Extraction of Infrared Target based on Gaussian Mixture Model

We propose a method for target detection in Infrared images. In order to effectively detect a target region from an image with noises and clutters, spatial information of the target is first considered by analyzing pixel distributions of projections in horizontal and vertical directions. These distributions are represented as Gaussian distributions, and Gaussian Mixture Model is created from these distributions in order to find thresholding points of the target region. Through analyzing the calculated Gaussian Mixture Model, the target region is detected by eliminating various backgrounds such as noises and clutters. This is performed by using a novel thresholding method which can effectively detect the target region. As experimental results, the proposed method has achieved better performance than existing methods.

[1]  Lei Yang,et al.  Variance WIE based infrared images processing , 2006 .

[2]  Tianxu Zhang,et al.  Infrared image segmentation with 2D OTSU method based on particle swarm optimization , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[3]  Yi Lu,et al.  A Modified Canny Algorithm for Detecting Sky-Sea Line in Infrared Images , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[4]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[5]  Min Li,et al.  Image measures for segmentation algorithm evaluation of automatic target recognition system , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[6]  Magnús Snorrason,et al.  An Image Metric-Based ATR Performance Prediction Testbed , 2006, AIPR.

[7]  Mohan M. Trivedi,et al.  Low-Level Segmentation of Aerial Images with Fuzzy Clustering , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Hai Jin,et al.  A New Image Thresholding Method Based on Graph Cuts , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[9]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[10]  Hugo Guterman,et al.  Region-of-interest-based algorithm for automatic target detection in infrared images , 2005 .

[11]  Klamer Schutte,et al.  Automatic classification of ships from infrared (FLIR) images , 1999, Defense, Security, and Sensing.

[12]  Kamlesh Dutta,et al.  A Genetic Algorithm Based Task Scheduling for Cloud Computing with Fuzzy logic , 2013 .

[13]  Taek Lyul Song,et al.  Extracting targets from regions-of-interest in infrared images using a 2-D histogram , 2011 .

[14]  Maged Hamada Ibrahim,et al.  Energy Detection Based Sensing for Secure Cognitive Spectrum Sharing in the Presence of Primary User Emulation Attack , 2013 .

[15]  Jun-Wei Lu,et al.  An Algorithm for Locating Sky-Sea Line , 2006, 2006 IEEE International Conference on Automation Science and Engineering.

[16]  Bir Bhanu,et al.  Automatic Target Recognition: State of the Art Survey , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Tianxu Zhang,et al.  Detection algorithm for IR ship target in complex background of sea and sky , 2009, Applied Optics and Photonics China.

[18]  Udo Zölzer,et al.  GrayCut - Object Segmentation in IR-Images , 2006, ISVC.

[19]  Mike Alder,et al.  Initializing the EM Algorithm for use in Gaussian Mixture Modelling , 1993 .

[20]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[21]  Zhiguo Cao,et al.  Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter , 2007 .

[22]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[23]  Vincent J. Velten,et al.  Image characterization for automatic target recognition algorithm evaluations , 1991 .

[24]  Tianxu Zhang,et al.  Clutter-adaptive infrared small target detection in infrared maritime scenarios , 2011 .

[25]  Jie Yang,et al.  Accurate extraction of infrared target based on graph cut , 2008 .

[26]  Tianxu Zhang,et al.  Ship target detection and tracking in cluttered infrared imagery , 2011 .

[27]  Gianluca Marsiglia,et al.  Techniques for detection of multiple, extended, and low contrast targets in infrared maritime scenarios , 2006 .

[28]  Xiangzhi Bai,et al.  Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region , 2009, Signal Process..

[29]  B. Bhanu,et al.  Image understanding research for automatic target recognition , 1993, IEEE Aerospace and Electronic Systems Magazine.

[30]  Jianqi Zhang,et al.  New metrics for clutter affecting human target acquisition , 2006 .

[31]  Luis Salgado,et al.  Efficient image stabilization and automatic target detection in aerial FLIR sequences , 2006, SPIE Defense + Commercial Sensing.

[32]  John K. Goutsias,et al.  Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators , 2004, J. Electronic Imaging.

[33]  David L. Wilson Image-based contrast-to-clutter modeling of detection , 2001 .

[34]  Jeffrey D Bradley,et al.  A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms. , 2006, Medical physics.

[35]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.