Epipolar Geometry of Opti-Acoustic Stereo Imaging

Optical and sonar cameras are suitable imaging systems for inspecting underwater structures, both in regular maintenance and security operations. Despite their high resolution, optical systems have limited visibility range when deployed in turbid waters. In contrast, the new generation of high-frequency (MHz) forward-scan sonar cameras can provide images with enhanced target details in highly turbid waters though their range is reduced by one or two orders of magnitude as compared to traditional low or midfrequency (10s-100s KHz) sonar systems. It is conceivable that an effective inspection strategy is the deployment of both optical and sonar cameras on a submersible platform to enable target imaging in a range of turbidity conditions. Under this scenario and where visibility allows, registration of the images from both cameras (arranged in binocular stereo configuration) provides valuable scene information that cannot be readily recovered from each sensor alone. We explore and derive the constraint equations for the epipolar geometry and stereo triangulation in utilizing these two sensing modalities with different projection models. Theoretical results supported by computer simulations show that an opti-acoustic stereo imaging system outperforms a traditional binocular vision with optical cameras, particularly for increasing target distance and/or turbidity.

[1]  Behzad Kamgar-Parsi,et al.  Underwater imaging with a moving acoustic lens , 1998, IEEE Trans. Image Process..

[2]  Tomás Svoboda,et al.  Epipolar Geometry of Panoramic Cameras , 1998, ECCV.

[3]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  E. O. Belcher,et al.  Object identification with acoustic lenses , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[5]  U. Castellani,et al.  A complete system for on-line 3D modelling from acoustic images , 2005, Signal Process. Image Commun..

[6]  Paul Rademacher,et al.  Multiple-center-of-projection images , 1998, SIGGRAPH.

[7]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[8]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[9]  Andrew Zisserman,et al.  Multiple view geometry in computer visiond , 2001 .

[10]  Lee Freitag,et al.  Semi-Autonomous Mapping System , 2003 .

[11]  Yuri Rzhanov,et al.  Sensor-assisted video mosaicing for seafloor mapping , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  P. Firoozfam,et al.  An ROV Stereovision System for Ship-Hull Inspection , 2006, IEEE Journal of Oceanic Engineering.

[13]  Hervé Delingette,et al.  Biomechanical Model Construction from Different Modalities: Application to Cardiac Images , 2002, MICCAI.

[14]  L. Freitag,et al.  Semiautonomous Mapping System , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[15]  Qian Chen,et al.  A volumetric stereo matching method: application to image-based modeling , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[16]  Donald B. Plewes,et al.  A hybrid breast biopsy system combining ultrasound and MRI , 2003, IEEE Transactions on Medical Imaging.

[17]  S. Negahdaripour,et al.  Underwater mosaic creation using video sequences from different altitudes , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[18]  M. Zibaeifard,et al.  An adaptive simulated annealing scheme for multi-modality medical image registration by maximization of mutual information , 2006, 2006 8th international Conference on Signal Processing.

[19]  Shahriar Negahdaripour,et al.  Direct and Indirect 3-D Reconstruction from Opti-Acoustic Stereo Imaging , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[20]  Yoav Y. Schechner,et al.  Clear underwater vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[21]  Long Quan,et al.  Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Yoshiaki Shirai,et al.  Three-Dimensional Computer Vision , 1987, Symbolic Computation.

[23]  Andrea Fusiello,et al.  Augmented scene modeling and visualization by optical and acoustic sensor integration , 2004, IEEE Transactions on Visualization and Computer Graphics.

[24]  J. Santos-Victor,et al.  Underwater mosaicing and trajectory reconstruction using global alignment , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[25]  Leonard McMillan,et al.  General Linear Cameras , 2004, ECCV.

[26]  D. Gueriot Bathymetric and side-scan data fusion for sea-bottom 3D mosaicing , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[27]  Dennis G. Gallagher,et al.  Acoustic Lens Camera and Underwater Display Combine to Provide Efficient and Effective Hull and Berth Inspections , 2003 .

[28]  Steven M. Seitz,et al.  The Space of All Stereo Images , 2004, International Journal of Computer Vision.

[29]  P. Wolf,et al.  Elements of Photogrammetry(with Applications in GIS) , 2000 .

[30]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[31]  Jon Rigelsford Panoramic Vision: Sensors, Theory and Applications , 2002 .

[32]  C. A. HART,et al.  Manual of Photogrammetry , 1947, Nature.

[33]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

[34]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[35]  R. Vesetas,et al.  AMI: a 3-D imaging sonar for mine identification in turbid waters , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[36]  C. Barat,et al.  Exploiting natural contours for automatic sonar-to-video calibration , 2005, Europe Oceans 2005.

[37]  Yongmin Kim,et al.  Interactive 3D registration of ultrasound and magnetic resonance images based on a magnetic position sensor , 1999, IEEE Transactions on Information Technology in Biomedicine.

[38]  S. Negahdaripour Calibration of DIDSON forward-scan acoustic video camera , 2005, Proceedings of OCEANS 2005 MTS/IEEE.