Image Pattern Matching and optimization using simulated annealing: a Matlab Implementation
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1 Image Pattern Matching and optimization using simulated annealing: a Matlab Implementation Presented by By, Vandita Srivastava Authors: Vandita Srivastava Alfred Stein, D.G. Rossiter P.K. Garg R.D. Garg Scientist SE, Geoinformatics Department, IIRS Dehradun Department of Space, Govt. of India vandita@iirs.gov.in MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 1 1
2 Contents of Presentation Background Motivation Research Questions Research Objectives Methodology & Programme Logic Resources Used Results & Discussion Conclusion Future Recommendations MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 2 2
3 Motivation for pattern matching Advent of ever increasing spatio-temporal resolutions. Lack of quick, automated and reliable information mining methods. Image pattern matching a key research area in CBIR Simulated annealing (SA) a widely applied technique for optimization, now applied to spatial domain also. Geostatistics represents spatial structure in the best possible manner MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 3 3
4 Research Questions Can we obtain similar matching patterns in objects of images through process of optimization of images through SA? Can we use variogram as an optimization function in SA process? How to execute this in Matlab? How to optimize the code for performance (processing time, memory) What all issues to consider to make the code generic and flexible? MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 4 4
5 Research Objectives Main Objectives: Given a satellite image, obtain another image with similar matching pattern Explore potential of variogram as an optimization function Implement the whole methodology of SA in Matlab Sub Objectives: A flexible & generic SA implementation w.r.t inputs & outputs Code optimization for performance w.r.t processing time and memory Evaluation of SA implementation on several images from several sites/times/trees/ areas / configurations / no. of objects. MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 5 5
6 4 stage Implementation Image pre-preprocessing Binarization of tree canopy boundaries. Image rotation and cropping image area. Image Simulation Extraction of information on number and inter-spacing between objects Variogram Calculation Omni & Directional variograms Angle & Distance tolerances, Cutoff range definition Simulated Annealing MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 6 6
7 Simulated Annealing Motivated by the physical annealing process Material is heated and slowly cooled into a uniform structure Simulated annealing mimics this process The first SA algorithm was developed in 1953 (Metropolis) Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N.; Teller, Augusta H.; Teller, Edward (1953). "Equation of State Calculations by Fast Computing Machines". The Journal of Chemical Physics 21 (6): MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 7 7
8 Spatial Simulated Annealing SA carried out for Spatially Explicit Processes Kirkpatrick (1982) applied SA to optimisation problems SSA requirements Initial Temperature Initial solution Cooling schedule Acceptance criterion Stopping criterion Spatial Optimization function MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 8 8
9 SSA Parameters in our case S. No. Parameter Name Value 1 h (decay factor) Cooling Rate Diagonal distance normalized by 3 d max square root of number of circles 4 Precision (for stopping criteria) 6 places after decimal ( ) 5 Max iterations with allowed moves (for stopping criteria) Number of worse moves to decide in equation probability threshold (in turn to decide initial temperature t0 ) Initial temperature t MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 9 9
10 Image simulation Logic Real Image Simulated Image Optimization Function Probability Calculation Probability Checking If Not satisfied If Satisfied Accept Image Simulated Image adjustment MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 10 10
11 Resources used Study Area: Dehradun, Saharanpur (City & Periphery) Satellite Images: Quick Bird Saharanpur Temporal data used (March, May, December) Software Used: Matlab (IP & Mapping Toolboxes) Additional tools: Dehradun Questionnaire Survey (Horticulture department) Experts opinion (Simulated Annealing Parameters) MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 11 11
12 Field work Survey details 10 sites surveyed 50 trees measured 2 areas (DDN, Saharanpur) 3 arrangements (linear, sparse, regular) Sampling scheme: stratified random Field measurements Extent of tree Tree Species Approximate age MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 12 12
13 Programme Issues Dealt With High radiometric resolution of input image. Memory space optimisation during run. Code vectorisation used for faster calculations Flexible w.r.t input image & parameters Generic w.r.t. image resolution & size, number of objects, SA parameters MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 13 13
14 Programme Issues Dealt With Outputs as mat & xls files, graphs, in one single folder generated automatically, customized names based on parameters in a run Time performance profiles used to reduce time consuming processes Customized folder and file names MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 14 14
15 Results Sparse arrangement MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 15 15
16 Results Linear arrangement MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 16 16
17 Results Regular arrangement MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 17 17
18 Results Variograms in iterations MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 18 18
19 Results Behaviour of Fitness function MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 19 19
20 Evaluation of Results Evaluation on: several images from two sites (DDN & Saharanpur), different times (March, May & December), Different species (Litchi & Mango Orchards) 3 configurations (linear, regular & sparse) varying number of objects. Evaluation Methods: Visual analysis of patterns Nearest Neighbour Index/Polygon pattern analysis Evaluation stages: Binarisation Image pattern matching MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 20 20
21 Research outcomes An algorithm for Spatial Simulated Annealing (developed, implemented and evaluated) Demonstrated potential use of variogram as an optimization function A flexible SA implementation w.r.t input images & parameters A generic SA implementation w.r.t image products, type of objects Outcome of a SA run reported as mat & xls files, figures & graphs Code optimization for performance w.r.t processing time and memory space MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 21 SA 21
22 Potential Applications Pattern Recognition and matching Find images having clusters of similarly configured objects Content based image retrieval(cbir) Get several choices for arrangements with similar spatial configuration (low object density) (think of example and put)-good for planners as a tool. MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 22 22
23 Potential Application Areas Image mining & CBIR Agro-forestry, Horticulture, Forestry Carbon budgeting/ sequestration studies Global warming & environmental pollution studies Planning & Management studies MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 23 23
24 Conclusion Usefulness of variogram as an optimization demonstrated Spatial Simulated Annealing implemented for image pattern matching Success of SA demonstrated on binarised images of individual trees for obtaining images with similar patterns. The methodology is put forward for further evaluation by application scientists and other researchers for its benefits. MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 24 24
25 Future recommendations/further Work Extend for other type of objects of varying shapes. Extend for gray images Overlapping objects. MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 25 25
26 Thank You Questions? Vandita Srivastava Scientist SE, Geoinformatics Department, IIRS Dehradun Department of Space, Govt. of India Research Interest: Advanced Image Processing for information mining of geospatial data MatlabExpo, 2014 By Vandita Srivastava, IIRS, Dehradun 26 26
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