Conferences related to Structure From Motion

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)

AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Structure From Motion

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Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


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Most published Xplore authors for Structure From Motion

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Xplore Articles related to Structure From Motion

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Robust 3D reconstruction with omni-directional camera based on structure from motion

2018 International Workshop on Advanced Image Technology (IWAIT), 2018

3D scenes reconstruction with omni-directional camera is of utmost significance as the camera can capture the 360 degrees scene. It dramatically improves the performance of Structure from Motion (SfM) since the tracked interest points will not miss with a long base-line and sharp rotation. This paper proposes a system with preprocessing and modification based on conventional SfM pipeline for equirectangular ...


Observability Properties and Deterministic Algorithms in Visual-Inertial Structure from Motion

Observability Properties and Deterministic Algorithms in Visual-Inertial Structure from Motion, None

The term Structure from Motion (SfM) was coined by the computer vision community to define the problem of estimating the three-dimensional structure of the scene and the motion from two-dimensional image sequences. This monograph considers the same estimation problem but where the sensor suit is also composed of inertial sensors (accelerometers and gyroscopes). This problem is referred to as the ...


Odometry-Based Structure from Motion

2007 IEEE Intelligent Vehicles Symposium, 2007

Structure from motion refers to a technique to obtain 3D information from consecutive images taken with a moving monocular camera. In order to do this, the camera motion performed between two consecutive images needs to be known. In the work reported in this contribution, we investigated the precision of the odometry data of a commercially available passenger car. In order ...


Structure from motion for 3D object reconstruction based on local and global bundle adjustment

2015 Third World Conference on Complex Systems (WCCS), 2015

Structure from motion approach allows to recover both the 3D structure of the scene and the camera motion. It uses a global bundle adjustment by minimizing a non-linear criterion (often through the use of the Levenberg-Marquardt algorithm) to adjust the various entities initially estimated, which requires a long calculation time and can converge to a local solution due to a ...


SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM ...


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Educational Resources on Structure From Motion

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IEEE-USA E-Books

  • Robust 3D reconstruction with omni-directional camera based on structure from motion

    3D scenes reconstruction with omni-directional camera is of utmost significance as the camera can capture the 360 degrees scene. It dramatically improves the performance of Structure from Motion (SfM) since the tracked interest points will not miss with a long base-line and sharp rotation. This paper proposes a system with preprocessing and modification based on conventional SfM pipeline for equirectangular images produced by omni- directional camera. The results show that proposed system can well estimate the accurate ego-motion and sparse 3D structure of a synthetic scene as well as a real-world scene by solving the problem of distortion and lower quality at certain area of the equirectangular images.

  • Observability Properties and Deterministic Algorithms in Visual-Inertial Structure from Motion

    The term Structure from Motion (SfM) was coined by the computer vision community to define the problem of estimating the three-dimensional structure of the scene and the motion from two-dimensional image sequences. This monograph considers the same estimation problem but where the sensor suit is also composed of inertial sensors (accelerometers and gyroscopes). This problem is referred to as the Visual-Inertial Structure from Motion (VI-SfM). The VI-SfM problem has generated particular interest and has been investigated by both computer science and neuroscience. These sensors require no external infrastructure which is a key advantage for robots operating in unknown environments where GPS signals are shadowed. For this reason, vision and inertial sensing have received great attention from within the mobile robotics community in recent years and many approaches have been introduced. Observability Properties and Deterministic Algorithms in Visual-Inertial Structure from Motion provides the reader with the state of the art in VI-SfM and also adds a series of new results. In particular, these new results significantly improve the current state of the art by providing new properties related to three fundamental issues: observability properties, resolvability in closed-form and data association. These results are important from a technological point of view. Additionally, they can provide a new insight for the comprehension of the process of vestibular and visual integration, which has been investigated in the framework of neuroscience.

  • Odometry-Based Structure from Motion

    Structure from motion refers to a technique to obtain 3D information from consecutive images taken with a moving monocular camera. In order to do this, the camera motion performed between two consecutive images needs to be known. In the work reported in this contribution, we investigated the precision of the odometry data of a commercially available passenger car. In order to identify the required precision, we developed an error model based on camera parameters and the bicycle model. We investigated two options, both being based on speed measurements. The first one uses steering angle measurements, the second one uses measurements of the yaw rate. Concluding, we found out that the specified precision of all odometry data available is sufficient to solve structure from motion. Long-term measurements empirically confirm the precision values given in the specification. This result encouraged us to actually implement a structure-from-motion approach which yields depth information as predicted from the theoretical considerations. Further work needs to be carried out in order to compensate for roll motions.

  • Structure from motion for 3D object reconstruction based on local and global bundle adjustment

    Structure from motion approach allows to recover both the 3D structure of the scene and the camera motion. It uses a global bundle adjustment by minimizing a non-linear criterion (often through the use of the Levenberg-Marquardt algorithm) to adjust the various entities initially estimated, which requires a long calculation time and can converge to a local solution due to a bad initialization. In this paper, we used the Structure from Motion approach for 3D object reconstruction from images taken by a single camera moving around the object. The proposed approach is based on the combination of the local bundle adjustment (LBA) and the global bundle adjustment (GBA) which is very useful to ensure effective and rapid convergence to the optimal solution. Our reconstruction system is initialized from two calibrated images. After each insertion of a new uncalibrated Image, we integrate a LBA to adjust the new estimated parameters and avoid as much as possible the accumulation of errors which can affect the system's stability. After the insertion of the last image, a GBA is performed to adjust as best as possible all the estimated entities already refined locally (3D points and camera parameters). Experimental results show the reliability and rapidity of the proposed approach compared to the classical approach.

  • SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion

    Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM based on finding a coarse initial solution using hybrid discrete-continuous optimization and then improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random field (MRF) formulation, coupled with a continuous Levenberg-Marquardt refinement. The formulation naturally incorporates various sources of information about both the cameras and points, including noisy geotags and vanishing point (VP) estimates. We test our method on several large-scale photo collections, including one with measured camera positions, and show that it produces models that are similar to or better than those produced by incremental bundle adjustment, but more robustly and in a fraction of the time.

  • Probe localization using structure from motion for 3D ultrasound image reconstruction

    This paper proposes an accurate ultrasound probe localization method for 3D US image reconstruction. The proposed method consists of (i) feature tracking of a video sequence and (ii) camera pose estimation using structure from motion (SfM). SfM is used to reconstruct 3D point clouds from multiple-view images and simultaneously estimate each camera position. To apply SfM to a video sequence, the accurate method is required to track features between adjacent frames. We employ a sub-pixel image matching technique using Phase-Only Correlation (POC) for feature tracking. POC is a technique of image matching using phase components in the Fourier transforms of images. Through a set of experiments, we demonstrate that the proposed method can estimate the location of the US probe with about 2mm error for the probe travel distance of 150-200mm.

  • Pushing the limit of non-rigid structure-from-motion by shape clustering

    Recovering both camera motions and non-rigid 3D shapes from 2D feature tracks is a challenging problem in computer vision. Long-term, complex non-rigid shape variations in real world videos further increase the difficulty for Non- rigid structure-from-motion (NRSfM). Furthermore, there does not exist a criterion to characterize the possibility in recovering the non-rigid shapes and camera motions (i.e., how easy or how difficult the problem could be). In this paper, we first present an analysis to the "reconstructability" measure for NRSfM, where we show that 3D shape complexity and camera motion complexity can be used to index the re-constructability. We propose an iterative shape clustering based method to NRSfM, which alternates between 3D shape clustering and 3D shape reconstruction. Thus, the global reconstructability has been improved and better reconstruction can be achieved. Experimental results on long-term, complex non-rigid motion sequences show that our method outperforms the current state-of-the-art methods by a margin.

  • Application of Structure-from-Motion 3D Reconstruction in Computer-Guided Surgical Training

    The following paper presents the effects of work on evaluating application of structure-from-motion 3D reconstruction algorithms to the purpose of computer- guided surgical training. In the paper a system for laparoscopic surgery training is presented. The key part of the system is a novel approach to training, where a 3D model of the operating fields serves as a base of the interaction between the trainee and the system. Outline of the 3D processing pipeline is presented, results are shown and discussed.

  • Using structure from motion for monument 3D reconstruction from images with heterogeneous background

    In the paper the task of 3D reconstruction of the cultural heritage objects, historical and cultural monuments is considered. The problems of performing this task in the complex environment are explained. The Structure from Motion (SfM) technique is chosen. It is implemented by example of 3D reconstruction of the Sergei Yesenin monument in the Ryazan Kremlin from its images. The technique of the foreground detection for the images with heterogeneous background is proposed. It is used in the modified SfM technique. The results of using this technique are illustrated.

  • Accuracy analysis of UAV remote sensing imagery mosaicking based on structure-from-motion

    Structure-From-Motion (SFM) method is based on the same scene and different angles of the captured sequence of image, then calculate the feature points in the photogrammetric coordinate system of three-dimensional coordinates and camera parameters. SFM can directly generated orthophoto map just by captured overlapping images, but mosaicking accuracy has not to be verified. The purpose of this study is that verify the feasibility and accuracy of SFM method in UAV image mosaic. The process of UAV imagery mosaicking based on SFM method was elaborated, and the test image was mosaicked with UAV imagery processing software which based on SFM. The result: (1) UAV imagery mosaicking based on SFM algorithm has low accuracy on geographic positioning because of the low precision POS. But the distance\area measurement with high accuracy, the perimeter accuracy is above 96.6% and the area accuracy is above 93.2%. (2) The image had high accuracy after geometric correction using ground points. When 5 ground points were used, the mean value of absolute error was 0.60 m. The study showed: (1) the accuracy of perimeter and area can basically meet the accuracy requirements of distance\area measurement in agricultural applications. (2) the orthophoto map was rectified by ground control point can significantly improve the geo-location precision of the image.



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