound sequences, mimicking two ischemic motion patterns. Mean and standard deviation of the displacement errors over the cardiac cycle for the numeric and analytic solutions were 0.68+/-0.40 mm and 0.75+/-0.43 mm respectively. Correlations for the radial, longitudinal and circumferential strain components at end-systole were 0.89, 0.83 and 0.95 versus 0.90, 0.88 and 0.92 for the numeric and analytic regularization respectively. The analytic solution matched the performance of the numeric solution as no statistically significant differences (p>0.05) were found when expressed in terms of bias or limits-of-agreement.
Accuracy Evaluation for a Precise Indoor Multi-Camera Pose Estimation System
NASA Astrophysics Data System (ADS)
Götz, C.; Tuttas, S.; Hoegner, L.; Eder, K.; Stilla, U.
2011-04-01
Pose estimation is used for different applications like indoor positioning, simultaneous localization and mapping (SLAM), industrial measurement and robot calibration. For industrial applications several approaches dealing with the subject of pose estimation employ photogrammetric methods. Cameras which observe an object from a given point of view are utilized as well as cameras which are firmly mounted on the object that is to be oriented. Since it is not always possible to create an environment that the camera can observe the object, we concentrate on the latter option. A camera system shall be developed which is flexibly applicable in an indoor environment, and can cope with different occlusion situations, varying distances and density of reference marks. For this purpose in a first step a conception has been designed and a test scenario was created to evaluate different camera configurations and reference mark distributions. Both issues, the theoretical concept as well as the experimental setup are subject of this document.
A Simple Interface for 3D Position Estimation of a Mobile Robot with Single Camera.
Chao, Chun-Tang; Chung, Ming-Hsuan; Chiou, Juing-Shian; Wang, Chi-Jo
2016-01-01
In recent years, there has been an increase in the number of mobile robots controlled by a smart phone or tablet. This paper proposes a visual control interface for a mobile robot with a single camera to easily control the robot actions and estimate the 3D position of a target. In this proposal, the mobile robot employed an Arduino Yun as the core processor and was remote-controlled by a tablet with an Android operating system. In addition, the robot was fitted with a three-axis robotic arm for grasping. Both the real-time control signal and video transmission are transmitted via Wi-Fi. We show that with a properly calibrated camera and the proposed prototype procedures, the users can click on a desired position or object on the touchscreen and estimate its 3D coordinates in the real world by simple analytic geometry instead of a complicated algorithm. The results of the measurement verification demonstrates that this approach has great potential for mobile robots. PMID:27023556
A Simple Interface for 3D Position Estimation of a Mobile Robot with Single Camera
Chao, Chun-Tang; Chung, Ming-Hsuan; Chiou, Juing-Shian; Wang, Chi-Jo
2016-01-01
In recent years, there has been an increase in the number of mobile robots controlled by a smart phone or tablet. This paper proposes a visual control interface for a mobile robot with a single camera to easily control the robot actions and estimate the 3D position of a target. In this proposal, the mobile robot employed an Arduino Yun as the core processor and was remote-controlled by a tablet with an Android operating system. In addition, the robot was fitted with a three-axis robotic arm for grasping. Both the real-time control signal and video transmission are transmitted via Wi-Fi. We show that with a properly calibrated camera and the proposed prototype procedures, the users can click on a desired position or object on the touchscreen and estimate its 3D coordinates in the real world by simple analytic geometry instead of a complicated algorithm. The results of the measurement verification demonstrates that this approach has great potential for mobile robots. PMID:27023556
3D global estimation and augmented reality visualization of intra-operative X-ray dose.
Rodas, Nicolas Loy; Padoy, Nicolas
2014-01-01
The growing use of image-guided minimally-invasive surgical procedures is confronting clinicians and surgical staff with new radiation exposure risks from X-ray imaging devices. The accurate estimation of intra-operative radiation exposure can increase staff awareness of radiation exposure risks and enable the implementation of well-adapted safety measures. The current surgical practice of wearing a single dosimeter at chest level to measure radiation exposure does not provide a sufficiently accurate estimation of radiation absorption throughout the body. In this paper, we propose an approach that combines data from wireless dosimeters with the simulation of radiation propagation in order to provide a global radiation risk map in the area near the X-ray device. We use a multi-camera RGBD system to obtain a 3D point cloud reconstruction of the room. The positions of the table, C-arm and clinician are then used 1) to simulate the propagation of radiation in a real-world setup and 2) to overlay the resulting 3D risk-map onto the scene in an augmented reality manner. By using real-time wireless dosimeters in our system, we can both calibrate the simulation and validate its accuracy at specific locations in real-time. We demonstrate our system in an operating room equipped with a robotised X-ray imaging device and validate the radiation simulation on several X-ray acquisition setups. PMID:25333145
Learning a Tracking and Estimation Integrated Graphical Model for Human Pose Tracking.
Zhao, Lin; Gao, Xinbo; Tao, Dacheng; Li, Xuelong
2015-12-01
We investigate the tracking of 2-D human poses in a video stream to determine the spatial configuration of body parts in each frame, but this is not a trivial task because people may wear different kinds of clothing and may move very quickly and unpredictably. The technology of pose estimation is typically applied, but it ignores the temporal context and cannot provide smooth, reliable tracking results. Therefore, we develop a tracking and estimation integrated model (TEIM) to fully exploit temporal information by integrating pose estimation with visual tracking. However, joint parsing of multiple articulated parts over time is difficult, because a full model with edges capturing all pairwise relationships within and between frames is loopy and intractable. In previous models, approximate inference was usually resorted to, but it cannot promise good results and the computational cost is large. We overcome these problems by exploring the idea of divide and conquer, which decomposes the full model into two much simpler tractable submodels. In addition, a novel two-step iteration strategy is proposed to efficiently conquer the joint parsing problem. Algorithmically, we design TEIM very carefully so that: 1) it enables pose estimation and visual tracking to compensate for each other to achieve desirable tracking results; 2) it is able to deal with the problem of tracking loss; and 3) it only needs past information and is capable of tracking online. Experiments are conducted on two public data sets in the wild with ground truth layout annotations, and the experimental results indicate the effectiveness of the proposed TEIM framework. PMID:25826809
NASA Astrophysics Data System (ADS)
Fu, Deqian; Gao, Lisheng; Jhang, Seong Tae
2012-04-01
The mobile computing device has many limitations, such as relative small user interface and slow computing speed. Usually, augmented reality requires face pose estimation can be used as a HCI and entertainment tool. As far as the realtime implementation of head pose estimation on relatively resource limited mobile platforms is concerned, it is required to face different constraints while leaving enough face pose estimation accuracy. The proposed face pose estimation method met this objective. Experimental results running on a testing Android mobile device delivered satisfactory performing results in the real-time and accurately.
On-line 3D motion estimation using low resolution MRI
NASA Astrophysics Data System (ADS)
Glitzner, M.; de Senneville, B. Denis; Lagendijk, J. J. W.; Raaymakers, B. W.; Crijns, S. P. M.
2015-08-01
Image processing such as deformable image registration finds its way into radiotherapy as a means to track non-rigid anatomy. With the advent of magnetic resonance imaging (MRI) guided radiotherapy, intrafraction anatomy snapshots become technically feasible. MRI provides the needed tissue signal for high-fidelity image registration. However, acquisitions, especially in 3D, take a considerable amount of time. Pushing towards real-time adaptive radiotherapy, MRI needs to be accelerated without degrading the quality of information. In this paper, we investigate the impact of image resolution on the quality of motion estimations. Potentially, spatially undersampled images yield comparable motion estimations. At the same time, their acquisition times would reduce greatly due to the sparser sampling. In order to substantiate this hypothesis, exemplary 4D datasets of the abdomen were downsampled gradually. Subsequently, spatiotemporal deformations are extracted consistently using the same motion estimation for each downsampled dataset. Errors between the original and the respectively downsampled version of the dataset are then evaluated. Compared to ground-truth, results show high similarity of deformations estimated from downsampled image data. Using a dataset with {{≤ft(2.5 \\text{mm}\\right)}3} voxel size, deformation fields could be recovered well up to a downsampling factor of 2, i.e. {{≤ft(5 \\text{mm}\\right)}3} . In a therapy guidance scenario MRI, imaging speed could accordingly increase approximately fourfold, with acceptable loss of estimated motion quality.
Pose and Motion Estimation Using Dual Quaternion-Based Extended Kalman Filtering
Goddard, J.S.; Abidi, M.A.
1998-06-01
A solution to the remote three-dimensional (3-D) measurement problem is presented for a dynamic system given a sequence of two-dimensional (2-D) intensity images of a moving object. The 3-D transformation is modeled as a nonlinear stochastic system with the state estimate providing the six-degree-of-freedom motion and position values as well as structure. The stochastic model uses the iterated extended Kalman filter (IEKF) as a nonlinear estimator and a screw representation of the 3-D transformation based on dual quaternions. Dual quaternions, whose elements are dual numbers, provide a means to represent both rotation and translation in a unified notation. Linear object features, represented as dual vectors, are transformed using the dual quaternion transformation and are then projected to linear features in the image plane. The method has been implemented and tested with both simulated and actual experimental data. Simulation results are provided, along with comparisons to a point-based IEKF method using rotation and translation, to show the relative advantages of this method. Experimental results from testing using a camera mounted on the end effector of a robot arm are also given.
Pose and motion estimation using dual quaternion-based extended Kalman filtering
NASA Astrophysics Data System (ADS)
Goddard, J. S.; Abidi, Mongi A.
1998-03-01
A solution to the remote three-dimensional (3-D) measurement problem is presented for a dynamic system given a sequence of two-dimensional (2-D) intensity images of a moving object. The 3-D transformation is modeled as a nonlinear stochastic system with the state estimate providing the six-degree-of-freedom motion and position values as well as structure. The stochastic model uses the iterated extended Kalman filter (IEKF) as a nonlinear estimator and a screw representation of the 3-D transformation based on dual quaternions. Dual quaternions, whose elements are dual numbers, provide a means to represent both rotation and translation in a unified notation. Linear object features, represented as dual vectors, are transformed using the dual quaternion transformation and are then projected to linear features in the image plane. The method has been implemented and tested with both simulated and actual experimental data. Simulation results are provided, along with comparisons to a point-based IEKF method using rotation and translation, to show the relative advantages of this method. Experimental results from testing using a camera mounted on the end effector of a robot arm are also given.
Object recognition and pose estimation of planar objects from range data
NASA Technical Reports Server (NTRS)
Pendleton, Thomas W.; Chien, Chiun Hong; Littlefield, Mark L.; Magee, Michael
1994-01-01
The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and
Bersvendsen, Jorn; Orderud, Fredrik; Massey, Richard John; Fosså, Kristian; Gerard, Olivier; Urheim, Stig; Samset, Eigil
2016-01-01
As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We represent the RV geometry by a Doo-Sabin subdivision surface with deformation modes derived from a training set of manual segmentations. The segmentation is then represented as a state estimation problem and solved with an extended Kalman filter by combining the RV geometry with a motion model and edge detection. Validation was performed by comparing surface-surface distances, volumes and ejection fractions in 17 patients with aortic insufficiency between the proposed method, magnetic resonance imaging (MRI), and a manual echocardiographic reference. The algorithm was efficient with a mean computation time of 2.0 s. The mean absolute distances between the proposed and manual segmentations were 3.6 ± 0.7 mm. Good agreements of end diastolic volume, end systolic volume and ejection fraction with respect to MRI ( -26±24 mL , -16±26 mL and 0 ± 10%, respectively) and a manual echocardiographic reference (7 ± 30 mL, 13 ± 17 mL and -5±7% , respectively) were observed. PMID:26168434
Estimation of foot pressure from human footprint depths using 3D scanner
NASA Astrophysics Data System (ADS)
Wibowo, Dwi Basuki; Haryadi, Gunawan Dwi; Priambodo, Agus
2016-03-01
The analysis of normal and pathological variation in human foot morphology is central to several biomedical disciplines, including orthopedics, orthotic design, sports sciences, and physical anthropology, and it is also important for efficient footwear design. A classic and frequently used approach to study foot morphology is analysis of the footprint shape and footprint depth. Footprints are relatively easy to produce and to measure, and they can be preserved naturally in different soils. In this study, we need to correlate footprint depth with corresponding foot pressure of individual using 3D scanner. Several approaches are used for modeling and estimating footprint depths and foot pressures. The deepest footprint point is calculated from z max coordinate-z min coordinate and the average of foot pressure is calculated from GRF divided to foot area contact and identical with the average of footprint depth. Evaluation of footprint depth was found from importing 3D scanner file (dxf) in AutoCAD, the z-coordinates than sorted from the highest to the lowest value using Microsoft Excel to make footprinting depth in difference color. This research is only qualitatif study because doesn't use foot pressure device as comparator, and resulting the maximum pressure on calceneus is 3.02 N/cm2, lateral arch is 3.66 N/cm2, and metatarsal and hallux is 3.68 N/cm2.
NASA Astrophysics Data System (ADS)
Lao, Yi; Gajawelli, Niharika; Haas, Lauren; Wilkins, Bryce; Hwang, Darryl; Tsao, Sinchai; Wang, Yalin; Law, Meng; Leporé, Natasha
2014-03-01
Mild traumatic brain injury (MTBI) or concussive injury affects 1.7 million Americans annually, of which 300,000 are due to recreational activities and contact sports, such as football, rugby, and boxing[1]. Finding the neuroanatomical correlates of brain TBI non-invasively and precisely is crucial for diagnosis and prognosis. Several studies have shown the in influence of traumatic brain injury (TBI) on the integrity of brain WM [2-4]. The vast majority of these works focus on athletes with diagnosed concussions. However, in contact sports, athletes are subjected to repeated hits to the head throughout the season, and we hypothesize that these have an influence on white matter integrity. In particular, the corpus callosum (CC), as a small structure connecting the brain hemispheres, may be particularly affected by torques generated by collisions, even in the absence of full blown concussions. Here, we use a combined surface-based morphometry and relative pose analyses, applying on the point distribution model (PDM) of the CC, to investigate TBI related brain structural changes between 9 pre-season and 9 post-season contact sport athlete MRIs. All the data are fed into surface based morphometry analysis and relative pose analysis. The former looks at surface area and thickness changes between the two groups, while the latter consists of detecting the relative translation, rotation and scale between them.
Joint azimuth and elevation localization estimates in 3D synthetic aperture radar scenarios
NASA Astrophysics Data System (ADS)
Pepin, Matthew
2015-05-01
The location of point scatterers in Synthetic Aperture Radar (SAR) data is exploited in several modern analyzes including persistent scatter tracking, terrain deformation, and object identification. The changes in scatterers over time (pulse-to-pulse including vibration and movement, or pass-to-pass including direct follow on, time of day, and season), can be used to draw more information about the data collection. Multiple pass and multiple antenna SAR scenarios have extended these analyzes to location in three dimensions. Either multiple passes at different elevation angles may be .own or an antenna array with an elevation baseline performs a single pass. Parametric spectral estimation in each dimension allows sub-pixel localization of point scatterers in some cases additionally exploiting the multiple samples in each cross dimension. The accuracy of parametric estimation is increased when several azimuth passes or elevations (snapshots) are summed to mitigate measurement noise. Inherent range curvature across the aperture however limits the accuracy in the range dimension to that attained from a single pulse. Unlike the stationary case where radar returns may be averaged the movement necessary to create the synthetic aperture is only approximately (to pixel level accuracy) removed to form SAR images. In parametric estimation increased accuracy is attained when two dimensions are used to jointly estimate locations. This paper involves jointly estimating azimuth and elevation to attain increased accuracy 3D location estimates. In this way the full 2D array of azimuth and elevation samples is used to obtain the maximum possible accuracy. In addition the independent dimension collection geometry requires choosing which dimension azimuth or elevation attains the highest accuracy while joint estimation increases accuracy in both dimensions. When maximum parametric estimation accuracy in azimuth is selected the standard interferometric SAR scenario results. When
Estimation of single cell volume from 3D confocal images using automatic data processing
NASA Astrophysics Data System (ADS)
Chorvatova, A.; Cagalinec, M.; Mateasik, A.; Chorvat, D., Jr.
2012-06-01
Cardiac cells are highly structured with a non-uniform morphology. Although precise estimation of their volume is essential for correct evaluation of hypertrophic changes of the heart, simple and unified techniques that allow determination of the single cardiomyocyte volume with sufficient precision are still limited. Here, we describe a novel approach to assess the cell volume from confocal microscopy 3D images of living cardiac myocytes. We propose a fast procedure based on segementation using active deformable contours. This technique is independent on laser gain and/or pinhole settings and it is also applicable on images of cells stained with low fluorescence markers. Presented approach is a promising new tool to investigate changes in the cell volume during normal, as well as pathological growth, as we demonstrate in the case of cell enlargement during hypertension in rats.
Unassisted 3D camera calibration
NASA Astrophysics Data System (ADS)
Atanassov, Kalin; Ramachandra, Vikas; Nash, James; Goma, Sergio R.
2012-03-01
With the rapid growth of 3D technology, 3D image capture has become a critical part of the 3D feature set on mobile phones. 3D image quality is affected by the scene geometry as well as on-the-device processing. An automatic 3D system usually assumes known camera poses accomplished by factory calibration using a special chart. In real life settings, pose parameters estimated by factory calibration can be negatively impacted by movements of the lens barrel due to shaking, focusing, or camera drop. If any of these factors displaces the optical axes of either or both cameras, vertical disparity might exceed the maximum tolerable margin and the 3D user may experience eye strain or headaches. To make 3D capture more practical, one needs to consider unassisted (on arbitrary scenes) calibration. In this paper, we propose an algorithm that relies on detection and matching of keypoints between left and right images. Frames containing erroneous matches, along with frames with insufficiently rich keypoint constellations, are detected and discarded. Roll, pitch yaw , and scale differences between left and right frames are then estimated. The algorithm performance is evaluated in terms of the remaining vertical disparity as compared to the maximum tolerable vertical disparity.
3D viscosity maps for Greenland and effect on GRACE mass balance estimates
NASA Astrophysics Data System (ADS)
van der Wal, Wouter; Xu, Zheng
2016-04-01
The GRACE satellite mission measures mass loss of the Greenland ice sheet. To correct for glacial isostatic adjustment numerical models are used. Although generally found to be a small signal, the full range of possible GIA models has not been explored yet. In particular, low viscosities due to a wet mantle and high temperatures due to the nearby Iceland hotspot could have a significant effect on GIA gravity rates. The goal of this study is to present a range of possible viscosity maps, and investigate the effect on GRACE mass balance estimates. Viscosity is derived using flow laws for olivine. Mantle temperature is computed from global seismology models, based on temperature derivatives for different mantle compositions. An indication for grain sizes is obtained by xenolith findings at a few locations. We also investigate the weakening effect of the presence of melt. To calculate gravity rates, we use a finite-element GIA model with the 3D viscosity maps and the ICE-5G loading history. GRACE mass balances for mascons in Greenland are derived with a least-squares inversion, using separate constraints for the inland and coastal areas in Greenland. Biases in the least-squares inversion are corrected using scale factors estimated from a simulation based on a surface mass balance model (Xu et al., submitted to The Cryosphere). Model results show enhanced gravity rates in the west and south of Greenland with 3D viscosity maps, compared to GIA models with 1D viscosity. The effect on regional mass balance is up to 5 Gt/year. Regional low viscosity can make present-day gravity rates sensitivity to ice thickness changes in the last decades. Therefore, an improved ice loading history for these time scales is needed.
NASA Astrophysics Data System (ADS)
Arroucau, Pierre; Custódio, Susana
2015-04-01
Solving inverse problems requires an estimate of data uncertainties. This usually takes the form of a data covariance matrix, which determines the shape of the model posterior distribution. Those uncertainties are yet not always known precisely and it is common practice to simply set them to a fixed, reasonable value. In the case of earthquake location, the hypocentral parameters (longitude, latitude, depth and origin time) are typically inverted for using seismic phase arrival times. But quantitative data variance estimates are rarely provided. Instead, arrival time catalogs usually associate phase picks with a quality factor, which is subsequently interpreted more or less arbitrarily in terms of data uncertainty in the location procedure. Here, we present a hierarchical Bayesian algorithm for earthquake location in 3D heterogeneous media, in which not only the earthquake hypocentral parameters, but also the P- and S-wave arrival time uncertainties, are inverted for, hence allowing more realistic posterior model covariance estimates. Forward modeling is achieved by means of the Fast Marching Method (FMM), an eikonal solver which has the ability to take interfaces into account, so direct, reflected and refracted phases can be used in the inversion. We illustrate the ability of our algorithm to retrieve earthquake hypocentral parameters as well as data uncertainties through synthetic examples and using a subset of arrival time catalogs for mainland Portugal and its Atlantic margin.
NASA Astrophysics Data System (ADS)
Arroucau, P.; Custodio, S.
2014-12-01
Solving inverse problems requires an estimate of data uncertainties. This usually takes the form of a data covariance matrix, which determines the shape of the model posterior distribution. Those uncertainties are yet not always known precisely and it is common practice to simply set them to a fixed, reasonable value. In the case of earthquake location, the hypocentral parameters (longitude, latitude, depth and origin time) are typically inverted for using seismic phase arrival times. But quantitative data variance estimates are rarely provided. Instead, arrival time catalogs usually associate phase picks with a quality factor, which is subsequently interpreted more or less arbitrarily in terms of data uncertainty in the location procedure. Here, we present a hierarchical Bayesian algorithm for earthquake location in 3D heterogeneous media, in which not only the earthquake hypocentral parameters, but also the P- and S-wave arrival time uncertainties, are inverted for, hence allowing more realistic posterior model covariance estimates. Forward modeling is achieved by means of the Fast Marching Method (FMM), an eikonal solver which has the ability to take interfaces into account, so direct, reflected and refracted phases can be used in the inversion. We illustrate the ability of our algorithm to retrieve earthquake hypocentral parameters as well as data uncertainties through synthetic examples and using a subset of arrival time catalogs for mainland Portugal and its Atlantic margin.
A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators
Ligorio, Gabriele; Sabatini, Angelo Maria
2015-01-01
In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented. PMID:26703603
Correlation techniques as applied to pose estimation in space station docking
NASA Astrophysics Data System (ADS)
Rollins, John M.; Juday, Richard D.; Monroe, Stanley E., Jr.
2002-08-01
The telerobotic assembly of space-station components has become the method of choice for the International Space Station (ISS) because it offers a safe alternative to the more hazardous option of space walks. The disadvantage of telerobotic assembly is that it does not necessarily provide for direct arbitrary views of mating interfaces for the teleoperator. Unless cameras are present very close to the interface positions, such views must be generated graphically, based on calculated pose relationships derived from images. To assist in this photogrammetric pose estimation, circular targets, or spots, of high contrast have been affixed on each connecting module at carefully surveyed positions. The appearance of a subset of spots must form a constellation of specific relative positions in the incoming image stream in order for the docking to proceed. Spot positions are expressed in terms of their apparent centroids in an image. The precision of centroid estimation is required to be as fine as 1/20th pixel, in some cases. This paper presents an approach to spot centroid estimation using cross correlation between spot images and synthetic spot models of precise centration. Techniques for obtaining sub-pixel accuracy and for shadow and lighting irregularity compensation are discussed.
A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.
Ligorio, Gabriele; Sabatini, Angelo Maria
2015-01-01
In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented. PMID:26703603
Correlation Techniques as Applied to Pose Estimation in Space Station Docking
NASA Technical Reports Server (NTRS)
Rollins, J. Michael; Juday, Richard D.; Monroe, Stanley E., Jr.
2002-01-01
The telerobotic assembly of space-station components has become the method of choice for the International Space Station (ISS) because it offers a safe alternative to the more hazardous option of space walks. The disadvantage of telerobotic assembly is that it does not provide for direct arbitrary views of mating interfaces for the teleoperator. Unless cameras are present very close to the interface positions, such views must be generated graphically, based on calculated pose relationships derived from images. To assist in this photogrammetric pose estimation, circular targets, or spots, of high contrast have been affixed on each connecting module at carefully surveyed positions. The appearance of a subset of spots essentially must form a constellation of specific relative positions in the incoming digital image stream in order for the docking to proceed. Spot positions are expressed in terms of their apparent centroids in an image. The precision of centroid estimation is required to be as fine as 1I20th pixel, in some cases. This paper presents an approach to spot centroid estimation using cross correlation between spot images and synthetic spot models of precise centration. Techniques for obtaining sub-pixel accuracy and for shadow, obscuration and lighting irregularity compensation are discussed.
Comparison of different methods for gender estimation from face image of various poses
NASA Astrophysics Data System (ADS)
Ishii, Yohei; Hongo, Hitoshi; Niwa, Yoshinori; Yamamoto, Kazuhiko
2003-04-01
Recently, gender estimation from face images has been studied for frontal facial images. However, it is difficult to obtain such facial images constantly in the case of application systems for security, surveillance and marketing research. In order to build such systems, a method is required to estimate gender from the image of various facial poses. In this paper, three different classifiers are compared in appearance-based gender estimation, which use four directional features (FDF). The classifiers are linear discriminant analysis (LDA), Support Vector Machines (SVMs) and Sparse Network of Winnows (SNoW). Face images used for experiments were obtained from 35 viewpoints. The direction of viewpoints varied +/-45 degrees horizontally, +/-30 degrees vertically at 15 degree intervals respectively. Although LDA showed the best performance for frontal facial images, SVM with Gaussian kernel was found the best performance (86.0%) for the facial images of 35 viewpoints. It is considered that SVM with Gaussian kernel is robust to changes in viewpoint when estimating gender from these results. Furthermore, the estimation rate was quite close to the average estimation rate at 35 viewpoints respectively. It is supposed that the methods are reasonable to estimate gender within the range of experimented viewpoints by learning face images from multiple directions by one class.
UAV based 3D digital surface model to estimate paleolandscape in high mountainous environment
NASA Astrophysics Data System (ADS)
Mészáros, János; Árvai, Mátyás; Kohán, Balázs; Deák, Márton; Nagy, Balázs
2016-04-01
reliable results and resolution. Based on the sediment layers of the peat bog together with the generated 3D surface model the paleoenvironment, the largest paleowater level can be reconstructed and we can estimate the dimension of the landslide which created the basin of the peat bog.
Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands
Arroyo Ohori, Ken; Ledoux, Hugo; Peters, Ravi; Stoter, Jantien
2016-01-01
The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach. PMID:27254151
Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands.
Biljecki, Filip; Arroyo Ohori, Ken; Ledoux, Hugo; Peters, Ravi; Stoter, Jantien
2016-01-01
The remote estimation of a region's population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach. PMID:27254151
NASA Astrophysics Data System (ADS)
Zambrano, Miller; Tondi, Emanuele; Mancini, Lucia; Trias, F. Xavier; Arzilli, Fabio; Lanzafame, Gabriele; Aibibula, Nijiati
2016-04-01
In porous rocks strain is commonly localized in narrow Deformation Bands (DBs), where the petrophysical properties are significantly modified with respect the pristine rock. As a consequence, DBs could have an important effect on production and development of porous reservoirs representing baffles zones or, in some cases, contribute to reservoir compartmentalization. Taking in consideration that the decrease of permeability within DBs is related to changes in the porous network properties (porosity, connectivity) and the pores morphology (size distribution, specific surface area), an accurate porous network characterization is useful for understanding both the effect of deformation banding on the porous network and their influence upon fluid flow through the deformed rocks. In this work, a 3D characterization of the microstructure and texture of DBs hosted in porous carbonate grainstones was obtained at the Elettra laboratory (Trieste, Italy) by using two different techniques: phase-contrast synchrotron radiation computed microtomography (micro-CT) and microfocus X-ray micro-CT. These techniques are suitable for addressing quantitative analysis of the porous network and implementing Computer Fluid Dynamics (CFD)experiments in porous rocks. Evaluated samples correspond to grainstones highly affected by DBs exposed in San Vito Lo Capo peninsula (Sicily, Italy), Favignana Island (Sicily, Italy) and Majella Mountain (Abruzzo, Italy). For the analysis, the data were segmented in two main components porous and solid phases. The properties of interest are porosity, connectivity, a grain and/or porous textural properties, in order to differentiate host rock and DBs in different zones. Permeability of DB and surrounding host rock were estimated by the implementation of CFD experiments, permeability results are validated by comparing with in situ measurements. In agreement with previous studies, the 3D image analysis and flow simulation indicate that DBs could be constitute
Scoliosis corrective force estimation from the implanted rod deformation using 3D-FEM analysis
2015-01-01
Background Improvement of material property in spinal instrumentation has brought better deformity correction in scoliosis surgery in recent years. The increase of mechanical strength in instruments directly means the increase of force, which acts on bone-implant interface during scoliosis surgery. However, the actual correction force during the correction maneuver and safety margin of pull out force on each screw were not well known. In the present study, estimated corrective forces and pull out forces were analyzed using a novel method based on Finite Element Analysis (FEA). Methods Twenty adolescent idiopathic scoliosis patients (1 boy and 19 girls) who underwent reconstructive scoliosis surgery between June 2009 and Jun 2011 were included in this study. Scoliosis correction was performed with 6mm diameter titanium rod (Ti6Al7Nb) using the simultaneous double rod rotation technique (SDRRT) in all cases. The pre-maneuver and post-maneuver rod geometry was collected from intraoperative tracing and postoperative 3D-CT images, and 3D-FEA was performed with ANSYS. Cobb angle of major curve, correction rate and thoracic kyphosis were measured on X-ray images. Results Average age at surgery was 14.8, and average fusion length was 8.9 segments. Major curve was corrected from 63.1 to 18.1 degrees in average and correction rate was 71.4%. Rod geometry showed significant change on the concave side. Curvature of the rod on concave and convex sides decreased from 33.6 to 17.8 degrees, and from 25.9 to 23.8 degrees, respectively. Estimated pull out forces at apical vertebrae were 160.0N in the concave side screw and 35.6N in the convex side screw. Estimated push in force at LIV and UIV were 305.1N in the concave side screw and 86.4N in the convex side screw. Conclusions Corrective force during scoliosis surgery was demonstrated to be about four times greater in the concave side than in convex side. Averaged pull out and push in force fell below previously reported safety
An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation
He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue
2015-01-01
Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions. PMID:26184191
An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation.
He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue
2015-01-01
Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions. PMID:26184191
Zhang, Yi; Chandler, Damon M
2015-11-01
Algorithms for a stereoscopic image quality assessment (IQA) aim to estimate the qualities of 3D images in a manner that agrees with human judgments. The modern stereoscopic IQA algorithms often apply 2D IQA algorithms on stereoscopic views, disparity maps, and/or cyclopean images, to yield an overall quality estimate based on the properties of the human visual system. This paper presents an extension of our previous 2D most apparent distortion (MAD) algorithm to a 3D version (3D-MAD) to evaluate 3D image quality. The 3D-MAD operates via two main stages, which estimate perceived quality degradation due to 1) distortion of the monocular views and 2) distortion of the cyclopean view. In the first stage, the conventional MAD algorithm is applied on the two monocular views, and then the combined binocular quality is estimated via a weighted sum of the two estimates, where the weights are determined based on a block-based contrast measure. In the second stage, intermediate maps corresponding to the lightness distance and the pixel-based contrast are generated based on a multipathway contrast gain-control model. Then, the cyclopean view quality is estimated by measuring the statistical-difference-based features obtained from the reference stereopair and the distorted stereopair, respectively. Finally, the estimates obtained from the two stages are combined to yield an overall quality score of the stereoscopic image. Tests on various 3D image quality databases demonstrate that our algorithm significantly improves upon many other state-of-the-art 2D/3D IQA algorithms. PMID:26186775
NASA Astrophysics Data System (ADS)
Verrier, N.; Grosjean, N.; Dib, E.; Méès, L.; Fournier, C.; Marié, J.-L.
2016-04-01
Digital holography is a valuable tool for three-dimensional information extraction. Among existing configurations, the originally proposed set-up (i.e. Gabor, or in-line holography), is reasonably immune to variations in the experimental environment making it a method of choice for studies of fluid dynamics. Nevertheless, standard hologram reconstruction techniques, based on numerical light back-propagation are prone to artifacts such as twin images or aliases that limit both the quality and quantity of information extracted from the acquired holograms. To get round this issue, the hologram reconstruction as a parametric inverse problem has been shown to accurately estimate 3D positions and the size of seeding particles directly from the hologram. To push the bounds of accuracy on size estimation still further, we propose to fully exploit the information redundancy of a hologram video sequence using joint estimation reconstruction. Applying this approach in a bench-top experiment, we show that it led to a relative precision of 0.13% (for a 60 μm diameter droplet) for droplet size estimation, and a tracking precision of {σx}× {σy}× {σz}=0.15× 0.15× 1~\\text{pixels} .
Estimation of Hydraulic Fracturing in the Earth Fill Dam by 3-D Analysis
NASA Astrophysics Data System (ADS)
Nishimura, Shin-Ichi
It is necessary to calculate strength and strain for estimation of hydraulic fracturing in the earth fill dam, and to which the FEM is effective. 2-D analysis can produce good results to some extent if an embankment is linear and the plain strain condition can be set to the cross section. However, there may be some conditions not possible to express in the 2-D plain because the actual embankment of agricultural reservoirs is formed by straight and curved lines. Moreover, it may not be possible to precisely calculate strain in the direction of dam axis because the 2-D analysis in the cross section cannot take the shape in the vertical section into consideration. Therefore, we performed 3-D built up analysis targeting the actually-leaked agricultural reservoir to examine hazards of hydraulic fracturing based on the shape of an embankment and by rapid impoundment of water. It resulted in the occurrence of hydraulic fracturing to develop by water pressure due to the vertical cracks caused by tensile strain in the valley and refractive section of the foundation.
Angle Estimation of Simultaneous Orthogonal Rotations from 3D Gyroscope Measurements
Stančin, Sara; Tomažič, Sašo
2011-01-01
A 3D gyroscope provides measurements of angular velocities around its three intrinsic orthogonal axes, enabling angular orientation estimation. Because the measured angular velocities represent simultaneous rotations, it is not appropriate to consider them sequentially. Rotations in general are not commutative, and each possible rotation sequence has a different resulting angular orientation. None of these angular orientations is the correct simultaneous rotation result. However, every angular orientation can be represented by a single rotation. This paper presents an analytic derivation of the axis and angle of the single rotation equivalent to three simultaneous rotations around orthogonal axes when the measured angular velocities or their proportions are approximately constant. Based on the resulting expressions, a vector called the simultaneous orthogonal rotations angle (SORA) is defined, with components equal to the angles of three simultaneous rotations around coordinate system axes. The orientation and magnitude of this vector are equal to the equivalent single rotation axis and angle, respectively. As long as the orientation of the actual rotation axis is constant, given the SORA, the angular orientation of a rigid body can be calculated in a single step, thus making it possible to avoid computing the iterative infinitesimal rotation approximation. The performed test measurements confirm the validity of the SORA concept. SORA is simple and well-suited for use in the real-time calculation of angular orientation based on angular velocity measurements derived using a gyroscope. Moreover, because of its demonstrated simplicity, SORA can also be used in general angular orientation notation. PMID:22164090
Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion
NASA Astrophysics Data System (ADS)
Pohl, Petr; Sirotenko, Michael; Tolstaya, Ekaterina; Bucha, Victor
2014-02-01
In this article we propose high quality motion estimation based on variational optical flow formulation with non-local regularization term. To improve motion in occlusion areas we introduce occlusion motion inpainting based on 3-frame motion clustering. Variational formulation of optical flow proved itself to be very successful, however a global optimization of cost function can be time consuming. To achieve acceptable computation times we adapted the algorithm that optimizes convex function in coarse-to-fine pyramid strategy and is suitable for modern GPU hardware implementation. We also introduced two simplifications of cost function that significantly decrease computation time with acceptable decrease of quality. For motion clustering based motion inpaitning in occlusion areas we introduce effective method of occlusion aware joint 3-frame motion clustering using RANSAC algorithm. Occlusion areas are inpainted by motion model taken from cluster that shows consistency in opposite direction. We tested our algorithm on Middlebury optical flow benchmark, where we scored around 20th position, but being one of the fastest method near the top. We also successfully used this algorithm in semi-automatic 2D to 3D conversion tool for spatio-temporal background inpainting, automatic adaptive key frame detection and key points tracking.
Relative Scale Estimation and 3D Registration of Multi-Modal Geometry Using Growing Least Squares.
Mellado, Nicolas; Dellepiane, Matteo; Scopigno, Roberto
2016-09-01
The advent of low cost scanning devices and the improvement of multi-view stereo techniques have made the acquisition of 3D geometry ubiquitous. Data gathered from different devices, however, result in large variations in detail, scale, and coverage. Registration of such data is essential before visualizing, comparing and archiving them. However, state-of-the-art methods for geometry registration cannot be directly applied due to intrinsic differences between the models, e.g., sampling, scale, noise. In this paper we present a method for the automatic registration of multi-modal geometric data, i.e., acquired by devices with different properties (e.g., resolution, noise, data scaling). The method uses a descriptor based on Growing Least Squares, and is robust to noise, variation in sampling density, details, and enables scale-invariant matching. It allows not only the measurement of the similarity between the geometry surrounding two points, but also the estimation of their relative scale. As it is computed locally, it can be used to analyze large point clouds composed of millions of points. We implemented our approach in two registration procedures (assisted and automatic) and applied them successfully on a number of synthetic and real cases. We show that using our method, multi-modal models can be automatically registered, regardless of their differences in noise, detail, scale, and unknown relative coverage. PMID:26672045
Digital holography as a method for 3D imaging and estimating the biovolume of motile cells.
Merola, F; Miccio, L; Memmolo, P; Di Caprio, G; Galli, A; Puglisi, R; Balduzzi, D; Coppola, G; Netti, P; Ferraro, P
2013-12-01
Sperm morphology is regarded as a significant prognostic factor for fertilization, as abnormal sperm structure is one of the most common factors in male infertility. Furthermore, obtaining accurate morphological information is an important issue with strong implications in zoo-technical industries, for example to perform sorting of species X from species Y. A challenging step forward would be the availability of a fast, high-throughput and label-free system for the measurement of physical parameters and visualization of the 3D shape of such biological specimens. Here we show a quantitative imaging approach to estimate simply and quickly the biovolume of sperm cells, combining the optical tweezers technique with digital holography, in a single and integrated set-up for a biotechnology assay process on the lab-on-a-chip scale. This approach can open the way for fast and high-throughput analysis in label-free microfluidic based "cytofluorimeters" and prognostic examination based on sperm morphology, thus allowing advancements in reproductive science. PMID:24129638
Estimating Mass Properties of Dinosaurs Using Laser Imaging and 3D Computer Modelling
Bates, Karl T.; Manning, Phillip L.; Hodgetts, David; Sellers, William I.
2009-01-01
Body mass reconstructions of extinct vertebrates are most robust when complete to near-complete skeletons allow the reconstruction of either physical or digital models. Digital models are most efficient in terms of time and cost, and provide the facility to infinitely modify model properties non-destructively, such that sensitivity analyses can be conducted to quantify the effect of the many unknown parameters involved in reconstructions of extinct animals. In this study we use laser scanning (LiDAR) and computer modelling methods to create a range of 3D mass models of five specimens of non-avian dinosaur; two near-complete specimens of Tyrannosaurus rex, the most complete specimens of Acrocanthosaurus atokensis and Strutiomimum sedens, and a near-complete skeleton of a sub-adult Edmontosaurus annectens. LiDAR scanning allows a full mounted skeleton to be imaged resulting in a detailed 3D model in which each bone retains its spatial position and articulation. This provides a high resolution skeletal framework around which the body cavity and internal organs such as lungs and air sacs can be reconstructed. This has allowed calculation of body segment masses, centres of mass and moments or inertia for each animal. However, any soft tissue reconstruction of an extinct taxon inevitably represents a best estimate model with an unknown level of accuracy. We have therefore conducted an extensive sensitivity analysis in which the volumes of body segments and respiratory organs were varied in an attempt to constrain the likely maximum plausible range of mass parameters for each animal. Our results provide wide ranges in actual mass and inertial values, emphasizing the high level of uncertainty inevitable in such reconstructions. However, our sensitivity analysis consistently places the centre of mass well below and in front of hip joint in each animal, regardless of the chosen combination of body and respiratory structure volumes. These results emphasize that future
Humm, J L; Macklis, R M; Lu, X Q; Yang, Y; Bump, K; Beresford, B; Chin, L M
1995-01-01
In order to better predict and understand the effects of radiopharmaceuticals used for therapy, it is necessary to determine more accurately the radiation absorbed dose to cells in tissue. Using thin-section autoradiography, the spatial distribution of sources relative to the cells can be obtained from a single section with micrometre resolution. By collecting and analysing serial sections, the 3D microscopic distribution of radionuclide relative to the cellular histology, and therefore the dose rate distribution, can be established. In this paper, a method of 3D reconstruction of serial sections is proposed, and measurements are reported of (i) the accuracy and reproducibility of quantitative autoradiography and (ii) the spatial precision with which tissue features from one section can be related to adjacent sections. Uncertainties in the activity determination for the specimen result from activity losses during tissue processing (4-11%), and the variation of grain count per unit activity between batches of serial sections (6-25%). Correlation of the section activity to grain count densities showed deviations ranging from 6-34%. The spatial alignment uncertainties were assessed using nylon fibre fiduciary markers incorporated into the tissue block, and compared to those for alignment based on internal tissue landmarks. The standard deviation for the variation in nylon fibre fiduciary alignment was measured to be 41 microns cm-1, compared to 69 microns cm-1 when internal tissue histology landmarks were used. In addition, tissue shrinkage during histological processing of up to 10% was observed. The implications of these measured activity and spatial distribution uncertainties upon the estimate of cellular dose rate distribution depends upon the range of the radiation emissions. For long-range beta particles, uncertainties in both the activity and spatial distribution translate linearly to the uncertainty in dose rate of < 15%. For short-range emitters (< 100
Estimating mass properties of dinosaurs using laser imaging and 3D computer modelling.
Bates, Karl T; Manning, Phillip L; Hodgetts, David; Sellers, William I
2009-01-01
Body mass reconstructions of extinct vertebrates are most robust when complete to near-complete skeletons allow the reconstruction of either physical or digital models. Digital models are most efficient in terms of time and cost, and provide the facility to infinitely modify model properties non-destructively, such that sensitivity analyses can be conducted to quantify the effect of the many unknown parameters involved in reconstructions of extinct animals. In this study we use laser scanning (LiDAR) and computer modelling methods to create a range of 3D mass models of five specimens of non-avian dinosaur; two near-complete specimens of Tyrannosaurus rex, the most complete specimens of Acrocanthosaurus atokensis and Strutiomimum sedens, and a near-complete skeleton of a sub-adult Edmontosaurus annectens. LiDAR scanning allows a full mounted skeleton to be imaged resulting in a detailed 3D model in which each bone retains its spatial position and articulation. This provides a high resolution skeletal framework around which the body cavity and internal organs such as lungs and air sacs can be reconstructed. This has allowed calculation of body segment masses, centres of mass and moments or inertia for each animal. However, any soft tissue reconstruction of an extinct taxon inevitably represents a best estimate model with an unknown level of accuracy. We have therefore conducted an extensive sensitivity analysis in which the volumes of body segments and respiratory organs were varied in an attempt to constrain the likely maximum plausible range of mass parameters for each animal. Our results provide wide ranges in actual mass and inertial values, emphasizing the high level of uncertainty inevitable in such reconstructions. However, our sensitivity analysis consistently places the centre of mass well below and in front of hip joint in each animal, regardless of the chosen combination of body and respiratory structure volumes. These results emphasize that future
Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor
Saeed, Anwar; Al-Hamadi, Ayoub; Ghoneim, Ahmed
2015-01-01
Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding 5.1∘,4.6∘,4.2∘ for pitch, yaw and roll angles, respectively. PMID:26343651
Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor.
Saeed, Anwar; Al-Hamadi, Ayoub; Ghoneim, Ahmed
2015-01-01
Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding 5:1; 4:6; 4:2 for pitch, yaw and roll angles, respectively. PMID:26343651
Shen, Jie; Liu, Guangcan; Chen, Jia; Fang, Yuqiang; Xie, Jianbin; Yu, Yong; Yan, Shuicheng
2014-11-01
In this paper, we utilize structured learning to simultaneously address two intertwined problems: 1) human pose estimation (HPE) and 2) garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications. Unlike previous works that usually handle the two problems separately, our approach aims to produce an optimal joint estimation for both HPE and GAC via a unified inference procedure. To this end, we adopt a preprocessing step to detect potential human parts from each image (i.e., a set of candidates) that allows us to have a manageable input space. In this way, the simultaneous inference of HPE and GAC is converted to a structured learning problem, where the inputs are the collections of candidate ensembles, outputs are the joint labels of human parts and garment attributes, and joint feature representation involves various cues such as pose-specific features, garment-specific features, and cross-task features that encode correlations between human parts and garment attributes. Furthermore, we explore the strong edge evidence around the potential human parts so as to derive more powerful representations for oriented human parts. Such evidences can be seamlessly integrated into our structured learning model as a kind of energy function, and the learning process could be performed by standard structured support vector machines algorithm. However, the joint structure of the two problems is a cyclic graph, which hinders efficient inference. To resolve this issue, we compute instead approximate optima using an iterative procedure, where in each iteration, the variables of one problem are fixed. In this way, satisfactory solutions can be efficiently computed by dynamic programming. Experimental results on two benchmark data sets show the state-of-the-art performance of our approach. PMID:25248181
NASA Astrophysics Data System (ADS)
Shen, Jie; Liu, Guangcan; Chen, Jia; Fang, Yuqiang; Xie, Jianbin; Yu, Yong; Yan, Shuicheng
2014-11-01
In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications. Unlike previous works that usually handle the two problems separately, our approach aims to produce a jointly optimal estimation for both HPE and GAC via a unified inference procedure. To this end, we adopt a preprocessing step to detect potential human parts from each image (i.e., a set of "candidates") that allows us to have a manageable input space. In this way, the simultaneous inference of HPE and GAC is converted to a structured learning problem, where the inputs are the collections of candidate ensembles, the outputs are the joint labels of human parts and garment attributes, and the joint feature representation involves various cues such as pose-specific features, garment-specific features, and cross-task features that encode correlations between human parts and garment attributes. Furthermore, we explore the "strong edge" evidence around the potential human parts so as to derive more powerful representations for oriented human parts. Such evidences can be seamlessly integrated into our structured learning model as a kind of energy function, and the learning process could be performed by standard structured Support Vector Machines (SVM) algorithm. However, the joint structure of the two problems is a cyclic graph, which hinders efficient inference. To resolve this issue, we compute instead approximate optima by using an iterative procedure, where in each iteration the variables of one problem are fixed. In this way, satisfactory solutions can be efficiently computed by dynamic programming. Experimental results on two benchmark datasets show the state-of-the-art performance of our approach.
NASA Astrophysics Data System (ADS)
Rabbani, Arash; Ayatollahi, Shahab; Kharrat, Riyaz; Dashti, Nader
2016-08-01
In this study, we have utilized 3-D micro-tomography images of real and synthetic rocks to introduce two mathematical correlations which estimate the distribution parameters of 3-D coordination number using a single 2-D cross-sectional image. By applying a watershed segmentation algorithm, it is found that the distribution of 3-D coordination number is acceptably predictable by statistical analysis of the network extracted from 2-D images. In this study, we have utilized 25 volumetric images of rocks in order to propose two mathematical formulas. These formulas aim to approximate the average and standard deviation of coordination number in 3-D pore networks. Then, the formulas are applied for five independent test samples to evaluate the reliability. Finally, pore network flow modeling is used to find the error of absolute permeability prediction using estimated and measured coordination numbers. Results show that the 2-D images are considerably informative about the 3-D network of the rocks and can be utilized to approximate the 3-D connectivity of the porous spaces with determination coefficient of about 0.85 that seems to be acceptable considering the variety of the studied samples.
NASA Astrophysics Data System (ADS)
Wake, Kanako; Varsier, Nadège; Watanabe, Soichi; Taki, Masao; Wiart, Joe; Mann, Simon; Deltour, Isabelle; Cardis, Elisabeth
2009-10-01
A worldwide epidemiological study called 'INTERPHONE' has been conducted to estimate the hypothetical relationship between brain tumors and mobile phone use. In this study, we proposed a method to estimate 3D distribution of the specific absorption rate (SAR) in the human head due to mobile phone use to provide the exposure gradient for epidemiological studies. 3D SAR distributions due to exposure to an electromagnetic field from mobile phones are estimated from mobile phone compliance testing data for actual devices. The data for compliance testing are measured only on the surface in the region near the device and in a small 3D region around the maximum on the surface in a homogeneous phantom with a specific shape. The method includes an interpolation/extrapolation and a head shape conversion. With the interpolation/extrapolation, SAR distributions in the whole head are estimated from the limited measured data. 3D SAR distributions in the numerical head models, where the tumor location is identified in the epidemiological studies, are obtained from measured SAR data with the head shape conversion by projection. Validation of the proposed method was performed experimentally and numerically. It was confirmed that the proposed method provided good estimation of 3D SAR distribution in the head, especially in the brain, which is the tissue of major interest in epidemiological studies. We conclude that it is possible to estimate 3D SAR distributions in a realistic head model from the data obtained by compliance testing measurements to provide a measure for the exposure gradient in specific locations of the brain for the purpose of exposure assessment in epidemiological studies. The proposed method has been used in several studies in the INTERPHONE.
Comparative assessment of bone pose estimation using Point Cluster Technique and OpenSim.
Lathrop, Rebecca L; Chaudhari, Ajit M W; Siston, Robert A
2011-11-01
Estimating the position of the bones from optical motion capture data is a challenge associated with human movement analysis. Bone pose estimation techniques such as the Point Cluster Technique (PCT) and simulations of movement through software packages such as OpenSim are used to minimize soft tissue artifact and estimate skeletal position; however, using different methods for analysis may produce differing kinematic results which could lead to differences in clinical interpretation such as a misclassification of normal or pathological gait. This study evaluated the differences present in knee joint kinematics as a result of calculating joint angles using various techniques. We calculated knee joint kinematics from experimental gait data using the standard PCT, the least squares approach in OpenSim applied to experimental marker data, and the least squares approach in OpenSim applied to the results of the PCT algorithm. Maximum and resultant RMS differences in knee angles were calculated between all techniques. We observed differences in flexion/extension, varus/valgus, and internal/external rotation angles between all approaches. The largest differences were between the PCT results and all results calculated using OpenSim. The RMS differences averaged nearly 5° for flexion/extension angles with maximum differences exceeding 15°. Average RMS differences were relatively small (< 1.08°) between results calculated within OpenSim, suggesting that the choice of marker weighting is not critical to the results of the least squares inverse kinematics calculations. The largest difference between techniques appeared to be a constant offset between the PCT and all OpenSim results, which may be due to differences in the definition of anatomical reference frames, scaling of musculoskeletal models, and/or placement of virtual markers within OpenSim. Different methods for data analysis can produce largely different kinematic results, which could lead to the misclassification
NASA Astrophysics Data System (ADS)
Hostache, Renaud; Krein, Andreas; Barrière, Julien
2014-05-01
Coupling the 3D hydro-morphodynamic model Telemac-3D-sisyphe and seismic measurements to estimate bedload transport rates in a small gravel-bed river. Renaud Hostache, Andreas Krein, Julien Barrière During flood events, amounts of river bed material are transported via bedload. This causes problems, like the silting of reservoirs or the disturbance of biological habitats. Some current bedload measuring techniques have limited possibilities for studies in high temporal resolutions. Optical systems are usually not applicable because of high turbidity due to concentrated suspended sediment transported. Sediment traps or bedload samplers yield only summative information on bedload transport with low temporal resolution. An alternative bedload measuring technique is the use of seismological systems installed next to the rivers. The potential advantages are observations in real time and under undisturbed conditions. The study area is a 120 m long reach of River Colpach (21.5 km2), a small gravel bed river in Northern Luxembourg. A combined approach of hydro-climatological observations, hydraulic measurements, sediment sampling, and seismological measurements is used in order to investigate bedload transport phenomena. Information derived from seismic measurements and results from a 3-dimensional hydro-morphodynamic model are exemplarily discussed for a November 2013 flood event. The 3-dimensional hydro-morphodynamic model is based on the Telemac hydroinformatic system. This allows for dynamically coupling a 3D hydrodynamic model (Telemac-3D) and a morphodynamic model (Sisyphe). The coupling is dynamic as these models exchange their information during simulations. This is a main advantage as it allows for taking into account the effects of the morphologic changes of the riverbed on the water hydrodynamic and the bedload processes. The coupled model has been calibrated using time series of gauged water depths and time series of bed material collected sequentially (after
NASA Astrophysics Data System (ADS)
Maljers, Denise; den Dulk, Maryke; ten Veen, Johan; Hummelman, Jan; Gunnink, Jan; van Gessel, Serge
2016-04-01
The Geological Survey of the Netherlands (GSN) develops and maintains subsurface models with regional to national coverage. These models are paramount for petroleum exploration in conventional reservoirs, for understanding the distribution of unconventional reservoirs, for mapping geothermal aquifers, for the potential to store carbon, or for groundwater- or aggregate resources. Depending on the application domain these models differ in depth range, scale, data used, modelling software and modelling technique. Depth uncertainty information is available for the Geological Survey's 3D raster layer models DGM Deep and DGM Shallow. These models cover different depth intervals and are constructed using different data types and different modelling software. Quantifying the uncertainty of geological models that are constructed using multiple data types as well as geological expert-knowledge is not straightforward. Examples of geological expert-knowledge are trend surfaces displaying the regional thickness trends of basin fills or steering points that are used to guide the pinching out of geological formations or the modelling of the complex stratal geometries associated with saltdomes and saltridges. This added a-priori knowledge, combined with the assumptions underlying kriging (normality and second-order stationarity), makes the kriging standard error an incorrect measure of uncertainty for our geological models. Therefore the methods described below were developed. For the DGM Deep model a workflow has been developed to assess uncertainty by combining precision (giving information on the reproducibility of the model results) and accuracy (reflecting the proximity of estimates to the true value). This was achieved by centering the resulting standard deviations around well-tied depths surfaces. The standard deviations are subsequently modified by three other possible error sources: data error, structural complexity and velocity model error. The uncertainty workflow
NASA Astrophysics Data System (ADS)
Asgari, Shadnaz; Ali, Andreas M.; Collier, Travis C.; Yao, Yuan; Hudson, Ralph E.; Yao, Kung; Taylor, Charles E.
2007-09-01
The focus of most direction-of-arrival (DOA) estimation problems has been based mainly on a two-dimensional (2D) scenario where we only need to estimate the azimuth angle. But in various practical situations we have to deal with a three-dimensional scenario. The importance of being able to estimate both azimuth and elevation angles with high accuracy and low complexity is of interest. We present the theoretical and the practical issues of DOA estimation using the Approximate-Maximum-Likelihood (AML) algorithm in a 3D scenario. We show that the performance of the proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. Various numerical results are presented. We use two acoustic arrays each consisting of 8 microphones to do some field measurements. The processing of the measured data from the acoustic arrays for different azimuth and elevation angles confirms the effectiveness of the proposed methods.
Video reframing relying on panoramic estimation based on a 3D representation of the scene
NASA Astrophysics Data System (ADS)
de Simon, Agnes; Figue, Jean; Nicolas, Henri
2000-05-01
This paper describes a new method for creating mosaic images from an original video and for computing a new sequence modifying some camera parameters like image size, scale factor, view angle... A mosaic image is a representation of the full scene observed by a moving camera during its displacement. It provides a wide angle of view of the scene from a sequence of images shot with a narrow angle of view camera. This paper proposes a method to create a virtual sequence from a calibrated original video and a rough 3D model of the scene. A 3D relationship between original and virtual images gives pixel correspondent in different images for a same 3D point in model scene. To texture the model with natural textures obtained in the original sequence, a criterion based on constraints related to the temporal variations of the background and 3D geometric considerations is used. Finally, in the presented method, the textured 3D model is used to recompute a new sequence of image with possibly different point of view and camera aperture angle. The algorithm is being proven with virtual sequences and, obtained results are encouraging up to now.
Joint tracking, pose estimation, and target recognition using HRRR and track data: new results
NASA Astrophysics Data System (ADS)
Zajic, Tim; Rago, Constantino; Mahler, Ronald P. S.; Huff, Melvyn; Noviskey, Michael J.
2001-08-01
The work presented here is a continuation of research first reported in Mahler, et. Al. Our goal is a generalization of Bayesian filtering and estimation theory to the problem of multisensor, multitarget, multi-evidence unified joint detection, tracking and target identification. Our earlier efforts were focused on integrating the Statistical Features algorithm with a Bayesian nonlinear filter, allowing simultaneous determination of target position, velocity, pose and type via maximum a posteriori estimation. In this paper we continue to address the problem of target classification based on high range resolution radar signatures. While we continue to consider feature based techniques, as in StaF and our earlier work, instead of considering the location and magnitude of peaks in a signature as our features, we consider three alternative features. The features arise from applying either a Wavelet Decomposition, Principal Component Analysis or Linear Discriminant Analysis to the signature. We discuss briefly also, in the wavelet decomposition setting, the challenge of assigning a measure of uncertainty with a classification decision.
Tracey, Jeff A; Sheppard, James; Zhu, Jun; Wei, Fuwen; Swaisgood, Ronald R; Fisher, Robert N
2014-01-01
Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species--giant panda, dugong, and California condor--to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research. PMID:24988114
Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation
Tracey, Jeff A.; Sheppard, James; Zhu, Jun; Wei, Fu-Wen; Swaisgood, Ronald R.; Fisher, Robert N.
2014-01-01
Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.
Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation
Tracey, Jeff A.; Sheppard, James; Zhu, Jun; Wei, Fuwen; Swaisgood, Ronald R.; Fisher, Robert N.
2014-01-01
Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research. PMID:24988114
Konofagou, E E; Ophir, J
2000-06-01
In elastography we have previously developed a tracking and correction method that estimates the axial and lateral strain components along and perpendicular to the compressor/scanning axis following an externally applied compression. However, the resulting motion is a three-dimensional problem. Therefore, in order to fully describe this motion we need to consider a 3D model and estimate all three principal strain components, i.e. axial, lateral and elevational (out-of-plane), for a full 3D tensor description. Since motion is coupled in all three dimensions, the three motion components have to be decoupled prior to their estimation. In this paper, we describe a method that estimates and corrects motion in three dimensions, which is an extension of the 2D motion tracking and correction method discussed before. In a similar way as in the 2D motion estimation, and by assuming that ultrasonic frames are available in more than one parallel elevational plane, we used methods of interpolation and cross-correlation between elevationally displaced RF echo segments to estimate the elevational displacement and strain. In addition, the axial, lateral and elevational displacements were used to estimate all three shear strain components that, together with the normal strain estimates, fully describe the full 3D normal strain tensor resulting from the uniform compression. Results of this method from three-dimensional finite-element simulations are shown. PMID:10870710
A hybrid antenna array design for 3-d direction of arrival estimation.
Saqib, Najam-Us; Khan, Imdad
2015-01-01
A 3-D beam scanning antenna array design is proposed that gives a whole 3-D spherical coverage and also suitable for various radar and body-worn devices in the Body Area Networks applications. The Array Factor (AF) of the proposed antenna is derived and its various parameters like directivity, Half Power Beam Width (HPBW) and Side Lobe Level (SLL) are calculated by varying the size of the proposed antenna array. Simulations were carried out in MATLAB 2012b. The radiators are considered isotropic and hence mutual coupling effects are ignored. The proposed array shows a considerable improvement against the existing cylindrical and coaxial cylindrical arrays in terms of 3-D scanning, size, directivity, HPBW and SLL. PMID:25790103
Estimation of Atmospheric Methane Surface Fluxes Using a Global 3-D Chemical Transport Model
NASA Astrophysics Data System (ADS)
Chen, Y.; Prinn, R.
2003-12-01
Accurate determination of atmospheric methane surface fluxes is an important and challenging problem in global biogeochemical cycles. We use inverse modeling to estimate annual, seasonal, and interannual CH4 fluxes between 1996 and 2001. The fluxes include 7 time-varying seasonal (3 wetland, rice, and 3 biomass burning) and 3 steady aseasonal (animals/waste, coal, and gas) global processes. To simulate atmospheric methane, we use the 3-D chemical transport model MATCH driven by NCEP reanalyzed observed winds at a resolution of T42 ( ˜2.8° x 2.8° ) in the horizontal and 28 levels (1000 - 3 mb) in the vertical. By combining existing datasets of individual processes, we construct a reference emissions field that represents our prior guess of the total CH4 surface flux. For the methane sink, we use a prescribed, annually-repeating OH field scaled to fit methyl chloroform observations. MATCH is used to produce both the reference run from the reference emissions, and the time-dependent sensitivities that relate individual emission processes to observations. The observational data include CH4 time-series from ˜15 high-frequency (in-situ) and ˜50 low-frequency (flask) observing sites. Most of the high-frequency data, at a time resolution of 40-60 minutes, have not previously been used in global scale inversions. In the inversion, the high-frequency data generally have greater weight than the weekly flask data because they better define the observational monthly means. The Kalman Filter is used as the optimal inversion technique to solve for emissions between 1996-2001. At each step in the inversion, new monthly observations are utilized and new emissions estimates are produced. The optimized emissions represent deviations from the reference emissions that lead to a better fit to the observations. The seasonal processes are optimized for each month, and contain the methane seasonality and interannual variability. The aseasonal processes, which are less variable, are
NASA Astrophysics Data System (ADS)
Qayyum, A.; Malik, A. S.; Saad, M. N. M.; Iqbal, M.; Abdullah, F.; Rahseed, W.; Abdullah, T. A. R. B. T.; Ramli, A. Q.
2015-04-01
Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites' imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.
Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging.
Axer, Markus; Strohmer, Sven; Gräßel, David; Bücker, Oliver; Dohmen, Melanie; Reckfort, Julia; Zilles, Karl; Amunts, Katrin
2016-01-01
Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal. PMID:27147981
Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging
Axer, Markus; Strohmer, Sven; Gräßel, David; Bücker, Oliver; Dohmen, Melanie; Reckfort, Julia; Zilles, Karl; Amunts, Katrin
2016-01-01
Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal. PMID:27147981
NASA Astrophysics Data System (ADS)
Courbet, C.; DICK, P.; Lefevre, M.; Wittebroodt, C.; Matray, J.; Barnichon, J.
2013-12-01
logging, porosity varies by a factor of 2.5 whilst hydraulic conductivity varies by 2 to 3 orders of magnitude. In addition, a 3D numerical reconstruction of the internal structure of the fault zone inferred from borehole imagery has been built to estimate the permeability tensor variations. First results indicate that hydraulic conductivity values calculated for this structure are 2 to 3 orders of magnitude above those measured in situ. Such high values are due to the imaging method that only takes in to account open fractures of simple geometry (sine waves). Even though improvements are needed to handle more complex geometry, outcomes are promising as the fault damaged zone clearly appears as the highest permeability zone, where stress analysis show that the actual stress state may favor tensile reopening of fractures. Using shale samples cored from the different internal structures of the fault zone, we aim now to characterize the advection and diffusion using laboratory petrophysical tests combined with radial and through-diffusion experiments.
SU-E-J-135: An Investigation of Ultrasound Imaging for 3D Intra-Fraction Prostate Motion Estimation
O'Shea, T; Harris, E; Bamber, J; Evans, P
2014-06-01
Purpose: This study investigates the use of a mechanically swept 3D ultrasound (US) probe to estimate intra-fraction motion of the prostate during radiation therapy using an US phantom and simulated transperineal imaging. Methods: A 3D motion platform was used to translate an US speckle phantom while simulating transperineal US imaging. Motion patterns for five representative types of prostate motion, generated from patient data previously acquired with a Calypso system, were using to move the phantom in 3D. The phantom was also implanted with fiducial markers and subsequently tracked using the CyberKnife kV x-ray system for comparison. A normalised cross correlation block matching algorithm was used to track speckle patterns in 3D and 2D US data. Motion estimation results were compared with known phantom translations. Results: Transperineal 3D US could track superior-inferior (axial) and anterior-posterior (lateral) motion to better than 0.8 mm root-mean-square error (RMSE) at a volume rate of 1.7 Hz (comparable with kV x-ray tracking RMSE). Motion estimation accuracy was poorest along the US probe's swept axis (right-left; RL; RMSE < 4.2 mm) but simple regularisation methods could be used to improve RMSE (< 2 mm). 2D US was found to be feasible for slowly varying motion (RMSE < 0.5 mm). 3D US could also allow accurate radiation beam gating with displacement thresholds of 2 mm and 5 mm exhibiting a RMSE of less than 0.5 mm. Conclusion: 2D and 3D US speckle tracking is feasible for prostate motion estimation during radiation delivery. Since RL prostate motion is small in magnitude and frequency, 2D or a hybrid (2D/3D) US imaging approach which also accounts for potential prostate rotations could be used. Regularisation methods could be used to ensure the accuracy of tracking data, making US a feasible approach for gating or tracking in standard or hypo-fractionated prostate treatments.
Autonomous proximity operations using machine vision for trajectory control and pose estimation
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Sternberg, Stanley R.
1991-01-01
A machine vision algorithm was developed which permits guidance control to be maintained during autonomous proximity operations. At present this algorithm exists as a simulation, running upon an 80386 based personal computer, using a ModelMATE CAD package to render the target vehicle. However, the algorithm is sufficiently simple, so that following off-line training on a known target vehicle, it should run in real time with existing vision hardware. The basis of the algorithm is a sequence of single camera images of the target vehicle, upon which radial transforms were performed. Selected points of the resulting radial signatures are fed through a decision tree, to determine whether the signature matches that of the known reference signatures for a particular view of the target. Based upon recognized scenes, the position of the maneuvering vehicle with respect to the target vehicles can be calculated, and adjustments made in the former's trajectory. In addition, the pose and spin rates of the target satellite can be estimated using this method.
Real-time upper-body human pose estimation from depth data using Kalman filter for simulator
NASA Astrophysics Data System (ADS)
Lee, D.; Chi, S.; Park, C.; Yoon, H.; Kim, J.; Park, C. H.
2014-08-01
Recently, many studies show that an indoor horse riding exercise has a positive effect on promoting health and diet. However, if a rider has an incorrect posture, it will be the cause of back pain. In spite of this problem, there is only few research on analyzing rider's posture. Therefore, the purpose of this study is to estimate a rider pose from a depth image using the Asus's Xtion sensor in real time. In the experiments, we show the performance of our pose estimation algorithm in order to comparing the results between our joint estimation algorithm and ground truth data.
NASA Astrophysics Data System (ADS)
Saxena, Nishank; Mavko, Gary
2016-03-01
Estimation of elastic rock moduli using 2D plane strain computations from thin sections has several numerical and analytical advantages over using 3D rock images, including faster computation, smaller memory requirements, and the availability of cheap thin sections. These advantages, however, must be weighed against the estimation accuracy of 3D rock properties from thin sections. We present a new method for predicting elastic properties of natural rocks using thin sections. Our method is based on a simple power-law transform that correlates computed 2D thin section moduli and the corresponding 3D rock moduli. The validity of this transform is established using a dataset comprised of FEM-computed elastic moduli of rock samples from various geologic formations, including Fontainebleau sandstone, Berea sandstone, Bituminous sand, and Grossmont carbonate. We note that using the power-law transform with a power-law coefficient between 0.4-0.6 contains 2D moduli to 3D moduli transformations for all rocks that are considered in this study. We also find that reliable estimates of P-wave (Vp) and S-wave velocity (Vs) trends can be obtained using 2D thin sections.
Parameter Estimation of Fossil Oysters from High Resolution 3D Point Cloud and Image Data
NASA Astrophysics Data System (ADS)
Djuricic, Ana; Harzhauser, Mathias; Dorninger, Peter; Nothegger, Clemens; Mandic, Oleg; Székely, Balázs; Molnár, Gábor; Pfeifer, Norbert
2014-05-01
A unique fossil oyster reef was excavated at Stetten in Lower Austria, which is also the highlight of the geo-edutainment park 'Fossilienwelt Weinviertel'. It provides the rare opportunity to study the Early Miocene flora and fauna of the Central Paratethys Sea. The site presents the world's largest fossil oyster biostrome formed about 16.5 million years ago in a tropical estuary of the Korneuburg Basin. About 15,000 up to 80-cm-long shells of Crassostrea gryphoides cover a 400 m2 large area. Our project 'Smart-Geology for the World's largest fossil oyster reef' combines methods of photogrammetry, geology and paleontology to document, evaluate and quantify the shell bed. This interdisciplinary approach will be applied to test hypotheses on the genesis of the taphocenosis (e.g.: tsunami versus major storm) and to reconstruct pre- and post-event processes. Hence, we are focusing on using visualization technologies from photogrammetry in geology and paleontology in order to develop new methods for automatic and objective evaluation of 3D point clouds. These will be studied on the basis of a very dense surface reconstruction of the oyster reef. 'Smart Geology', as extension of the classic discipline, exploits massive data, automatic interpretation, and visualization. Photogrammetry provides the tools for surface acquisition and objective, automated interpretation. We also want to stress the economic aspect of using automatic shape detection in paleontology, which saves manpower and increases efficiency during the monitoring and evaluation process. Currently, there are many well known algorithms for 3D shape detection of certain objects. We are using dense 3D laser scanning data from an instrument utilizing the phase shift measuring principle, which provides accurate geometrical basis < 3 mm. However, the situation is difficult in this multiple object scenario where more than 15,000 complete or fragmentary parts of an object with random orientation are found. The goal
Estimation of gold potentials using 3D restoration modeling, Mount Pleasant Area, Western Australia
NASA Astrophysics Data System (ADS)
Mejia-Herrera, Pablo; Kakurina, Maria; Royer, Jean-Jacques
2015-04-01
A broad variety of gold-deposits are related to fault systems developed during a deformation event. Such discontinuities control the metals transport and allow the relatively high permeability necessary for the metals accumulation during the ore-deposits formation. However, some gold deposits formed during the same deformation event occur at locations far from the main faults. In those cases, the fracture systems are related with the rock heterogeneity that partially controls the damage development on the rock mass. A geo-mechanical 3D restoration modeling approach was used to simulate the strain developed during a stretching episode occurred in the Mount Pleasant region, Western Australia. Firstly a 3D solid-model was created from geological maps and interpreted structural cross-sections available on the studied region. The backward model was obtained flattening a stretching-representative reference surface selected from the lithology sequence. The deformation modeling was carried out on a 3D model built on Gocad/Skua and restored using a full geo-mechanical modeling based on a finite element method used to compute the volume restoration in a 600 m tetrahedral-mesh-resolution solid. The 3D structural restoration of the region was performed flattening surfaces using a flexural slip deformation style. Results show how the rock heterogeneity allows damages in locations far from the fault systems. The distant off-fault damage areas are located preferentially in lithological contacts and also follow the deformation trend of the region. Using a logistic regression method, it is shown that off-fault zones with high gold occurrences correlate spatially on locations with locally-high-gradient first deformational parameter, obtained from the restoration strain field. This contribution may provide some explanation for the presence of gold accumulations away from main fault systems, and the method could be used for inferring favorable areas in exploration surveys.
Estimation of the thermal conductivity of hemp based insulation material from 3D tomographic images
NASA Astrophysics Data System (ADS)
El-Sawalhi, R.; Lux, J.; Salagnac, P.
2016-08-01
In this work, we are interested in the structural and thermal characterization of natural fiber insulation materials. The thermal performance of these materials depends on the arrangement of fibers, which is the consequence of the manufacturing process. In order to optimize these materials, thermal conductivity models can be used to correlate some relevant structural parameters with the effective thermal conductivity. However, only a few models are able to take into account the anisotropy of such material related to the fibers orientation, and these models still need realistic input data (fiber orientation distribution, porosity, etc.). The structural characteristics are here directly measured on a 3D tomographic image using advanced image analysis techniques. Critical structural parameters like porosity, pore and fiber size distribution as well as local fiber orientation distribution are measured. The results of the tested conductivity models are then compared with the conductivity tensor obtained by numerical simulation on the discretized 3D microstructure, as well as available experimental measurements. We show that 1D analytical models are generally not suitable for assessing the thermal conductivity of such anisotropic media. Yet, a few anisotropic models can still be of interest to relate some structural parameters, like the fiber orientation distribution, to the thermal properties. Finally, our results emphasize that numerical simulations on 3D realistic microstructure is a very interesting alternative to experimental measurements.
Guyomarc'h, Pierre; Dutailly, Bruno; Charton, Jérôme; Santos, Frédéric; Desbarats, Pascal; Coqueugniot, Hélène
2014-11-01
This study presents Anthropological Facial Approximation in Three Dimensions (AFA3D), a new computerized method for estimating face shape based on computed tomography (CT) scans of 500 French individuals. Facial soft tissue depths are estimated based on age, sex, corpulence, and craniometrics, and projected using reference planes to obtain the global facial appearance. Position and shape of the eyes, nose, mouth, and ears are inferred from cranial landmarks through geometric morphometrics. The 100 estimated cutaneous landmarks are then used to warp a generic face to the target facial approximation. A validation by re-sampling on a subsample demonstrated an average accuracy of c. 4 mm for the overall face. The resulting approximation is an objective probable facial shape, but is also synthetic (i.e., without texture), and therefore needs to be enhanced artistically prior to its use in forensic cases. AFA3D, integrated in the TIVMI software, is available freely for further testing. PMID:25088006
NASA Astrophysics Data System (ADS)
Ivy, D. J.; Rigby, M. L.; Prinn, R. G.; Muhle, J.; Weiss, R. F.
2009-12-01
We present optimized annual global emissions from 1973-2008 of nitrogen trifluoride (NF3), a powerful greenhouse gas which is not currently regulated by the Kyoto Protocol. In the past few decades, NF3 production has dramatically increased due to its usage in the semiconductor industry. Emissions were estimated through the 'pulse-method' discrete Kalman filter using both a simple, flexible 2-D 12-box model used in the Advanced Global Atmospheric Gases Experiment (AGAGE) network and the Model for Ozone and Related Tracers (MOZART v4.5), a full 3-D atmospheric chemistry model. No official audited reports of industrial NF3 emissions are available, and with limited information on production, a priori emissions were estimated using both a bottom-up and top-down approach with two different spatial patterns based on semiconductor perfluorocarbon (PFC) emissions from the Emission Database for Global Atmospheric Research (EDGAR v3.2) and Semiconductor Industry Association sales information. Both spatial patterns used in the models gave consistent results, showing the robustness of the estimated global emissions. Differences between estimates using the 2-D and 3-D models can be attributed to transport rates and resolution differences. Additionally, new NF3 industry production and market information is presented. Emission estimates from both the 2-D and 3-D models suggest that either the assumed industry release rate of NF3 or industry production information is still underestimated.
Zhang, Yu; Teng, Poching; Shimizu, Yo; Hosoi, Fumiki; Omasa, Kenji
2016-01-01
For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R² = 0.98) and 0.57 mm (R² = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency. PMID:27314348
Zhang, Yu; Teng, Poching; Shimizu, Yo; Hosoi, Fumiki; Omasa, Kenji
2016-01-01
For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R2 = 0.98) and 0.57 mm (R2 = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency. PMID:27314348
Relative pose estimation of a lander using crater detection and matching
NASA Astrophysics Data System (ADS)
Lu, Tingting; Hu, Weiduo; Liu, Chang; Yang, Daguang
2016-02-01
Future space exploration missions require precise information about the lander pose during the descent and landing steps. An effective algorithm that utilizes crater detection and matching is presented to determine the lander pose with respect to the planetary surface. First, the projections of the crater circular rims in the descent image are detected and fitted into ellipses based on the geometric distance and coplanar circles constraint. Second, the detected craters are metrically rectified through a two-dimensional homography and matched with the crater database by similarity transformation. Finally, the lander pose is calculated by a norm-based optimization method. The algorithm is tested by synthetic and real trials. The experimental results show that our presented algorithm can determine the lander pose accurately and robustly.
Selecting best-fit models for estimating the body mass from 3D data of the human calcaneus.
Jung, Go-Un; Lee, U-Young; Kim, Dong-Ho; Kwak, Dai-Soon; Ahn, Yong-Woo; Han, Seung-Ho; Kim, Yi-Suk
2016-05-01
Body mass (BM) estimation could facilitate the interpretation of skeletal materials in terms of the individual's body size and physique in forensic anthropology. However, few metric studies have tried to estimate BM by focusing on prominent biomechanical properties of the calcaneus. The purpose of this study was to prepare best-fit models for estimating BM from the 3D human calcaneus by two major linear regression analysis (the heuristic statistical and all-possible-regressions techniques) and validate the models through predicted residual sum of squares (PRESS) statistics. A metric analysis was conducted based on 70 human calcaneus samples (29 males and 41 females) taken from 3D models in the Digital Korean Database and 10 variables were measured for each sample. Three best-fit models were postulated by F-statistics, Mallows' Cp, and Akaike information criterion (AIC) and Bayes information criterion (BIC) for each available candidate models. Finally, the most accurate regression model yields lowest %SEE and 0.843 of R(2). Through the application of leave-one-out cross validation, the predictive power was indicated a high level of validation accuracy. This study also confirms that the equations for estimating BM using 3D models of human calcaneus will be helpful to establish identification in forensic cases with consistent reliability. PMID:26970867
Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras
Morris, Mark; Sellers, William I.
2015-01-01
Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints. PMID:25780778
NASA Astrophysics Data System (ADS)
Mamou, Jonathan; Oelze, Michael L.; O'Brien, William D.; Zachary, James F.
2001-05-01
Accurate estimates of scatterer parameters (size and acoustic concentration) are beneficial adjuncts to characterize disease from ultrasonic backscatterer measurements. An estimation technique was developed to obtain parameter estimates from the Fourier transform of the spatial autocorrelation function (SAF). A 3D impedance map (3DZM) is used to obtain the SAF of tissue. 3DZMs are obtained by aligning digitized light microscope images from histologic preparations of tissue. Estimates were obtained for simulated 3DZMs containing spherical scatterers randomly located: relative errors were less than 3%. Estimates were also obtained from a rat fibroadenoma and a 4T1 mouse mammary tumor (MMT). Tissues were fixed (10% neutral-buffered formalin), embedded in paraffin, serially sectioned and stained with H&E. 3DZM results were compared to estimates obtained independently against ultrasonic backscatter measurements. For the fibroadenoma and MMT, average scatterer diameters were 91 and 31.5 μm, respectively. Ultrasonic measurements yielded average scatterer diameters of 105 and 30 μm, respectively. The 3DZM estimation scheme showed results similar to those obtained by the independent ultrasonic measurements. The 3D impedance maps show promise as a powerful tool to characterize ultrasonic scattering sites of tissue. [Work supported by the University of Illinois Research Board.
Bonci, T; Camomilla, V; Dumas, R; Chèze, L; Cappozzo, A
2015-11-26
When stereophotogrammetry and skin-markers are used, bone-pose estimation is jeopardised by the soft tissue artefact (STA). At marker-cluster level, this can be represented using a modal series of rigid (RT; translation and rotation) and non-rigid (NRT; homothety and scaling) geometrical transformations. The NRT has been found to be smaller than the RT and claimed to have a limited impact on bone-pose estimation. This study aims to investigate this matter and comparatively assessing the propagation of both STA components to bone-pose estimate, using different numbers of markers. Twelve skin-markers distributed over the anterior aspect of a thigh were considered and STA time functions were generated for each of them, as plausibly occurs during walking, using an ad hoc model and represented through the geometrical transformations. Using marker-clusters made of four to 12 markers affected by these STAs, and a Procrustes superimposition approach, bone-pose and the relevant accuracy were estimated. This was done also for a selected four marker-cluster affected by STAs randomly simulated by modifying the original STA NRT component, so that its energy fell in the range 30-90% of total STA energy. The pose error, which slightly decreased while increasing the number of markers in the marker-cluster, was independent from the NRT amplitude, and was always null when the RT component was removed. It was thus demonstrated that only the RT component impacts pose estimation accuracy and should thus be accounted for when designing algorithms aimed at compensating for STA. PMID:26555716
NASA Astrophysics Data System (ADS)
van der Wal, Wouter; Whitehouse, Pippa L.; Schrama, Ernst J. O.
2015-03-01
Seismic data indicate that there are large viscosity variations in the mantle beneath Antarctica. Consideration of such variations would affect predictions of models of Glacial Isostatic Adjustment (GIA), which are used to correct satellite measurements of ice mass change. However, most GIA models used for that purpose have assumed the mantle to be uniformly stratified in terms of viscosity. The goal of this study is to estimate the effect of lateral variations in viscosity on Antarctic mass balance estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data. To this end, recently-developed global GIA models based on lateral variations in mantle temperature are tuned to fit constraints in the northern hemisphere and then compared to GPS-derived uplift rates in Antarctica. We find that these models can provide a better fit to GPS uplift rates in Antarctica than existing GIA models with a radially-varying (1D) rheology. When 3D viscosity models in combination with specific ice loading histories are used to correct GRACE measurements, mass loss in Antarctica is smaller than previously found for the same ice loading histories and their preferred 1D viscosity profiles. The variation in mass balance estimates arising from using different plausible realizations of 3D viscosity amounts to 20 Gt/yr for the ICE-5G ice model and 16 Gt/yr for the W12a ice model; these values are larger than the GRACE measurement error, but smaller than the variation arising from unknown ice history. While there exist 1D Earth models that can reproduce the total mass balance estimates derived using 3D Earth models, the spatial pattern of gravity rates can be significantly affected by 3D viscosity in a way that cannot be reproduced by GIA models with 1D viscosity. As an example, models with 1D viscosity always predict maximum gravity rates in the Ross Sea for the ICE-5G ice model, however, for one of the three preferred 3D models the maximum (for the same ice model) is found
SU-E-J-237: Real-Time 3D Anatomy Estimation From Undersampled MR Acquisitions
Glitzner, M; Lagendijk, J; Raaymakers, B; Crijns, S; Senneville, B Denis de
2015-06-15
Recent developments made MRI guided radiotherapy feasible. Performing simultaneous imaging during fractions can provide information about changing anatomy by means of deformable image registration for either immediate plan adaptations or accurate dose accumulation on the changing anatomy. In 3D MRI, however, acquisition time is considerable and scales with resolution. Furthermore, intra-scan motion degrades image quality.In this work, we investigate the sensitivity of registration quality on imageresolution: potentially, by employing spatial undersampling, the acquisition timeof MR images for the purpose of deformable image registration can be reducedsignificantly.On a volunteer, 3D-MR imaging data was sampled in a navigator-gated manner, acquiring one axial volume (360×260×100mm{sup 3}) per 3s during exhale phase. A T1-weighted FFE sequence was used with an acquired voxel size of (2.5mm{sup 3}) for a duration of 17min. Deformation vector fields were evaluated for 100 imaging cycles with respect to the initial anatomy using deformable image registration based on optical flow. Subsequently, the imaging data was downsampled by a factor of 2, simulating a fourfold acquisition speed. Displacements of the downsampled volumes were then calculated by the same process.In kidneyliver boundaries and the region around stomach/duodenum, prominent organ drifts could be observed in both the original and the downsampled imaging data. An increasing displacement of approximately 2mm was observed for the kidney, while an area around the stomach showed sudden displacements of 4mm. Comparison of the motile points over time showed high reproducibility between the displacements of high-resolution and downsampled volumes: over a 17min acquisition, the componentwise RMS error was not more than 0.38mm.Based on the synthetic experiments, 3D nonrigid image registration shows little sensitivity to image resolution and the displacement information is preserved even when halving the
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-01-01
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the “non-progressing” and “progressing” glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection. PMID:25606299
NASA Astrophysics Data System (ADS)
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-03-01
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
CO2 mass estimation visible in time-lapse 3D seismic data from a saline aquifer and uncertainties
NASA Astrophysics Data System (ADS)
Ivanova, A.; Lueth, S.; Bergmann, P.; Ivandic, M.
2014-12-01
At Ketzin (Germany) the first European onshore pilot scale project for geological storage of CO2 was initiated in 2004. This project is multidisciplinary and includes 3D time-lapse seismic monitoring. A 3D pre-injection seismic survey was acquired in 2005. Then CO2 injection into a sandstone saline aquifer started at a depth of 650 m in 2008. A 1st 3D seismic repeat survey was acquired in 2009 after 22 kilotons had been injected. The imaged CO2 signature was concentrated around the injection well (200-300 m). A 2nd 3D seismic repeat survey was acquired in 2012 after 61 kilotons had been injected. The imaged CO2 signature further extended (100-200 m). The injection was terminated in 2013. Totally 67 kilotons of CO2 were injected. Time-lapse seismic processing, petrophysical data and geophysical logging on CO2 saturation have allowed for an estimate of the amount of CO2 visible in the seismic data. This estimate is dependent upon a choice of a number of parameters and contains a number of uncertainties. The main uncertainties are following. The constant reservoir porosity and CO2 density used for the estimation are probably an over-simplification since the reservoir is quite heterogeneous. May be velocity dispersion is present in the Ketzin reservoir rocks, but we do not consider it to be large enough that it could affect the mass of CO2 in our estimation. There are only a small number of direct petrophysical observations, providing a weak statistical basis for the determination of seismic velocities based on CO2 saturation and we have assumed that the petrophysical experiments were carried out on samples that are representative for the average properties of the whole reservoir. Finally, the most of the time delay values in the both 3D seismic repeat surveys within the amplitude anomaly are near the noise level of 1-2 ms, however a change of 1 ms in the time delay affects significantly the mass estimate, thus the choice of the time-delay cutoff is crucial. In spite
Hierarchical estimation of a dense deformation field for 3-D robust registration.
Hellier, P; Barillot, C; Mémin, E; Pérez, P
2001-05-01
A new method for medical image registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. At each stage of the hierarchical estimation, we refine current estimate by seeking a piecewise affine model for the incremental deformation field. The performance of this method is numerically evaluated on simulated data and its benefits and robustness are shown on a database of 18 magnetic resonance imaging scans of the head. PMID:11403198
NASA Astrophysics Data System (ADS)
Yulaeva, E.; Fan, Y.; Moosdorf, N.; Richard, S. M.; Bristol, S.; Peters, S. E.; Zaslavsky, I.; Ingebritsen, S.
2015-12-01
The Digital Crust EarthCube building block creates a framework for integrating disparate 3D/4D information from multiple sources into a comprehensive model of the structure and composition of the Earth's upper crust, and to demonstrate the utility of this model in several research scenarios. One of such scenarios is estimation of various crustal properties related to fluid dynamics (e.g. permeability and porosity) at each node of any arbitrary unstructured 3D grid to support continental-scale numerical models of fluid flow and transport. Starting from Macrostrat, an existing 4D database of 33,903 chronostratigraphic units, and employing GeoDeepDive, a software system for extracting structured information from unstructured documents, we construct 3D gridded fields of sediment/rock porosity, permeability and geochemistry for large sedimentary basins of North America, which will be used to improve our understanding of large-scale fluid flow, chemical weathering rates, and geochemical fluxes into the ocean. In this talk, we discuss the methods, data gaps (particularly in geologically complex terrain), and various physical and geological constraints on interpolation and uncertainty estimation.
SU-E-J-01: 3D Fluoroscopic Image Estimation From Patient-Specific 4DCBCT-Based Motion Models
Dhou, S; Hurwitz, M; Lewis, J; Mishra, P
2014-06-01
Purpose: 3D motion modeling derived from 4DCT images, taken days or weeks before treatment, cannot reliably represent patient anatomy on the day of treatment. We develop a method to generate motion models based on 4DCBCT acquired at the time of treatment, and apply the model to estimate 3D time-varying images (referred to as 3D fluoroscopic images). Methods: Motion models are derived through deformable registration between each 4DCBCT phase, and principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated based on cone-beam projections simulating kV treatment imaging. PCA coefficients are optimized iteratively through comparison of these cone-beam projections and projections estimated based on the motion model. Digital phantoms reproducing ten patient motion trajectories, and a physical phantom with regular and irregular motion derived from measured patient trajectories, are used to evaluate the method in terms of tumor localization, and the global voxel intensity difference compared to ground truth. Results: Experiments included: 1) assuming no anatomic or positioning changes between 4DCT and treatment time; and 2) simulating positioning and tumor baseline shifts at the time of treatment compared to 4DCT acquisition. 4DCBCT were reconstructed from the anatomy as seen at treatment time. In case 1) the tumor localization error and the intensity differences in ten patient were smaller using 4DCT-based motion model, possible due to superior image quality. In case 2) the tumor localization error and intensity differences were 2.85 and 0.15 respectively, using 4DCT-based motion models, and 1.17 and 0.10 using 4DCBCT-based models. 4DCBCT performed better due to its ability to reproduce daily anatomical changes. Conclusion: The study showed an advantage of 4DCBCT-based motion models in the context of 3D fluoroscopic images estimation. Positioning and tumor baseline shift uncertainties were mitigated by the 4DCBCT
A statistical approach to estimate the 3D size distribution of spheres from 2D size distributions
Kong, M.; Bhattacharya, R.N.; James, C.; Basu, A.
2005-01-01
Size distribution of rigidly embedded spheres in a groundmass is usually determined from measurements of the radii of the two-dimensional (2D) circular cross sections of the spheres in random flat planes of a sample, such as in thin sections or polished slabs. Several methods have been devised to find a simple factor to convert the mean of such 2D size distributions to the actual 3D mean size of the spheres without a consensus. We derive an entirely theoretical solution based on well-established probability laws and not constrained by limitations of absolute size, which indicates that the ratio of the means of measured 2D and estimated 3D grain size distribution should be r/4 (=.785). Actual 2D size distribution of the radii of submicron sized, pure Fe0 globules in lunar agglutinitic glass, determined from backscattered electron images, is tested to fit the gamma size distribution model better than the log-normal model. Numerical analysis of 2D size distributions of Fe0 globules in 9 lunar soils shows that the average mean of 2D/3D ratio is 0.84, which is very close to the theoretical value. These results converge with the ratio 0.8 that Hughes (1978) determined for millimeter-sized chondrules from empirical measurements. We recommend that a factor of 1.273 (reciprocal of 0.785) be used to convert the determined 2D mean size (radius or diameter) of a population of spheres to estimate their actual 3D size. ?? 2005 Geological Society of America.
Real-time estimation of FLE statistics for 3-D tracking with point-based registration.
Wiles, Andrew D; Peters, Terry M
2009-09-01
Target registration error (TRE) has become a widely accepted error metric in point-based registration since the error metric was introduced in the 1990s. It is particularly prominent in image-guided surgery (IGS) applications where point-based registration is used in both image registration and optical tracking. In point-based registration, the TRE is a function of the fiducial marker geometry, location of the target and the fiducial localizer error (FLE). While the first two items are easily obtained, the FLE is usually estimated using an a priori technique and applied without any knowledge of real-time information. However, if the FLE can be estimated in real-time, particularly as it pertains to optical tracking, then the TRE can be estimated more robustly. In this paper, a method is presented where the FLE statistics are estimated from the latest measurement of the fiducial registration error (FRE) statistics. The solution is obtained by solving a linear system of equations of the form Ax=b for each marker at each time frame where x are the six independent FLE covariance parameters and b are the six independent estimated FRE covariance parameters. The A matrix is only a function of the tool geometry and hence the inverse of the matrix can be computed a priori and used at each instant in which the FLE estimation is required, hence minimizing the level of computation at each frame. When using a good estimate of the FRE statistics, Monte Carlo simulations demonstrate that the root mean square of the FLE can be computed within a range of 70-90 microm. Robust estimation of the TRE for an optically tracked tool, using a good estimate of the FLE, will provide two enhancements in IGS. First, better patient to image registration will be obtained by using the TRE of the optical tool as a weighting factor of point-based registration used to map the patient to image space. Second, the directionality of the TRE can be relayed back to the surgeon giving the surgeon the option
NASA Astrophysics Data System (ADS)
Kalisperakis, I.; Stentoumis, Ch.; Grammatikopoulos, L.; Karantzalos, K.
2015-08-01
The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (i) hyperspectral data, (ii) 2D RGB orthophotomosaics and (iii) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (r2 > 73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.
NASA Astrophysics Data System (ADS)
Moreira, António H. J.; Queirós, Sandro; Morais, Pedro; Rodrigues, Nuno F.; Correia, André Ricardo; Fernandes, Valter; Pinho, A. C. M.; Fonseca, Jaime C.; Vilaça, João. L.
2015-03-01
The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant's pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant's pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant's main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant's pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67+/-34μm and 108μm, and angular misfits of 0.15+/-0.08° and 1.4°, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants' pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.
NASA Astrophysics Data System (ADS)
Arantes, Gilberto, Jr.; Marconi Rocco, Evandro; da Fonseca, Ijar M.; Theil, Stephan
2010-05-01
Space robotics has a substantial interest in achieving on-orbit satellite servicing operations autonomously, e.g. rendezvous and docking/berthing (RVD) with customer and malfunctioning satellites. An on-orbit servicing vehicle requires the ability to estimate the position and attitude in situations whenever the targets are uncooperative. Such situation comes up when the target is damaged. In this context, this work presents a robust autonomous pose system applied to RVD missions. Our approach is based on computer vision, using a single camera and some previous knowledge of the target, i.e. the customer spacecraft. A rendezvous analysis mission tool for autonomous service satellite has been developed and presented, for far maneuvers, e.g. distance above 1 km from the target, and close maneuvers. The far operations consist of orbit transfer using the Lambert formulation. The close operations include the inspection phase (during which the pose estimation is computed) and the final approach phase. Our approach is based on the Lambert problem for far maneuvers and the Hill equations are used to simulate and analyze the approaching and final trajectory between target and chase during the last phase of the rendezvous operation. A method for optimally estimating the relative orientation and position between camera system and target is presented in detail. The target is modelled as an assembly of points. The pose of the target is represented by dual quaternion in order to develop a simple quadratic error function in such a way that the pose estimation task becomes a least square minimization problem. The problem of pose is solved and some methods of non-linear square optimization (Newton, Newton-Gauss, and Levenberg-Marquard) are compared and discussed in terms of accuracy and computational cost.
NASA Astrophysics Data System (ADS)
Gunga, Hanns-Christian; Suthau, Tim; Bellmann, Anke; Friedrich, Andreas; Schwanebeck, Thomas; Stoinski, Stefan; Trippel, Tobias; Kirsch, Karl; Hellwich, Olaf
2007-08-01
Both body mass and surface area are factors determining the essence of any living organism. This should also hold true for an extinct organism such as a dinosaur. The present report discusses the use of a new 3D laser scanner method to establish body masses and surface areas of an Asian elephant (Zoological Museum of Copenhagen, Denmark) and of Plateosaurus engelhardti, a prosauropod from the Upper Triassic, exhibited at the Paleontological Museum in Tübingen (Germany). This method was used to study the effect that slight changes in body shape had on body mass for P. engelhardti. It was established that body volumes varied between 0.79 m3 (slim version) and 1.14 m3 (robust version), resulting in a presumable body mass of 630 and 912 kg, respectively. The total body surface areas ranged between 8.8 and 10.2 m2, of which, in both reconstructions of P. engelhardti, ˜33% account for the thorax area alone. The main difference between the two models is in the tail and hind limb reconstruction. The tail of the slim version has a surface area of 1.98 m2, whereas that of the robust version has a surface area of 2.73 m2. The body volumes calculated for the slim version were as follows: head 0.006 m3, neck 0.016 m3, fore limbs 0.020 m3, hind limbs 0.08 m3, thoracic cavity 0.533 m3, and tail 0.136 m3. For the robust model, the following volumes were established: 0.01 m3 head, neck 0.026 m3, fore limbs 0.025 m3, hind limbs 0.18 m3, thoracic cavity 0.616 m3, and finally, tail 0.28 m3. Based on these body volumes, scaling equations were used to assess the size that the organs of this extinct dinosaur have.
Estimation of vocal fold plane in 3D CT images for diagnosis of vocal fold abnormalities.
Hewavitharanage, Sajini; Gubbi, Jayavardhana; Thyagarajan, Dominic; Lau, Ken; Palaniswami, Marimuthu
2015-01-01
Vocal folds are the key body structures that are responsible for phonation and regulating air movement into and out of lungs. Various vocal fold disorders may seriously impact the quality of life. When diagnosing vocal fold disorders, CT of the neck is the commonly used imaging method. However, vocal folds do not align with the normal axial plane of a neck and the plane containing vocal cords and arytenoids does vary during phonation. It is therefore important to generate an algorithm for detecting the actual plane containing vocal folds. In this paper, we propose a method to automatically estimate the vocal fold plane using vertebral column and anterior commissure localization. Gray-level thresholding, connected component analysis, rule based segmentation and unsupervised k-means clustering were used in the proposed algorithm. The anterior commissure segmentation method achieved an accuracy of 85%, a good estimate of the expert assessment. PMID:26736949
Real-time geometric scene estimation for RGBD images using a 3D box shape grammar
NASA Astrophysics Data System (ADS)
Willis, Andrew R.; Brink, Kevin M.
2016-06-01
This article describes a novel real-time algorithm for the purpose of extracting box-like structures from RGBD image data. In contrast to conventional approaches, the proposed algorithm includes two novel attributes: (1) it divides the geometric estimation procedure into subroutines having atomic incremental computational costs, and (2) it uses a generative "Block World" perceptual model that infers both concave and convex box elements from detection of primitive box substructures. The end result is an efficient geometry processing engine suitable for use in real-time embedded systems such as those on an UAVs where it is intended to be an integral component for robotic navigation and mapping applications.
Estimation and 3-D modeling of seismic parameters for fluvial systems
Brown, R.L.; Levey, R.A.
1994-12-31
Borehole measurements of parameters related to seismic propagation (Vp, Vs, Qp and Qs) are seldom available at all the wells within an area of study. Well logs and other available data can be used along with certain results from laboratory measurements to predict seismic parameters at wells where these measurements are not available. Next, three dimensional interpolation techniques based upon geological constraints can then be used to estimate the spatial distribution of geophysical parameters within a given environment. The net product is a more realistic model of the distribution of geophysical parameters which can be used in the design of surface and borehole seismic methods for probing the reservoir.
3D whiteboard: collaborative sketching with 3D-tracked smart phones
NASA Astrophysics Data System (ADS)
Lue, James; Schulze, Jürgen P.
2014-02-01
We present the results of our investigation of the feasibility of a new approach for collaborative drawing in 3D, based on Android smart phones. Our approach utilizes a number of fiduciary markers, placed in the working area where they can be seen by the smart phones' cameras, in order to estimate the pose of each phone in the room. Our prototype allows two users to draw 3D objects with their smart phones by moving their phones around in 3D space. For example, 3D lines are drawn by recording the path of the phone as it is moved around in 3D space, drawing line segments on the screen along the way. Each user can see the virtual drawing space on their smart phones' displays, as if the display was a window into this space. Besides lines, our prototype application also supports 3D geometry creation, geometry transformation operations, and it shows the location of the other user's phone.
Real-Time Estimation of 3-D Needle Shape and Deflection for MRI-Guided Interventions
Park, Yong-Lae; Elayaperumal, Santhi; Daniel, Bruce; Ryu, Seok Chang; Shin, Mihye; Savall, Joan; Black, Richard J.; Moslehi, Behzad; Cutkosky, Mark R.
2015-01-01
We describe a MRI-compatible biopsy needle instrumented with optical fiber Bragg gratings for measuring bending deflections of the needle as it is inserted into tissues. During procedures, such as diagnostic biopsies and localized treatments, it is useful to track any tool deviation from the planned trajectory to minimize positioning errors and procedural complications. The goal is to display tool deflections in real time, with greater bandwidth and accuracy than when viewing the tool in MR images. A standard 18 ga × 15 cm inner needle is prepared using a fixture, and 350-μm-deep grooves are created along its length. Optical fibers are embedded in the grooves. Two sets of sensors, located at different points along the needle, provide an estimate of the bent profile, as well as temperature compensation. Tests of the needle in a water bath showed that it produced no adverse imaging artifacts when used with the MR scanner. PMID:26405428
Curiale, Ariel H; Vegas-Sánchez-Ferrero, Gonzalo; Bosch, Johan G; Aja-Fernández, Santiago
2015-08-01
The strain and strain-rate measures are commonly used for the analysis and assessment of regional myocardial function. In echocardiography (EC), the strain analysis became possible using Tissue Doppler Imaging (TDI). Unfortunately, this modality shows an important limitation: the angle between the myocardial movement and the ultrasound beam should be small to provide reliable measures. This constraint makes it difficult to provide strain measures of the entire myocardium. Alternative non-Doppler techniques such as Speckle Tracking (ST) can provide strain measures without angle constraints. However, the spatial resolution and the noisy appearance of speckle still make the strain estimation a challenging task in EC. Several maximum likelihood approaches have been proposed to statistically characterize the behavior of speckle, which results in a better performance of speckle tracking. However, those models do not consider common transformations to achieve the final B-mode image (e.g. interpolation). This paper proposes a new maximum likelihood approach for speckle tracking which effectively characterizes speckle of the final B-mode image. Its formulation provides a diffeomorphic scheme than can be efficiently optimized with a second-order method. The novelty of the method is threefold: First, the statistical characterization of speckle generalizes conventional speckle models (Rayleigh, Nakagami and Gamma) to a more versatile model for real data. Second, the formulation includes local correlation to increase the efficiency of frame-to-frame speckle tracking. Third, a probabilistic myocardial tissue characterization is used to automatically identify more reliable myocardial motions. The accuracy and agreement assessment was evaluated on a set of 16 synthetic image sequences for three different scenarios: normal, acute ischemia and acute dyssynchrony. The proposed method was compared to six speckle tracking methods. Results revealed that the proposed method is the most
NASA Astrophysics Data System (ADS)
Liang, Liang; Martin, Caitlin; Wang, Qian; Sun, Wei; Duncan, James
2016-03-01
Aortic valve (AV) disease is a significant cause of morbidity and mortality. The preferred treatment modality for severe AV disease is surgical resection and replacement of the native valve with either a mechanical or tissue prosthetic. In order to develop effective and long-lasting treatment methods, computational analyses, e.g., structural finite element (FE) and computational fluid dynamic simulations, are very effective for studying valve biomechanics. These computational analyses are based on mesh models of the aortic valve, which are usually constructed from 3D CT images though many hours of manual annotation, and therefore an automatic valve shape reconstruction method is desired. In this paper, we present a method for estimating the aortic valve shape from 3D cardiac CT images, which is represented by triangle meshes. We propose a pipeline for aortic valve shape estimation which includes novel algorithms for building local shape dictionaries and for building landmark detectors and curve detectors using local shape dictionaries. The method is evaluated on real patient image dataset using a leave-one-out approach and achieves an average accuracy of 0.69 mm. The work will facilitate automatic patient-specific computational modeling of the aortic valve.
A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images
NASA Astrophysics Data System (ADS)
Shamsil, Arefin; Escoto, Abelardo; Naish, Michael D.; Patel, Rajni V.
2016-03-01
Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation (r = -0.783, p = 0.004) between the tumor surface margins and a good positive correlation (r = 0.743, p = 0.009) between the tumor depth margins.
NASA Astrophysics Data System (ADS)
Wu, Shunguang; Hong, Lang
2008-04-01
A framework of simultaneously estimating the motion and structure parameters of a 3D object by using high range resolution (HRR) and ground moving target indicator (GMTI) measurements with template information is given. By decoupling the motion and structure information and employing rigid-body constraints, we have developed the kinematic and measurement equations of the problem. Since the kinematic system is unobservable by using only one scan HRR and GMTI measurements, we designed an architecture to run the motion and structure filters in parallel by using multi-scan measurements. Moreover, to improve the estimation accuracy in large noise and/or false alarm environments, an interacting multi-template joint tracking (IMTJT) algorithm is proposed. Simulation results have shown that the averaged root mean square errors for both motion and structure state vectors have been significantly reduced by using the template information.
Landscape scale estimation of soil carbon stock using 3D modelling.
Veronesi, F; Corstanje, R; Mayr, T
2014-07-15
Soil C is the largest pool of carbon in the terrestrial biosphere, and yet the processes of C accumulation, transformation and loss are poorly accounted for. This, in part, is due to the fact that soil C is not uniformly distributed through the soil depth profile and most current landscape level predictions of C do not adequately account the vertical distribution of soil C. In this study, we apply a method based on simple soil specific depth functions to map the soil C stock in three-dimensions at landscape scale. We used soil C and bulk density data from the Soil Survey for England and Wales to map an area in the West Midlands region of approximately 13,948 km(2). We applied a method which describes the variation through the soil profile and interpolates this across the landscape using well established soil drivers such as relief, land cover and geology. The results indicate that this mapping method can effectively reproduce the observed variation in the soil profiles samples. The mapping results were validated using cross validation and an independent validation. The cross-validation resulted in an R(2) of 36% for soil C and 44% for BULKD. These results are generally in line with previous validated studies. In addition, an independent validation was undertaken, comparing the predictions against the National Soil Inventory (NSI) dataset. The majority of the residuals of this validation are between ± 5% of soil C. This indicates high level of accuracy in replicating topsoil values. In addition, the results were compared to a previous study estimating the carbon stock of the UK. We discuss the implications of our results within the context of soil C loss factors such as erosion and the impact on regional C process models. PMID:24636454
Paone, Jeffrey R; Bolme, David S; Ferrell, Regina Kay; Aykac, Deniz; Karnowski, Thomas Paul
2015-01-01
Keeping a driver focused on the road is one of the most critical steps in insuring the safe operation of a vehicle. The Strategic Highway Research Program 2 (SHRP2) has over 3,100 recorded videos of volunteer drivers during a period of 2 years. This extensive naturalistic driving study (NDS) contains over one million hours of video and associated data that could aid safety researchers in understanding where the driver s attention is focused. Manual analysis of this data is infeasible, therefore efforts are underway to develop automated feature extraction algorithms to process and characterize the data. The real-world nature, volume, and acquisition conditions are unmatched in the transportation community, but there are also challenges because the data has relatively low resolution, high compression rates, and differing illumination conditions. A smaller dataset, the head pose validation study, is available which used the same recording equipment as SHRP2 but is more easily accessible with less privacy constraints. In this work we report initial head pose accuracy using commercial and open source face pose estimation algorithms on the head pose validation data set.
NASA Astrophysics Data System (ADS)
Rottner, L.; Baehr, C.
2014-12-01
Turbulent phenomena in the atmospheric boundary layer (ABL) are characterized by small spatial and temporal scales which make them difficult to observe and to model.New remote sensing instruments, like Doppler Lidar, give access to fine and high-frequency observations of wind in the ABL. This study suggests to use a method of nonlinear estimation based on these observations to reconstruct 3D wind in a hemispheric volume, and to estimate atmospheric turbulent parameters. The wind observations are associated to particle systems which are driven by a local turbulence model. The particles have both fluid and stochastic properties. Therefore, spatial averages and covariances may be deduced from the particles. Among the innovative aspects, we point out the absence of the common hypothesis of stationary-ergodic turbulence and the non-use of particle model closure hypothesis. Every time observations are available, 3D wind is reconstructed and turbulent parameters such as turbulent kinectic energy, dissipation rate, and Turbulent Intensity (TI) are provided. This study presents some results obtained using real wind measurements provided by a five lines of sight Lidar. Compared with classical methods (e.g. eddy covariance) our technic renders equivalent long time results. Moreover it provides finer and real time turbulence estimations. To assess this new method, we suggest computing independently TI using different observation types. First anemometer data are used to have TI reference.Then raw and filtered Lidar observations have also been compared. The TI obtained from raw data is significantly higher than the reference one, whereas the TI estimated with the new algorithm has the same order.In this study we have presented a new class of algorithm to reconstruct local random media. It offers a new way to understand turbulence in the ABL, in both stable or convective conditions. Later, it could be used to refine turbulence parametrization in meteorological meso-scale models.
NASA Astrophysics Data System (ADS)
Bradford, John H.; Clement, William P.; Barrash, Warren
2009-04-01
To evaluate the uncertainty of water-saturated sediment velocity and porosity estimates derived from surface-based, ground-penetrating radar reflection tomography, we conducted a controlled field experiment at the Boise Hydrogeophysical Research Site (BHRS). The BHRS is an experimental well field located near Boise, Idaho. The experimental data set consisted of 3-D multioffset radar acquired on an orthogonal 20 × 30 m surface grid that encompassed a set of 13 boreholes. Experimental control included (1) 1-D vertical velocity functions determined from traveltime inversion of vertical radar profiles (VRP) and (2) neutron porosity logs. We estimated the porosity distribution in the saturated zone using both the Topp and Complex Refractive Index Method (CRIM) equations and found the CRIM estimates in better agreement with the neutron logs. We found that when averaged over the length of the borehole, surface-derived velocity measurements were within 5% of the VRP velocities and that the porosity differed from the neutron log by less than 0.05. The uncertainty, however, is scale dependent. We found that the standard deviation of differences between ground-penetrating-radar-derived and neutron-log-derived porosity values was as high as 0.06 at an averaging length of 0.25 m but decreased to less than 0.02 at length scale of 11 m. Additionally, we used the 3-D porosity distribution to identify a relatively high-porosity anomaly (i.e., local sedimentary body) within a lower-porosity unit and verified the presence of the anomaly using the neutron porosity logs. Since the reflection tomography approach requires only surface data, it can provide rapid assessment of bulk hydrologic properties, identify meter-scale anomalies of hydrologic significance, and may provide input for other higher-resolution measurement methods.
NASA Astrophysics Data System (ADS)
Abbott, W. W.; Faisal, A. A.
2012-08-01
Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s-1, more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark—the control of the video arcade game ‘Pong’.
NASA Astrophysics Data System (ADS)
Svalkvist, Angelica; Hansson, Jonny; Bâth, Magnus
2014-03-01
Three-dimensional (3D) imaging with interventional fluoroscopy systems is today a common examination. The examination includes acquisition of two-dimensional projection images, used to reconstruct section images of the patient. The aim of the present study was to investigate the difference in resulting effective dose obtained using different levels of complexity in calculations of effective doses from these examinations. In the study the Siemens Artis Zeego interventional fluoroscopy system (Siemens Medical Solutions, Erlangen, Germany) was used. Images of anthropomorphic chest and pelvis phantoms were acquired. The exposure values obtained were used to calculate the resulting effective doses from the examinations, using the computer software PCXMC (STUK, Helsinki, Finland). The dose calculations were performed using three different methods: 1. using individual exposure values for each projection image, 2. using the mean tube voltage and the total DAP value, evenly distributed over the projection images, and 3. using the mean kV and the total DAP value, evenly distributed over smaller selection of projection images. The results revealed that the difference in resulting effective dose between the first two methods was smaller than 5%. When only a selection of projection images were used in the dose calculations the difference increased to over 10%. Given the uncertainties associated with the effective dose concept, the results indicate that dose calculations based on average exposure values distributed over a smaller selection of projection angles can provide reasonably accurate estimations of the radiation doses from 3D imaging using interventional fluoroscopy systems.
Matejczyk, Marek; Płaza, Grażyna A; Nałęcz-Jawecki, Grzegorz; Ulfig, Krzysztof; Markowska-Szczupak, Agata
2011-02-01
parameters of the landfill leachates should be analyzed together to assess the environmental risk posed by landfill emissions. PMID:21087786
Distributed consensus on camera pose.
Jorstad, Anne; DeMenthon, Daniel; Wang, I-Jeng; Burlina, Philippe
2010-09-01
Our work addresses pose estimation in a distributed camera framework. We examine how processing cameras can best reach a consensus about the pose of an object when they are each given a model of the object, defined by a set of point coordinates in the object frame of reference. The cameras can only see a subset of the object feature points in the midst of background clutter points, not knowing which image points match with which object points, nor which points are object points or background points. The cameras individually recover a prediction of the object's pose using their knowledge of the model, and then exchange information with their neighbors, performing consensus updates locally to obtain a single estimate consistent across all cameras, without requiring a common centralized processor. Our main contributions are: 1) we present a novel algorithm performing consensus updates in 3-D world coordinates penalized by a 3-D model, and 2) we perform a thorough comparison of our method with other current consensus methods. Our method is consistently the most accurate, and we confirm that the existing consensus method based upon calculating the Karcher mean of rotations is also reliable and fast. Experiments on simulated and real imagery are reported. PMID:20363678
NASA Astrophysics Data System (ADS)
Püthe, Christoph; Manoj, Chandrasekharan; Kuvshinov, Alexey
2015-04-01
Electric fields induced in the conducting Earth during magnetic storms drive currents in power transmission grids, telecommunication lines or buried pipelines. These geomagnetically induced currents (GIC) can cause severe service disruptions. The prediction of GIC is thus of great importance for public and industry. A key step in the prediction of the hazard to technological systems during magnetic storms is the calculation of the geoelectric field. To address this issue for mid-latitude regions, we developed a method that involves 3-D modelling of induction processes in a heterogeneous Earth and the construction of a model of the magnetospheric source. The latter is described by low-degree spherical harmonics; its temporal evolution is derived from observatory magnetic data. Time series of the electric field can be computed for every location on Earth's surface. The actual electric field however is known to be perturbed by galvanic effects, arising from very local near-surface heterogeneities or topography, which cannot be included in the conductivity model. Galvanic effects are commonly accounted for with a real-valued time-independent distortion matrix, which linearly relates measured and computed electric fields. Using data of various magnetic storms that occurred between 2000 and 2003, we estimated distortion matrices for observatory sites onshore and on the ocean bottom. Strong correlations between modellings and measurements validate our method. The distortion matrix estimates prove to be reliable, as they are accurately reproduced for different magnetic storms. We further show that 3-D modelling is crucial for a correct separation of galvanic and inductive effects and a precise prediction of electric field time series during magnetic storms. Since the required computational resources are negligible, our approach is suitable for a real-time prediction of GIC. For this purpose, a reliable forecast of the source field, e.g. based on data from satellites
Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector
NASA Astrophysics Data System (ADS)
Kniaz, V. V.
2016-06-01
Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.
NASA Astrophysics Data System (ADS)
Liu, Yonghuai; Rodrigues, Marcos A.
2000-03-01
This paper describes research on the application of machine vision techniques to a real time automatic inspection task of air filter components in a manufacturing line. A novel calibration algorithm is proposed based on a special camera setup where defective items would show a large calibration error. The algorithm makes full use of rigid constraints derived from the analysis of geometrical properties of reflected correspondence vectors which have been synthesized into a single coordinate frame and provides a closed form solution to the estimation of all parameters. For a comparative study of performance, we also developed another algorithm based on this special camera setup using epipolar geometry. A number of experiments using synthetic data have shown that the proposed algorithm is generally more accurate and robust than the epipolar geometry based algorithm and that the geometric properties of reflected correspondence vectors provide effective constraints to the calibration of rigid body transformations.
NASA Astrophysics Data System (ADS)
Smith, P.; Nichols, N. K.; Dance, S.
2011-12-01
Data assimilation is typically used to provide initial conditions for state estimation; combining model predictions with observational data to produce an updated model state that most accurately characterises the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. However, even with perfect initial data, inaccurate representation of model parameters will lead to the growth of model error and therefore affect the ability of our model to accurately predict the true system state. A key question in model development is how to estimate parameters a priori. In most cases, parameter estimation is addressed as a separate issue to state estimation and model calibration is performed offline in a separate calculation. Here we demonstrate how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state as part of the assimilation process. We present a novel hybrid data assimilation algorithm developed for application to parameter estimation in morphodynamic models. The new approach is based on a computationally inexpensive 3D-Var scheme, where the specification of the covariance matrices is crucial for success. For combined state-parameter estimation, it is particularly important that the cross-covariances between the parameters and the state are given a good a priori specification. Early experiments indicated that in order to yield reliable estimates of the true parameters, a flow dependent representation of the state-parameter cross covariances is required. By combining ideas from 3D-Var and the extended Kalman filter we have developed a novel hybrid assimilation scheme that captures the flow dependent nature of the state-parameter cross covariances without the computational expense of explicitly propagating the full system covariance matrix. We will give details of the formulation of this