A structural difference based image clutter metric with brain cognitive model constraints

Previous clutter metrics have less than the desired accuracy in predicting targeting performance, in this paper, a structural difference based image clutter metric is proposed based on the given definition of image clutter metric. According to the sensitivity of human visual perception to image structural information, a structural similarity measure between the target and clutter images is firstly established. Previous clutter metrics not considering brain cognitive characteristics, we define an information content weight measure by introducing the widely accepted brain cognitive information extracting model in the field of image quality assessment (IQA), and then, pool the structural similarity measure to be a clutter metric, which can be entitled BSD metric. Comparative field tests show that BSD metric makes a more significant improvement than previously proposed metrics in predicting target acquisition performance including detection probability and search time.

[1]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.

[2]  Stanley R. Rotman,et al.  Textural metrics for clutter affecting human target acquisition , 1996, Defense, Security, and Sensing.

[3]  Eddie L. Jacobs,et al.  The Targeting Task Performance (TTP) Metric A New Model for Predicting Target Acquisition Performance , 2004 .

[4]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[5]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[6]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[7]  Gil Tidhar,et al.  Improved electro-optical target detection in a natural fractal environment , 1993, Other Conferences.

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

[9]  Theo J. Doll,et al.  Target detection in urban clutter , 1989, IEEE Trans. Syst. Man Cybern..

[10]  Stanley R. Rotman.  Evaluating human target acquisition using infrared sensor technology , 1995, Other Conferences.

[11]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[12]  Mohan M. Trivedi,et al.  Developing texture-based image clutter measures for object detection , 1992 .

[13]  John A. D'Agostino,et al.  NVEOD FLIR92 thermal imaging systems performance model , 1992, Defense, Security, and Sensing.

[14]  David L. Wilson,et al.  Concepts for search and detection model improvements , 1997, Defense, Security, and Sensing.

[15]  Stanley R. Rotman,et al.  Textural metrics for clutter affecting human target acquisition , 1996 .

[16]  Ronald G. Driggers,et al.  New metric for predicting target acquisition performance , 2004 .

[17]  Barry D. Vaughan.  Soldier-in-the-Loop Target Acquisition Performance Prediction Through 2001: Integration of Perceptual and Cognitive Models , 2006 .

[18]  Stanley R. Rotman,et al.  Evaluation of human detection performance of targets embedded in natural and enhanced infrared images using image metrics , 2000 .

[19]  James A. Ratches,et al.  Night vision modeling: historical perspective , 1999, Defense, Security, and Sensing.

[20]  Zelin Shi,et al.  FD: A feature difference based image clutter metric for targeting performance , 2012 .

[21]  William R. Reynolds.  Toward quantifying infrared clutter , 1990, Defense, Security, and Sensing.

[22]  H. Basford,et al.  Optimal eye movement strategies in visual search , 2005 .

[23]  Martin J. Wainwright,et al.  Scale Mixtures of Gaussians and the Statistics of Natural Images , 1999, NIPS.

[24]  Marshall Weathersby,et al.  Detection Performance in Clutter with Variable Resolution , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Ronald G. Driggers,et al.  Search and detection modeling of military imaging systems , 2005, SPIE Defense + Commercial Sensing.

[27]  Alexander Toet,et al.  Structural similarity determines search time and detection probability , 2010 .

[28]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.

[29]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[30]  Alexander Toet,et al.  Image dataset for testing search and detection models , 2001 .

[31]  Peter Alexander Hst,et al.  A New Weighted Metric: the Relative Metric I , 2001 .

[32]  Grant R. Gerhart,et al.  Detection probability using relative clutter in infrared images , 1998 .

[33]  Delian Liu,et al.  Modeling human false alarms using clutter metrics , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

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

[35]  Lucien M. Biberman.  Electro-optical imaging : system performance and modeling , 2001 .

[36]  Walter R. Lawson,et al.  Night Vision Laboratory Static Performance Model for Thermal Viewing Systems , 1975 .

[37]  Peter Hästö,et al.  A new weighted metric: the relative metric II , 2001 .

[38]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[39]  Peter Hästö,et al.  A new weighted metric , 2005 .

[40]  Mohan M. Trivedi,et al.  Quantitative characterization of image clutter: problem, progress, and promises , 1993, Defense, Security, and Sensing.

[41]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[42]  Jianqi Zhang,et al.  Detection probability and detection time using clutter metrics , 2007 .

[43]  Ronald G. Driggers,et al.  Current infrared target acquisition approach for military sensor design and wargaming , 2006, SPIE Defense + Commercial Sensing.

[44]  Eero P. Simoncelli,et al.  Random Cascades on Wavelet Trees and Their Use in Analyzing and Modeling Natural Images , 2001 .