A Novel Feature Extraction Algorithm for IED Detection from 2-D Images using Minimum Connected Components

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Procedia Computer Science 114 (2017) 507–514

Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS October 30 – November 1, 2017, Chicago, Illinois, USA

A Novel Feature Extraction Algorithm for IED Detection from 2-D Images using Minimum Connected Components Vijayalakshmi Ramasamya,b,*, D. Nandagopalb, M. Tranb & C. Abeynayakec b

a Miami University, Oxford, Ohio 45056, United States of America University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia c Defence Science and Technology Group, West Avenue, Edinburgh, SA 5111, Australia

Abstract Buried Improvised Explosive Devices (IEDs) have become a significant threat to security forces combating terrorism. The detection of these concealed threats is a very challenging task. Ground Penetrating Radar (GPR) has shown promise in the detection of buried metallic and non-metallic IEDs or their components. The GPR produces a 2-D image of radar returns reflected off the buried objects. The challenge is how to detect IED’s in the presence of strong backscatter. In this paper, a Graph Theory based approach known as Minimum Connected Component (MCC) has been applied to detect buried objects from the 2D images produced by the GPR. The MCC feature extraction algorithm efficiently extracted the IED component from ten different data sets collected by the GPR. The uniqueness of the algorithm is that it extracts the image of the IED without any user specified threshold or any user inputs. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems. Keywords: Graph theory; minimum connected component; feature extraction; image processing.

* Corresponding author. Tel.: +1- 513-529-0339; fax: +1-513-785-3145. E-mail address: [email protected]

1877-0509 © 2017 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems. 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: ­Engineering Cyber Physical Systems. 10.1016/j.procs.2017.09.018

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1. Introduction Landmines are small objects of explosive nature that are intended to destroy and damage human (Antipersonnel (AP) landmines) or vehicle (Antitank (AT) landmines) targets. According to the International Campaign to Ban Landmines (ICBL) report, in the decade starting 1999, there were at least 73,576 mine casualties in 119 countries. The 2007 Landmine Monitor Report, Egypt section stated that at least 70 countries are affected by more than 100 million buried landmines, covering approximately 200,000 square kilometers of the world surface [1]. In other words, landmines covered an area roughly equivalent to five times the size of Denmark. More recently, a 2016 survey reported that every year some 4,000 people get hurt or killed by landmines, more than 60 countries are still contaminated by landmines, and the threat of death or dismemberment by landmine is a threat faced daily by thousands of people including young children [2]. Given this stark reality, accurate landmine detection remains of vital importance. Various Landmine Detection Techniques (LDTs) are currently available, including biological detection, electro-magnetic, nuclear, optical, acoustic and mechanical techniques [2, 3]; but as the 2016 survey results show, there is still marked room for improvement. Object recognition is considered to be a crucial problem within various research domains of image processing and computer vision. Given an image containing one or more objects of interest, the object recognition problem is defined as a labeling problem that assigns correct labels to regions, or a set of regions in the image, and this problem is strongly associated with that of image segmentation. This is because object recognition is achieved using segmentation which, in turn, requires at least a partial recognition of objects [4]. Traditional content-based image retrieval algorithms rely on the basic features such as shape, color and texture for image classification; they do not, however, consider structural information contained in the image. Graph theory uses mathematical models called graphs to efficiently represent entities, their attributes and the relationships among and between these entities. As such, the Graph is a highly sophisticated data structure that can be used for modelling, describing and data-mining complex structures such as the internet, communication networks, social networks, metabolic networks, and biological networks where the relationships between the objects in the system play a dominant role [5, 6, 7]. Investigations to understand the intricacies of the underlying structural and functional behaviours of complex and dynamic network systems using graph theory have subsequently become fundamental to various scientific disciplines including social science, systems biology and, most recently, image processing [8, 9, 16]. Graph modeling also provides a wide range of cost effective techniques to represent image objects, as well as scenes, without losing the underlying critical information of these parts; because of this, it is well-suited to landmine detection applications. To this end, the current paper addresses facilitating the detection of landmines from 2D image datasets by using an efficient graph-based feature extraction technique that uses an efficient thresholding technique. This graph representation and pattern mining technique presented in this paper portrays not only less computational complexity than comparable techniques, but also proposes good performance in practice. 2. Review of Literature Initial research into landmine detection using image processing techniques showed some success in identifying the occurrences of identical objects in images [10, 11]. Being limited to the identification of only identical instances, these methods suffered, and continue to do so, from serious setbacks such as transformation and partial occlusion. There is still no fast solution for determining exactly where within the image the actual landmine response is located; hence a wide category of recognition methods have been developed to identify the class of an image by processing the whole image [6, 7]. These methods, such as intensity based object detection, not only evaluate all parts of the image but also label them using statistics on the frequencies of special features. In traditional intensity based object detection, an object is detected from an intensity image by using an appropriate thresholding method, followed by an area thresholding for removing the clutter. The intensity image with the objects outlined results from this detection method. The intensity threshold is based on the background. Due to the varying intensities of the objects and background from one image to another, the mean of the image is selected as the value of the threshold. Unfortunately, the difficulties related to detecting small dimension landmines in low sensitivity images, and rejecting background noise in the intensity thresholding approach, are potential



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downsides of these traditional techniques, as are inefficient preprocessing steps, like morphological operations, that result in false alarms [3, 12, 13]. Due to the complexity of precise image content interpretation and accurate objectsof-interest extraction, this area of research has posed a major challenge to the research community. To help address this, the current paper will demonstrate that, through the application of graph theory to intensity based object detection, landmine detection can be improved significantly. 2.1. Graph Representation In many scientific applications, the emergence of vast quantities of complicated tree, lattice and graph data has created a need to better represent these complex structures in the computer. Graph theory fulfills this need, offering a generic representation of complicated systems (graphs) to model such data components. The efficiency of graph algorithms to retrieve useful, non-trivial and hidden patterns of interactions among the component elements of large datasets has overridden traditional statistical pattern recognition techniques. As a result, graph based algorithms are now leading the way in various fields including Pattern Recognition and Computer Vision, Machine Learning, Data Mining, and Complex Network Research [14]. Graph theory has also laid a mathematical foundation that, if used efficiently, would be capable of bridging both the gap between these fields of research and also the gap between structural and statistical pattern recognition. Graph based computational network analysis quantitatively characterizes the structural and functional architectures of objects/processing elements of a system and their interactions/ relationships. It does this by considering the processing elements as nodes (vertices), and the physical/logical connections or functional associations among these processing elements as edges (links) between them. Such graph representations are thus capable of unraveling the strong or influential ties (relationships/connections) among the processing elements. This thereby provides useful inferences for identifying not only the significant connection patterns of the graph, but also the key actors or nodes that contribute to most of the significant activities taking place in it. 2.2. Graph Theory and Image Processing The use of graph data structure as a general approach to image segmentation in image processing dates back to the 1990s [14]. Following this, the emergence of an innovative graph technique using a graph cut algorithm initiated breakthrough research into the high-level knowledge needed to address the challenge of image segmentation in computer vision [17]. Bo Peng et al., subsequently presented a systematic review of graph theoretic techniques by distinguishing them into the five categories of: (i) Minimal spanning tree (MST) based method, (ii) graph cut with cost functions, (iii) graph cut on Markov random field models, (iv) the shortest path based methods, and (v) other efficient graph theoretic methods such as random walker and dominant set based method [8, 9, 17]. 2.3. The Concept of Minimum Connected Component (MCC) The MCC is a special connected spanning sub-graph computed from the fully connected network using a new graph operator that filters only a top set of high valued edges connecting all nodes based on a unique constraint [18]. The basic definitions of terminologies used in this concept are as outlined below. Definition: Let G = (V, E) be an undirected weighted graph with a set of V vertices, a set of E edges where each edge (u, v) is an unordered pair of vertices, and w(e) is the weight of the edge connecting 2 vertices. A Minimum Connected Component, MCC ≡ GMCC ≡ GMCC(Vmcc, Emcc) of graph G is defined as GMCC = [G(V,E)], where  is a graph operator that extracts only the top high-valued connections just needed to connect all the nodes of G forming a special spanning subgraph such that Emcc  E, and Vmcc = V. The graph operator  is applied on the non-empty weighted subgraph G that picks only the highest valued edges from G, one by one, and includes them into an initially empty forest containing all the nodes of the graph G constituting a subgraph. The uniqueness of the minimum connected component approach lies in the fact that it does not demand a substantial thresholding technique to identify the influential sub network to enable characterization of

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the patterns of interactions in the network elements. In generic terms, MCC is thus a subgraph of an undirected weighted graph, which contains only a set of top high valued edges such that a special spanning sub-graph is formed connecting all the nodes thereby portraying the prominent features of any complex network under study. 3. Proposed MCC based Feature Extraction Algorithm Identifying the underlying, hidden dynamic patterns contained in data from neurophysiological signals, images, social networks and protein interaction networks poses an essential challenge to modelling the data as complex networks. While nonlinear correlation techniques are helpful to construct fully connected graphs from such datasets, the MCC algorithm is proposed to extend the power of the graph theoretic concept; specifically, the spanning subgraph to extract a significant component called MCC from the fully connected input graph. This algorithm efficiently extracts the significant component of a graph without using any user specified threshold value. The MCC algorithm, when applied to EEG data, serves as an efficient tool for distinguishing and identifying predominant features of cognitive activity in healthy human participants. In addition, the MCC algorithm has been used to study the topological properties of EEG-based functional networks in Alzheimer’s disease (AD) [19]. As reported, the topological properties of networks constructed using MCC show more significant differences between AD and healthy participants than other methods in the literature. By providing a better understanding of how to model/transform landmine image data into graphs, the MCC algorithm has yielded a novel approach that helps to glue the concepts of graph based algorithms with image processing techniques. It does this by firstly modelling the landmine image as a graph for identifying the specific feature/pattern of landmines in the image data. Then, it maps the image matrix into a numeric matrix, before finally, applying an efficient graph mining algorithm that is capable of extracting the influential or unique patterns of sub structures embedded in these images. 3.1. The Feature Extraction in Landmine Image Data using Minimum Connected Component The proposed graph based MCC algorithm detects the presence of landmines in 2-D image data sets, thus providing a new opportunity for the accurate and efficient detection of landmines. The 2-D image data set used to test the algorithm is composed of greyscale images depicting the signal response of a localized area containing a target of interest acquired by a commercial GPR. The algorithm proceeds by extracting the influential data points in the images under study, such as those of the landmines, thereby efficiently extracting the relevant features. The proposed algorithm for Feature Extraction from a grayscale land mine image by constructing GMCC is outlined as follows. Algorithm: Feature extraction from a grayscale landmine image using GMCC Input: Grayscale landmine image data set (118 x118 x 3) – LM matrices Output: Grayscale images of landmine features extracted using MCC algorithm (118 x118 x 3) 1: Load the grayscale landmine image and extract a single layer – LM matrix A (118 x 118) 2: Initialize ‘num’ to 118, where, num = Number of Nodes in the matrix A 3: Initialize the LM-MCC matrix such that LM-MCC(1:num, 1:num) = 255(white) 4: Initialize a list Visited(num) to 0 5: Repeat until sum(Visited(num)) = 118 a: Find the minimum intensity value ‘min’ and its position (i, j) in the matrix A b: Replace LM-MCC(i, j) with min value, LM-MCC(i,j) = min c: Update the nodes i and j in Visited(num) list to 1 6: Denoise the resulting LM-MCC matrix a: Remove the 10 rows of data and the columns at top left and top right from LM-MCC (it represents the ground surface reflection in all images) 7: Convert LM-MCC matrix into grayscale LM-MCCGray 8: Plot the image LM-MCCGray



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The algorithm takes as input a 3-D grayscale landmine image data set of dimension (118 x 118 x 3). Since corresponding pixels in each layer of a grayscale image are identical in terms of intensity value, only a single 2-D layer of the 3-D image data is extracted for processing. This 2-D landmine matrix called LM matrix A (118 x 118) is considered for feature extraction. The number of nodes num in the matrix A is initialized to 0 and the elements of the output matrix, LM-MCC(1:num, 1:num) is initialized to 255(white). A list called Visited of length num (118 here) is initialized to 0. The following procedure is repeated until all the elements in this list Visited is set to 1 resulting in a sum of 118. The minimum intensity value called min and its position (i, j) in the matrix A are computed. The minimum value computed is stored at the element in the position (i, j) of LM-MCC(i, j) and the node positions i and j in the Visited list are updated to 1 that denotes the flags of visited nodes. This procedure repeats and finally results in the matrix LM-MCC when the sum of elements in the Visited list is 118. Area thresholding is used to remove the ground surface reflection in all images from LM-MCC and the resulting matrix is converted back into grayscale LM-MCCGray and is plotted. The novelty of the algorithm is that, when applied to the image, it dynamically extracts the pixels corresponding to the influential high intensity valued edges rather than using any user-specified threshold thereby making it computationally efficient. 4. Results and Discussion The MCC feature extraction algorithm efficiently extracts the land mine component of the grayscale image. The uniqueness of the algorithm is that it extracts the image of the landmine without any user specified threshold or any user inputs of special parameters to extract the features or image components. From Fig. 1 it is apparent that the landmine feature is extracted from the grayscale landmine image data by applying the MCC feature extraction algorithm. The algorithm does not use any user-defined threshold to extract the landmine feature as the traditional thresholding algorithms do. a

b

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Fig 1. (a) and (b) – (1) Grayscale landmine images, (2) the LM-MCC images of extracted landmine features and (3) the superimposed images of (2) on (1) for 10 images.

To further compare and quantify the significant differences in the landmine features extracted from different images, a post hoc t – test (two − tailed) using a Bonferroni adjustment is performed. The mean differences of each of the landmine images after MCC were computed with a 95% confidence interval. The null hypothesis that the mean differences of the landmine features extracted using the MCC algorithm from different landmine image samples are significantly different is tested against the alternative hypothesis that the mean differences of the landmine features extracted using MCC algorithm from different landmine image samples are the same. The null hypothesis is rejected at 95% confidence level. The results revealed that there are no significant differences between the means of the landmine features extracted showcasing the efficacy of the MCC algorithm in extracting only the landmine features from the images as presented in Table 1. Table 1. Statistical validation using post hoc t-test with Bonferroni adjustment on 10 landmine feature image data at 95% confidence interval. Landmine Images Compared

Mean Difference

95% Confidence Interval

Landmine Images Mean Compared Difference

95% Confidence Interval

1

2

-0.0561

[-8.9406, 8.8284]

3

10

2.6598

[-6.2247, 11.5443]

1

3

-0.5840

[-9.4685, 8.3006]

4

5

0.0605

[-8.8240, 8.9451]

1

4

0.8449

[-8.0396, 9.7295]

4

6

-0.6905

[-9.5750, 8.1941]

1

5

0.9055

[-7.9790, 9.7900]

4

7

-0.7675

[-9.6520, 8.1170]

1

6

0.1545

[-8.7300, 9.0390]

4

8

-2.6196

[-11.5042, 6.2649]

1

7

0.0774

[-8.8071, 8.9619]

4

9

0.0486

[-8.8359, 8.9331]



Vijayalakshmi Ramasamy et al. / Procedia Computer Science 114 (2017) 507–514 Vijayalakshmi Ramasamy et al. / Procedia Computer Science 00 (2017) 000–000 1

8

-1.7747

[-10.6592, 7.1098]

4

10

1.2309

[-7.6536, 10.1154]

1

9

0.8936

[-7.9910, 9.7781]

5

6

-0.7510

[-9.6355, 8.1335]

1

10

2.0758

[-6.8087, 10.9604]

5

7

-0.8281

[-9.7126, 8.0564]

2

3

-0.6400

[-9.5246, 8.2445]

5

8

-2.6802

[-11.5647, 6.2043]

2

4

0.7889

[-8.0957, 9.6734]

5

9

-0.0119

[-8.8964, 8.8726]

2

5

0.8494

[-8.0351, 9.7339]

5

10

1.1704

[-7.7142, 10.0549]

2

6

0.0984

[-8.7861, 8.9829]

6

7

-0.0771

[-8.9616, 8.8075]

2

7

0.0213

[-8.8632, 8.9058]

6

8

-1.9292

[-10.8137, 6.9553]

2

8

-1.8308

[-10.7153, 7.0537]

6

9

0.7391

[-8.1454,9.6236]

2

9

0.8375

[-8.0470 9.7220]

6

10

1.9214

[-6.9632, 10.8059]

2

10

2.0198

[-6.8648, 10.9043]

7

8

-1.8521

[-10.7366, 7.0324]

3

4

1.4289

[-7.4556, 10.3134]

7

9

0.8161

[-8.0684, 9.7007]

3

5

1.4894

[-7.3951, 10.3740]

7

10

1.9984

[-6.8861, 10.8829]

3

6

0.7384

[-8.1461, 9.6230]

8

9

2.6683

[-6.2162, 11.5528]

3

7

0.6614

[-8.2231, 9.5459]

8

10

3.8505

[-5.0340, 12.7351]

3

8

-1.1907

[-10.0753, 7.6938]

9

10

1.1823

[-7.7022, 10.0668]

3

9

1.4775

[-7.4070, 10.3620]

513

The table presents the confidence intervals for each of the ten unique pair-wise comparisons. Since all the confidence intervals contain 0, this is not a statistically significant difference. Means of two population are significantly different if their intervals are disjoint, and are not so if their intervals overlap. The empirical results in Table 1 thus validate the sensitivity of MCC in extracting the landmine features from the gray scale image data.

Fig 2. Group average comparison (Groups 1, 2, 3, … , 10) for 10 landmine features extracted using MCC algorithm

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The statistical relevance of the results shown in Table 1 has also been verified using a multi-comparison test. Fig. 2 shows the multiple comparison of the means and the group 1 (comparing landmine feature 1 with 2) mean is highlighted and the comparison interval is in blue. Because the comparison intervals for the other groups intersect with the intervals for the group 1 mean, the means are not different than group 1 mean. Other group means were selected to confirm that all group means are not significantly different from each other. In conclusion, there are no significant differences in the landmine features extracted using MCC algorithm. 5. Conclusion The experimental analysis demonstrates that given the challenges of recognizing landmine response shapes and the variability seen in the data, the graph based MCC landmine detection algorithm clearly appears to be a more appropriate match for this problem. This algorithm can also be used as a preprocessing technique for labelling the large amounts of features/shapes in the images under study. Hence this method proves to be effective for automatically extracting the features in the training data in a wide variety of applications. References [1] International campaign to ban landmines (2007) Landmine Monitor Report, Egypt section. [2] Why Landmies Are Still a Problem (2009) International Campaign to Ban Landmines. http://www.icbl.org/en-gb/problem/why-landminesare-still-a-problem.aspx [3] N. Xiang, and J. M. Sabatier (2003) "An Experimental Study on Antipersonnel Landmine Detection Using Acoustic-To-Seismic Coupling", J. Acoust. Soc. Am. 113 (3): 1333-41. [4] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck. (1995) Machine Vision, McGraw-Hill, Inc., ISBN 0-07-032018-7. [5] M.Deshpande, M.Kuramochi, G.Karypis. (2002) “Automated approaches for classifying structures”, Workshop on DataMining in Bioinformatics. 11–18. [6] L.C. Freeman. (2004) “The Development of Social Network Analysis: A Study in the Sociology of Science”, Empirical Press, Vancouver [7] S.H. Strogatz. (2001) “Exploring complexnetworks”, Nature 410: 268–276. [8] L. Grady. (2005) “Multilabel random walker segmentation using prior models”. IEEE Conference of Computer Vision and Pattern Recognition 1: 763-770. [9] M. Pavan and M. Pelillo. (2003) “A new graph-theoretic approach to clustering and segmentation”, Proc. IEEE Conf. Computer Vision and Pattern Recognition 1: 145-152. [10] D. G. Lowe, (2004) “Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision 60: 91-110. [11] H. Bay, T. Tuytelaars, and L. Van Gool. (2006) “Surf: Speeded up robust features," Computer Vision ECCV, Lecture Notes in Computer Science: 404-417. [12] N. Xiang, and J. M. Sabatier. (2000), "Landmine Detection Measurements Using Acoustic-To-Seismic Coupling", Proceedings of SPIE 4038: 645– 655. [13] O. Lezoray, L. Grady (eds.). (2012) “Image Processing with Graphs: Theory and Practice”, CRC Press. [14] Abraham Kandel, Horst Bunke, and Mark Last (Eds.). (2006) “Applied Graph Theory in Computer Vision and Pattern Recognition”, John Wiley & Sons, Inc., Publication. [15] Z. Wu and R. Leahy. (1993) “An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation”. IEEE. Trans. on Pattern Analysis and Machine Intelligence 15(11): 1101-1113. [16] Y. Boykov, O. Veksler and R. Zabih. (1999) “Fast Approximate Energy Minimization via Graph Cuts”. International Conference on Computer Vision 1: 377-384. [17] Bo Peng, Lei Zhang, David Zhang. (2013) “A survey of graph theoretical approaches to image segmentation” Pattern Recognition: 1020– 1038. [18] Vijayalakshmi Ramasamy, D. Nandagopal, N. Dasari, B. Cocks, N. Dahal, M. Thilaga, (2015) “Minimum Connected Component - A Novel Approach to Detection of Cognitive Load Induced Changes in Functional Brain Networks”, Neurocomputing 170: 15-31. [19] Jalili M (2016) “Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?” Scientific Reports 6.