image processing in biology11
DESCRIPTION
Description in reportTRANSCRIPT
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Background
Optimal Edge-Based Shape DetectionUnivesity of Konstanz
By Waleed Abrar
February 5, 2015
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Background
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Process of Image Acquisition
[1]
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Sampling , Quantization and Level of Processing
SamplingDigitization of the spatial coordinates (x,y)
QuantizationDigitization in amplitude (also called gray-level quantization)
Region Vs BoundaryBoundary sometime called as contour is a set of pixels in theregion that have one or more neighbour that are not in R
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Low level processing
Figure: Lena
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Type of Edges
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Edge Calculations
Gradient MagnitudeGradient Orientation
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BackgroundIntroduction and Motivation
Optimal Edge Base Shape Detection
Edge Detection Problem
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Introduction and MotivationOptimal Edge Base Shape DetectionExperimental Results and Evaluations
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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Introduction and MotivationOptimal Edge Base Shape DetectionExperimental Results and Evaluations
Main Concept in the Paper
Get better Detection of Edges + Better Localization ofEdgeCompare DOG, DOB and DODEMinimizing the sum of the noise power and the meansquared error between input and output
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Introduction and MotivationOptimal Edge Base Shape DetectionExperimental Results and Evaluations
Calculations
Step edge is Corrupted with uniform white noise
We need to find h that minimises E2 +M2
E = MSD between Input and output FM = MSD of output Noise Responses
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Introduction and MotivationOptimal Edge Base Shape DetectionExperimental Results and Evaluations
Edge Detection Cont..
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Introduction and MotivationOptimal Edge Base Shape DetectionExperimental Results and Evaluations
Optimal Smoothing OperatorWiener filter:
One Dimensional optimal filter
Two dimension can be extended as
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Optimal Edge Base Shape DetectionExperimental Results and Evaluations
Applications
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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Optimal Edge Base Shape DetectionExperimental Results and Evaluations
Applications
Edge detection and vehicle detection
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Optimal Edge Base Shape DetectionExperimental Results and Evaluations
Applications
Edge Detection and Vehicle Detection
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Optimal Edge Base Shape DetectionExperimental Results and Evaluations
Applications
Concept of Profiling Shapes
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Optimal Edge Base Shape DetectionExperimental Results and Evaluations
Applications
Concept of Profiling Shapes..
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Experimental Results and EvaluationsApplicationsConclusion
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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Experimental Results and EvaluationsApplicationsConclusion
Application Areas of optimal edge based shape detection
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ApplicationsConclusion
Demo and References
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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ApplicationsConclusion
Demo and References
Summarizing paper
DOG vs DODEDODE works really well for localization as compared to actualdetection of edge.
ProfilingProfiling Shapes extends the detection to multiple shapes withhigh confidence
EnhancementsCombination of low level edge detection with mid level edgegrouping
EfficientThe Algorithm actually give better results with less computation.
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ApplicationsConclusion
Demo and References
Recommendations
RecommendationsUSE SSIM OR E-SSIM to achieve better results as well toovercome orientation . SSIM give some kind of quanifiablemeasure to further enhance the Algorithm.- It also helps over come zooming scaled or rotated problem anddetection.-MSE is not always the right choice because of the signal Fidelity.
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ApplicationsConclusion
Demo and References
Outline
1 Background
2 Introduction and Motivation
3 Optimal Edge Base Shape Detection
4 Experimental Results and Evaluations
5 Applications
6 Conclusion
7 Demo and References
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ApplicationsConclusion
Demo and References
Literature Review
[1]DigitalImageProcessing,Rafaelhttp://lit.fe.uni-lj.si/showpdf.php?lang=slo&type=doc&doc=dip&format=0[2]http://www.slideshare.net/nchkarthik/digital-image-processing-26334694[3]OptimalEdge-BasedShapeDetection..http://www.cfar.umd.edu/hankyu/shape_html/shape_html.html#fig:operatorPerformanceComparisonofMedianandWienerFilterinImageDe-noising,IJCA,2010volume12ImageProcessingAndPatternRecognition(BITI3313)..http://de.mathworks.com/matlabcentral/fileexchange/28757-tracking-red-color-objects-using-matlab
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BackgroundIntroduction and Motivation Optimal Edge Base Shape DetectionExperimental Results and EvaluationsApplicationsConclusionDemo and References