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Hedborg, Johan
Publications (10 of 17) Show all publications
Hedborg, J., Robinson, A. & Felsberg, M. (2014). Robust Three-View Triangulation Done Fast. In: Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014: . Paper presented at IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 23-28, Columbus, OH, USA (pp. 152-157). IEEE
Open this publication in new window or tab >>Robust Three-View Triangulation Done Fast
2014 (English)In: Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, IEEE , 2014, p. 152-157Conference paper, Published paper (Refereed)
Abstract [en]

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

Place, publisher, year, edition, pages
IEEE, 2014
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
Keywords
Nonlinear optimization; Structure from motion; Three-view Triangulation; Cameras; Computer vision; Conferences; Noise; Polynomials; Robustness; Three-dimensional displays
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-111512 (URN)10.1109/CVPRW.2014.28 (DOI)000349552300023 ()978-1-4799-4309-8 (ISBN)
Conference
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 23-28, Columbus, OH, USA
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2018-10-09Bibliographically approved
Ellis, L., Pugeault, N., Öfjäll, K., Hedborg, J., Bowden, R. & Felsberg, M. (2013). Autonomous Navigation and Sign Detector Learning. In: IEEE Workshop on Robot Vision(WORV) 2013: . Paper presented at IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL, USA (pp. 144-151). IEEE
Open this publication in new window or tab >>Autonomous Navigation and Sign Detector Learning
Show others...
2013 (English)In: IEEE Workshop on Robot Vision(WORV) 2013, IEEE , 2013, p. 144-151Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time.

Place, publisher, year, edition, pages
IEEE, 2013
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-86214 (URN)10.1109/WORV.2013.6521929 (DOI)978-1-4673-5647-3 (ISBN)978-1-4673-5646-6 (ISBN)
Conference
IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL, USA
Projects
ELLIITETTCUASUK EPSRC: EP/H023135/1
Available from: 2012-12-11 Created: 2012-12-11 Last updated: 2016-06-14
Hedborg, J. & Felsberg, M. (2013). Fast Iterative Five point Relative Pose Estimation. In: : . Paper presented at IEEE Workshop on Robot Vision (WoRV 2013), January 15-17, 2013, Clearwater, FL, USA (pp. 60-67). IEEE conference proceedings
Open this publication in new window or tab >>Fast Iterative Five point Relative Pose Estimation
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nistér [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell's Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister's algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-90102 (URN)10.1109/WORV.2013.6521915 (DOI)978-1-4673-5647-3 (ISBN)978-1-4673-5646-6 (ISBN)
Conference
IEEE Workshop on Robot Vision (WoRV 2013), January 15-17, 2013, Clearwater, FL, USA
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IS11-0081
Available from: 2013-03-22 Created: 2013-03-19 Last updated: 2016-05-04Bibliographically approved
Hedborg, J. (2012). Motion and Structure Estimation From Video. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Motion and Structure Estimation From Video
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Digital camera equipped cell phones were introduced in Japan in 2001, they quickly became popular and by 2003 outsold the entire stand-alone digital camera market. In 2010 sales passed one billion units and the market is still growing. Another trend is the rising popularity of smartphones which has led to a rapid development of the processing power on a phone, and many units sold today bear close resemblance to a personal computer. The combination of a powerful processor and a camera which is easily carried in your pocket, opens up a large eld of interesting computer vision applications.

The core contribution of this thesis is the development of methods that allow an imaging device such as the cell phone camera to estimates its own motion and to capture the observed scene structure. One of the main focuses of this thesis is real-time performance, where a real-time constraint does not only result in shorter processing times, but also allows for user interaction.

In computer vision, structure from motion refers to the process of estimating camera motion and 3D structure by exploring the motion in the image plane caused by the moving camera. This thesis presents several methods for estimating camera motion. Given the assumption that a set of images has known camera poses associated to them, we train a system to solve the camera pose very fast for a new image. For the cases where no a priory information is available a fast minimal case solver is developed. The solver uses ve points in two camera views to estimate the cameras relative position and orientation. This type of minimal case solver is usually used within a RANSAC framework. In order to increase accuracy and performance a renement to the random sampling strategy of RANSAC is proposed. It is shown that the new scheme doubles the performance for the ve point solver used on video data. For larger systems of cameras a new Bundle Adjustment method is developed which are able to handle video from cell phones.

Demands for reduction in size, power consumption and price has led to a redesign of the image sensor. As a consequence the sensors have changed from a global shutter to a rolling shutter, where a rolling shutter image is acquired row by row. Classical structure from motion methods are modeled on the assumption of a global shutter and a rolling shutter can severely degrade their performance. One of the main contributions of this thesis is a new Bundle Adjustment method for cameras with a rolling shutter. The method accurately models the camera motion during image exposure with an interpolation scheme for both position and orientation.

The developed methods are not restricted to cellphones only, but is rather applicable to any type of mobile platform that is equipped with cameras, such as a autonomous car or a robot. The domestic robot comes in many  avors, everything from vacuum cleaners to service and pet robots. A robot equipped with a camera that is capable of estimating its own motion while sensing its environment, like the human eye, can provide an eective means of navigation for the robot. Many of the presented methods are well suited of robots, where low latency and real-time constraints are crucial in order to allow them to interact with their environment.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. p. 42
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1449
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76904 (URN)978-91-7519-892-7 (ISBN)
Public defence
2012-05-16, Visionen, hus B, Campus Valla, Linköpings Universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Projects
Virtual Photo Set (VPS)
Available from: 2012-04-24 Created: 2012-04-24 Last updated: 2019-12-08Bibliographically approved
Hedborg, J., Forssén, P.-E., Felsberg, M. & Ringaby, E. (2012). Rolling Shutter Bundle Adjustment. In: : . Paper presented at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012 (pp. 1434-1441). IEEE Computer Society; 1999
Open this publication in new window or tab >>Rolling Shutter Bundle Adjustment
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a bundle adjustment (BA) method that obtains accurate structure and motion from rolling shutter (RS) video sequences: RSBA. When a classical BA algorithm processes a rolling shutter video, the resultant camera trajectory is brittle, and complete failures are not uncommon. We exploit the temporal continuity of the camera motion to define residuals of image point trajectories with respect to the camera trajectory. We compare the camera trajectories from RSBA to those from classical BA, and from classical BA on rectified videos. The comparisons are done on real video sequences from an iPhone 4, with ground truth obtained from a global shutter camera, rigidly mounted to the iPhone 4. Compared to classical BA, the rolling shutter model requires just six extra parameters. It also degrades the sparsity of the system Jacobian slightly, but as we demonstrate, the increase in computation time is moderate. Decisive advantages are that RSBA succeeds in cases where competing methods diverge, and consistently produces more accurate results.

Place, publisher, year, edition, pages
IEEE Computer Society; 1999, 2012
Series
Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76903 (URN)10.1109/CVPR.2012.6247831 (DOI)000309166201074 ()978-1-4673-1227-1 (ISBN)
Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
Projects
VPS
Available from: 2012-04-24 Created: 2012-04-24 Last updated: 2017-06-01Bibliographically approved
Hedborg, J., Ringaby, E., Forssén, P.-E. & Felsberg, M. (2011). Structure and Motion Estimation from Rolling Shutter Video. In: IEEE International Conference onComputer Vision Workshops (ICCV Workshops), 2011. Paper presented at 2nd IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011,6-13 November,Barcelona, Spain (pp. 17-23). IEEE Xplore
Open this publication in new window or tab >>Structure and Motion Estimation from Rolling Shutter Video
2011 (English)In: IEEE International Conference onComputer Vision Workshops (ICCV Workshops), 2011, IEEE Xplore , 2011, p. 17-23Conference paper, Published paper (Refereed)
Abstract [en]

The majority of consumer quality cameras sold today have CMOS sensors with rolling shutters. In a rolling shutter camera, images are read out row by row, and thus each row is exposed during a different time interval. A rolling-shutter exposure causes geometric image distortions when either the camera or the scene is moving, and this causes state-of-the-art structure and motion algorithms to fail. We demonstrate a novel method for solving the structure and motion problem for rolling-shutter video. The method relies on exploiting the continuity of the camera motion, both between frames, and across a frame. We demonstrate the effectiveness of our method by controlled experiments on real video sequences. We show, both visually and quantitatively, that our method outperforms standard structure and motion, and is more accurate and efficient than a two-step approach, doing image rectification and structure and motion.

Place, publisher, year, edition, pages
IEEE Xplore, 2011
Keywords
Structure and Motion, Rolling Shutter, Bundel Adjustment
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-75258 (URN)10.1109/ICCVW.2011.6130217 (DOI)978-1-4673-0062-9 (ISBN)
Conference
2nd IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011,6-13 November,Barcelona, Spain
Available from: 2012-03-01 Created: 2012-02-23 Last updated: 2018-01-12Bibliographically approved
Hedborg, J. & Felsberg, M. (2010). Fast and Robust Relative Pose Estimation for Forward and Sideways Motions. In: SSBA.
Open this publication in new window or tab >>Fast and Robust Relative Pose Estimation for Forward and Sideways Motions
2010 (English)In: SSBA, 2010Conference paper, Published paper (Other academic)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-58323 (URN)
Projects
DIPLECS
Available from: 2010-08-11 Created: 2010-08-11 Last updated: 2016-05-04
Hedborg, J. & Forssén, P.-E. (2009). Fast and Accurate Ego-Motion Estimation. Paper presented at Swedish Symposium on Image Analysis - SSBA'2009, March 18-20, Halmstad, Sweden.
Open this publication in new window or tab >>Fast and Accurate Ego-Motion Estimation
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes a system that efficiently uses the KLT tracker together with a calibrated 5-point solver for structure-from-motion (SfM). Our system uses a GPU to perform tracking, and the CPU for SfM.

In this setup, it is advantageous to run the tracker both forwards and backwards in time, to detect incorrectly tracked points. We introduce a modification to the point selection inside the RANSAC step of the 5-point solver, and demonstrate how this speeds up the algorithm. Our evaluations are done using both real camera sequences, and data from a state-of-the art rendering engine with associated ground-truth.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-50622 (URN)
Conference
Swedish Symposium on Image Analysis - SSBA'2009, March 18-20, Halmstad, Sweden
Available from: 2009-10-13 Created: 2009-10-13 Last updated: 2015-12-10Bibliographically approved
Hedborg, J., Forssén, P.-E. & Felsberg, M. (2009). Fast and Accurate Structure and Motion Estimation. In: George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Yoshinori Kuno, Junxian Wang, Jun-Xuan Wang, Junxian Wang, Renato Pajarola and Peter Lindstrom et al. (Ed.), International Symposium on Visual Computing. Paper presented at 5th International Symposium, ISVC 2009, November 30 - December 2, Las Vegas, NV, USA (pp. 211-222). Berlin Heidelberg: Springer-Verlag
Open this publication in new window or tab >>Fast and Accurate Structure and Motion Estimation
2009 (English)In: International Symposium on Visual Computing / [ed] George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Yoshinori Kuno, Junxian Wang, Jun-Xuan Wang, Junxian Wang, Renato Pajarola and Peter Lindstrom et al., Berlin Heidelberg: Springer-Verlag , 2009, p. 211-222Conference paper, Oral presentation only (Refereed)
Abstract [en]

This paper describes a system for structure-and-motion estimation for real-time navigation and obstacle avoidance. We demonstrate it technique to increase the efficiency of the 5-point solution to the relative pose problem. This is achieved by a novel sampling scheme, where We add a distance constraint on the sampled points inside the RANSAC loop. before calculating the 5-point solution. Our setup uses the KLT tracker to establish point correspondences across tone in live video We also demonstrate how an early outlier rejection in the tracker improves performance in scenes with plenty of occlusions. This outlier rejection scheme is well Slated to implementation on graphics hardware. We evaluate the proposed algorithms using real camera sequences with fine-tuned bundle adjusted data as ground truth. To strenghten oar results we also evaluate using sequences generated by a state-of-the-art rendering software. On average we are able to reduce the number of RANSAC iterations by half and thereby double the speed.

Place, publisher, year, edition, pages
Berlin Heidelberg: Springer-Verlag, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 5875
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-50624 (URN)10.1007/978-3-642-10331-5_20 (DOI)000278937300020 ()
Conference
5th International Symposium, ISVC 2009, November 30 - December 2, Las Vegas, NV, USA
Projects
DIPLECS
Available from: 2009-10-13 Created: 2009-10-13 Last updated: 2016-05-04Bibliographically approved
Hedborg, J. (2009). Pose Estimation and Structure Analysisof Image Sequences. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Pose Estimation and Structure Analysisof Image Sequences
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous navigation for ground vehicles has many challenges. Autonomous systems must be able to self-localise, avoid obstacles and determine navigable surfaces. This thesis studies several aspects of autonomous navigation with a particular emphasis on vision, motivated by it being a primary component for navigation in many high-level biological organisms.  The key problem of self-localisation or pose estimation can be solved through analysis of the changes in appearance of rigid objects observed from different view points. We therefore describe a system for structure and motion estimation for real-time navigation and obstacle avoidance. With the explicit assumption of a calibrated camera, we have studied several schemes for increasing accuracy and speed of the estimation.The basis of most structure and motion pose estimation algorithms is a good point tracker. However point tracking is computationally expensive and can occupy a large portion of the CPU resources. In thisthesis we show how a point tracker can be implemented efficiently on the graphics processor, which results in faster tracking of points and the CPU being available to carry out additional processing tasks.In addition we propose a novel view interpolation approach, that can be used effectively for pose estimation given previously seen views. In this way, a vehicle will be able to estimate its location by interpolating previously seen data.Navigation and obstacle avoidance may be carried out efficiently using structure and motion, but only whitin a limited range from the camera. In order to increase this effective range, additional information needs to be incorporated, more specifically the location of objects in the image. For this, we propose a real-time object recognition method, which uses P-channel matching, which may be used for improving navigation accuracy at distances where structure estimation is unreliable.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. p. 28
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1418
Keywords
KLT, GPU, structure from motion, stereo, pose estimation
National Category
Engineering and Technology Computer Vision and Robotics (Autonomous Systems) Signal Processing
Identifiers
urn:nbn:se:liu:diva-58706 (URN)LiU-TEK-LIC-2009:26 (Local ID)978-91-7393-516-6 (ISBN)LiU-TEK-LIC-2009:26 (Archive number)LiU-TEK-LIC-2009:26 (OAI)
Opponent
Supervisors
Projects
Diplecs
Available from: 2011-01-25 Created: 2010-08-23 Last updated: 2020-03-10Bibliographically approved
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