Chromatic methodology for laser detection and ranging (LADAR) image description

Abstract Chromatic methodology for information extraction has evolved from a number of origins, some based upon human perceptions and the others on scientific and analytical approaches. From the applications of this methodology, a new robust LADAR image descriptor is proposed. This descriptor is developed to has a high discrimination capability, robust to the effects that disturb LADAR images, and requires less storage space and computational time for recognition. The performance of the proposed LADAR descriptor is evaluated using simulated LADAR images. Experimental tests are also being undertaken on the new descriptor to validate its ability with processing real LADAR images. The results show high discrimination capability for the new descriptor over the traditional techniques such as Moments descriptor, which is used to benchmark the results. The results also show the robustness of the new descriptor in the presence of noise, view change, low resolution, translation, scaling, and rotation effects.

[1]  Paul J. Besl,et al.  Surfaces in Range Image Understanding , 1988, Springer Series in Perception Engineering.

[2]  He Yuanjun,et al.  3D model retrieval based on orthogonal projections , 2005 .

[3]  Gordon R. Jones,et al.  Chromatic Monitoring of Complex Conditions , 2008 .

[4]  Wang Mei,et al.  SAR Image Target Recognition Based on Hu Invariant Moments and SVM , 2009, 2009 Fifth International Conference on Information Assurance and Security.

[5]  Katsushi Ikeuchi,et al.  A spherical representation for the recognition of curved objects , 1993, 1993 (4th) International Conference on Computer Vision.

[6]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[7]  Lampros K. Stergioulas,et al.  Gabor representation of optical signals using a truncated von Neumann lattice and its practical implementation , 2000 .

[8]  Dongming Zhao,et al.  Three-dimensional range data interpolation using B-spline surface fitting , 2001, J. Electronic Imaging.

[9]  Maarten Uijt de Haag,et al.  Use of 3D laser radar for navigation of unmanned aerial and ground vehicles in urban and indoor environments , 2007, SPIE Defense + Commercial Sensing.

[10]  Hans-Peter Kriegel,et al.  3D Shape Histograms for Similarity Search and Classification in Spatial Databases , 1999, SSD.

[11]  Shaoqiu Xu,et al.  Event and movement monitoring using chromatic methodologies , 2006 .

[12]  Stephane Ruel,et al.  Field testing of a 3D automatic target recognition and pose estimation algorithm , 2004, SPIE Defense + Commercial Sensing.

[13]  James R. Weber,et al.  The effects of different shape-based metrics on the identification of military targets from 3D ladar data , 2006, Electronic Imaging.

[14]  P. Iles,et al.  Rapid automatic target recognition using generic 3D sensor and shape-from-motion data , 2007, SPIE Defense + Commercial Sensing.

[15]  J. Deogun,et al.  Gene Function Classification Using Fuzzy K-Nearest Neighbor Approach , 2007 .

[16]  Maarten Uijt de Haag,et al.  Aerial vehicle navigation over unknown terrain environments using inertial measurements and dual airborne laser scanners or flash ladar , 2007, SPIE Defense + Commercial Sensing.

[17]  Raj Madhavan,et al.  Obstacle detection using a Time-of-Flight range camera for Automated Guided Vehicle safety and navigation , 2005, Integr. Comput. Aided Eng..

[18]  Bo Cheng,et al.  Real-time driver eye detection method using Support Vector Machine with Hu invariant moments , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[19]  Toshinori Watanabe,et al.  Image representation and classification based on data compression , 2010, SAC '10.

[20]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[21]  F. Seco,et al.  A laser range-finder scanner system for precise manoeuver and obstacle avoidance in maritime and inland navigation , 2004, Proceedings. Elmar-2004. 46th International Symposium on Electronics in Marine.

[22]  Mark R. Stevens,et al.  Feature-based target classification in laser radar , 2002, SPIE Defense + Commercial Sensing.

[23]  Alan M. Lytle,et al.  Standards requirements for LADARs? , 2005, SPIE Defense + Commercial Sensing.

[24]  Lampros K. Stergioulas,et al.  The Gabor transform basis of chromatic monitoring , 2000 .

[25]  Mohammed Bennamoun,et al.  Automatic Correspondence for 3d Modeling: an Extensive Review , 2005, Int. J. Shape Model..

[26]  Gordon R. Jones,et al.  Towards holistic chromatic intelligent monitoring of complex systems , 2006 .

[27]  D. Spencer,et al.  The photic field , 1981 .

[28]  Abhijit Mahalanobis.  Automatic target recognition using correlation filters , 1995, Defense, Security, and Sensing.

[29]  Bao-long Guo,et al.  Rotation, scaling and translation invariant image watermarking using feature points , 2008 .

[30]  Puteh Saad,et al.  Object Detection using Geometric Invariant Moment , 2006 .

[31]  W. B. Hall,et al.  Chromatic processing of optoacoustic signals for identifying incipient faults on electric power equipment , 1996 .

[32]  Cang Ye.  Toward safe navigation in urban environments , 2007, SPIE Defense + Commercial Sensing.

[33]  Sylvain Petitjean,et al.  A survey of methods for recovering quadrics in triangle meshes , 2002, CSUR.

[34]  K. R. Radhika,et al.  Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine , 2010 .

[35]  John C. Davis,et al.  Contouring: A Guide to the Analysis and Display of Spatial Data , 1992 .

[36]  Hsueh-Ming Hang,et al.  A feature-based robust digital image watermarking scheme , 2003, IEEE Trans. Signal Process..

[37]  Ali A. Al-Temeemy,et al.  Three-dimensional ladar imaging system using AR-4000LV laser range-finder , 2011, Optical Systems Design.

[38]  Dirk Langer,et al.  Imaging Ladar for 3-D Surveying and CAD Modeling of Real-World Environments , 2000, Int. J. Robotics Res..

[39]  Ahmed H. Tewfik,et al.  Geometric Invariance in image watermarking , 2004, IEEE Transactions on Image Processing.

[40]  Q.A. Holmes,et al.  Multiresolution surface feature analysis for automatic target identification based on laser radar images , 1997, Proceedings of International Conference on Image Processing.

[41]  Charlie Ward Trussell.  3D imaging for Army applications , 2001, SPIE Defense + Commercial Sensing.

[42]  Stephane Ruel,et al.  Development of a practical 3D automatic target recognition and pose estimation algorithm , 2004, SPIE Defense + Commercial Sensing.

[43]  Nor Ashidi Mat Isa,et al.  A novel aggregate classification technique using moment invariants and cascaded multilayered perceptron network , 2009 .

[44]  Burcu Akinci,et al.  A Comparative Analysis of Depth-Discontinuity and Mixed-Pixel Detection Algorithms , 2007, Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007).