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DANGER DETECTOR FINAL VIP PRESENTATION Krithika Chandrasekar Devang Parekh Shruthi S Reddy

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Outline Objectives Flowchart of algorithm Review of major concepts used to implement algorithm Results Future Work

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Objectives – Part I The algorithm takes as its input CT images of screened baggage. Each slice represents a 2D view of the outline of each object contrasted against a background. Our immediate objective is: To find a suitable method to separate 2D objects and background. Several thresholding techniques can be used (Otsu method helps to separate data into two or more classes of pixels.) To perform connected component analysis on the slices to obtain the 2D connected object. Perform clustering (or another suitable segmentation algorithm) if there is a distinct change in density across the volume of the object and confirm the exact region around which an explosive might be concealed.

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Objectives-Part II Develop an algorithm to perform assembly of CT slices. This involves finding slices with similar connected regions and grouping them together. Write code to perform clustering on the 3D image of the object. Last step involves comparing the results of clustering with known properties of explosives and flagging an alarm when a match is found (explained in further details in Results section)

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Flowchart of algorithm

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Otsu Thresholding Use of graythresh command in Matlab to find a threshold using Otsu’s Method

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CONNECTED COMPONENT ANALYSIS 7 Once region boundaries have been detected, it is often useful to extract regions which are not separated by a boundary. Any set of pixels which is not separated by a boundary is call connected. Each maximal region of connected pixels is called a connected component. The set of connected components partition an image into segments. Let s be a neighborhood system. – 4-point neighborhood system – 8-point neighborhood system Let c(s) be the set of neighbors that are connected to the point s. For all s and r, the set c(s) must have the properties that – c(s) ε s – r ε c(s), s ε c(r) A region R S is said to be connected under c(s) if for all s, r 2 R there exists a sequence of M pixels, s1, · · ·, sM such that s1 ε c(s), s2 ε c(s1), · · ·, sM ε c(sM−1), r ε c(sM) i.e. there is a connected path from s to r. *www.ee.mit.edu/ece590/lecture_notes/ece590H

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CCA-procedure Find connected regions for each slice using ‘bwconncomp’. Iteratively scan every slice to compare if similar connected regions are found. (Tolerance used=0.8) If slice ‘i’ and ‘i+1’ have the same/similar connected regions classify them into a single 3-D array Follow the same scanning procedure across all the slices in the data set. Note that all the slices in the Data set hold relevant data which can be analyzed meaningfully. Slices which have CT artifacts alone have been manually removed so that results of analysis are accurate. Next step performed is clustering.

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Feature Vector analysis Mean and Variance are the numerical features that are used to represent our object str1 = 'mean'; str2 = 'var'; eval([featvector '= struct( str1,mean(mean((A))), str2, var(var((A))));']); eval([featvectorOriginal '= struct( str1,mean(mean((C))), str2, var(var((C))));']); x = mean(mean((A))); y = var(var((A))); xOriginal = mean(mean((C))); yOriginal = var(var((C) ) ); meanGray(i) = x; varGray(i) = y; meanOrig(i) = xOriginal; varOrig(i) = yOriginal ;

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clustering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Hierarchical Clustering http://people.revoledu.com/kardi/tutorial/Clu stering/index.html

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Classification if (d==1) if ((abs (xOriginal - Feat1 (1))<1)||(abs (yOriginal - Feat1 (2))<1)) fprintf ('Location is found! Slice = % d Object # = % d \n',k,i) disp (row') disp (col') end if ( c == 1) Feat1 = [xOriginal yOriginal]; d=d+1; c=c+1; end

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Results All results shown below are for analysis performed between slice 10 and 14 of the given data set. A prominent Connected region for slice 10 Columns -96-137 Rows-210-243 (Let this be slice i)

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Results (contd) Prominent connected region for slice 11 Columns -91-139 Rows - 206-238 Tolerance>0.8 Gets assembled as i+1 into array A

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Results (contd) Prominent connected region for slice 11 Columns - 98-143 Rows - 205-237 Tolerance>0.8 Gets assembled as i+2 into array A

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Results of clustering for example above Number of centroids-9 (center of the hierarchical cluster for this connected region is at z=11) Histogram of pixel intensities-smooth distribution Conclusion-object is not a potential suspect

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Future work Integrate with simulator group and check validity of algorithm by using the visualization tool. Detect False alarms using MAP estimation and accurately find co-ordinates of the explosive, given the 2D CT slices of scanned baggage

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EECS 274 Computer Vision Segmentation by Clustering II.

EECS 274 Computer Vision Segmentation by Clustering II.

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