Fusion of Visual and IR Images for Concealed Weapon Detection 1

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1 Fusion of Visual and IR Images for Concealed Weapon Detection 1 Z. Xue, R. S. lum, and Y. i ECE Department, ehigh University 19 Memorial Drive West, ethlehem, P Phone: (610) , Fax: (610) , rblum@eecs.lehigh.edu bstract Image fusion for concealed weapon detection (CWD) using visual and IR images is studied. large set of existing image fusion algorithms are identified and their performance is compared for the CWD application using quantitative and qualitative measures. Keywords : image fusion, CWD, evaluation, IR 1 Introduction Concealed weapon detection (CWD) is an increasingly important topic in the general area of law enforcement and it appears to be a critical technology for dealing with terrorism, which appears to be the most significant law enforcement problem for the next decade. Since no single sensor technology can provide acceptable performance in CWD applications, image fusion has been identified as a key technology to achieve improved CWD procedures. Existing image sensing mechanisms include thermal/infrared (IR), millimeter wave, and visual. In our current work we focus on fusing visual and IR images for CWD. While some studies have considered fusing images of these types [1,], we have not seen studies comparing the performance of different image fusion methods for CWD applications. This is the focus of our current efforts and we present some preliminary results in this paper. We focus on fusing visual and IR images. Image Fusion Methods In this section, we briefly describe the various image fusion methods considered in this paper..1 Non-Multiscale-Decomposition-ased (NMD) Methods 1) Pixel-level weighted average straightforward approach to image fusion is to take the weighted average of the pixel intensity of the two source images. Typical methods are principal component analysis (PC) [3] and adaptive weight averaging (W) [4]. In the PC method, the weightings for each source image are obtained from the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the source images. In the W method, the weighting for the IR image will assign larger weights to either the warmer and cooler pixels, while the weighting for visual image will assign larger weights to those pixels whose intensities are much different from its neighbors. ) Pixel-level maximum or minimum This method is simply to take the maximum (minimum) value of the source images pixel by pixel. 3) Nonlinear method The nonlinear method developed by Therrien et. al. [5] separates the source image into low-pass and high-pass components. The method first adaptively modifies the low-pass component of each source image to enhance the local luminance mean, and then fuses the low-pass components by a nonlinear mapping. The method then adaptively modifies the high-pass component of each source image to enhance the local contrast, and then fuses the high-pass components by weighted averaging. Finally, the fused high-pass and low-pass images are added to produce the final enhanced fused image.. Multiscale-Decomposition-ased (MD) Methods Multiscale-decomposition-based (MD) fusion methods consist of three main steps. First, each source image is decomposed into a multiscale representation using a multiscale transform. Then a composite multiscale representation is constructed from the source representations and a fusion rule. Finally the fused image is obtained by taking an inverse multiscale transform of the composite multiscale representation. The difference between each method is in the particular multiscale 1 This material is based on work supported by the U. S. rmy Research Office under grant number DD The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.

2 Figure 1 The generic framework of image fusion schemes representation and fusion rule employed. MD image fusion schemes have been categorized by Zhang and lum [6], as shown in Figure 1. The generic framework consists of five parts: MSD method, activity level measurement, coefficient grouping method, coefficient combining method, and consistency verification. The three most commonly employed MSD methods (Figure 1) include the pyramid transform (PT), discrete wavelet transform (DWT), and discrete wavelet frame (DWF). The most frequently studied versions of the pyramid transform include the aplacian pyramid [7], the filter subtract decimate pyramid [8], the contrast pyramid [9], the gradient pyramid [10], and the morphological pyramid [11]. We refer the reader to [6] for further details on the framework in Figure 1 and the different possible MD image fusion methods. 3 Evaluation Methods good fusion algorithm should preserve or enhance all the useful features from the source images, not introduce artifacts or inconsistencies which will distract human observers or the following processing, and eliminate noise and provide robustness against registration errors [1]. The fusion results can be evaluated qualitatively (visual comparison) or quantitatively. Some quantitative evaluation methods require an ideal composite image called a reference image. The statistical evaluation criteria we used to compare the image fusion methods are listed below grouped according to the need for a reference image. We employed three evaluation methods requiring a reference image (the ideal fused image). 1. The root mean square error (RMSE) between the reference image R and the fused image Z 1 N M = (, ) 1 = R i j i j 1 RMSE = Z (1) NM where N M is the size of the image. In out tests, we used gray images.. The correlation (CORR) between the reference image R and the fused image Z where R R R = N RR, Z CORR = R + R M i= 1 j= 1 R i, N M R, Z = R( i, Z i= 1 j= 1 R N M ( ; RZ = i= 1 j= 1 3. The peak signal to noise ratio (PSNR) Z Z ; PSNR = 10 log 10 (3) 1 N M = 1 = R( i, Z i j 1 NM where is the maximum value for a pixel in the image. In out tests, is equal to 55. We employed three evaluation methods not requiring a reference image. 1. The standard deviation (SD) ( i i) i= 0 () σ = h( i), i = i h( i) (4) i= 0 where h is the normalized histogram of image.. The entropy (H)

3 H = i= 0 h( i) log h( i) (5) where h is the normalized histogram of image. 3. The overall cross entropy (CE) of the source images X, Y and the fused image Z CE( X ; Z ) + CE( Y ; Z ) CE( X, Y ; Z ) = (6) where CE ( X ; Z ) ( CE ( Y ; Z ) ) is the cross entropy of the source image X (Y) and the fused image Z h X ( i) CE( X ; Z ) = hx ( i) log (7) h ( i) i= 0 4 Experimental Test In our current work, we are interested in using image fusion to help a human or computer in detecting a concealed weapon using IR and visual sensors. One example is given in Figure. Figure (a) shows the gray level visual image and (b) shows the corresponding IR image with reverse polarity. The visual and IR images have been aligned by image registration. We observe that the body is darker than the background in the IR image. Further the background is almo st white and shows little detail because of the high thermal emissivity of body. The weapon is brighter than the surrounding body due to a temperature difference between it and the body. Finally the body boundary is pretty clear. The resolution of visual image is much higher than that of IR image, but there is no information on the concealed weapon in the visual image. (a) (c) Z (b) (d) Figure. Visual and IR image for CWD For the detection of a concealed weapon we are not very concerned about the background in our images. Hence, in the tests, we process the images to get rid of the background. That is, we extract the body parts from the visual image and IR image respectively as shown in Figure (c) and (d), and then fuse the extracted body part using various image fusion methods. We tested the fifteen different image fusion methods listed below. 1) NMSD fusion methods a) Principle component analysis (PC)[3] b) daptive weight averaging (W)[4] c) Pixel-level choosing maximum (MX) d) Nonlinear method [5] ) MSD fusion methods e) aplacian pyramid [7] (4-level, average for the lowest frequency level, choose maximum for other levels) f) Filter subtract decimate pyramid [8] (4-level, average for the lowest frequency level, choose maximum for other levels) g) Contrast pyramid [9] (4-level, average for the lowest frequency level, choose maximum for other levels) h) Gradient pyramid [10] (4-level, average for the lowest frequency level, choose maximum for other levels) i) Morphological pyramid [11] (6-level, average for the lowest frequency level, choose maximum for other levels) DWT-1[6]: DWT, coefficient-based, single-scale grouping, choose max, no verification [6] (-level, average for the last band) DWT-[6]: DWT, window-based (max), no grouping, choose max, no verification [6] (-level, average for the last band) l) DWT-3[6]: DWT, window-based (weighted average), no grouping, choose max, no verification [6] (-level, average for the last band) m) DWT-4[6]: DWT, window-based (weighted average), no grouping, weighted average, no verification [6] (-level, average for the last band) n) DWT-5[6]: DWT, window-based (max), no grouping, choose max, consistency verification [6] (-level, average for the last band) o) new fusion algorithm (DWF, window-based, modified urt s method, no grouping, consistency verification) described in more detail next. In the last algorithm we employ the discrete wavelet frame (DWF) representation instead of DWT due to its shift invariance and ability to employ redundant information [13]. The fusion algorithm is based on the observation stated previously that the visual image can

4 provide us the person s appearance in high resolution while the IR image is most useful only for objects not visible in the visual image. To exploit these ideas we use a modified version of urt s fusion algorithm for each band of each level. That is, the coefficient with the largest local energy is selected when the match measure M is less than or equal to threshold α. Otherwise, the fused coefficient is taken from visual image. That is information of the IR image (the object of interest, a gun in our examples). The reference image used in the test is created by extracting the gun from the IR image and pasting it into the visual image. One example is shown in Figure 4 and another is shown in Figure 5. C C c( i, = C C where C,, if M, if M, if M C and α, and S α, and S > α; S < S (8) C are the coefficients of the C wavelet transform for the visual, IR and fused image, respectively. In (8) S is the local energy and M M = C( i + m, j + S = n, (9) m is the match measure m n C ( i + m, j + n, C n S + S ( i + m, j + n, (10) (1) () (3) In this way, we attempt to maintain high resolution in the fused image by preserving the high-resolution in the visual image to the largest extent except for the dissimilar parts of these two images. procedure of consistency-verification [6] is also employed to get rid of isolated points. Nine images are used for this experiment. They are shown in Figure 3. In Figure 3, each pair of images (the upper one is visual image and the lower one is the corresponding IR image) has been registered. These images demonstrate various different conditions including different positions for the person, different colored clothing, different positions of weapon, and different shapes of the weapon. ll the fifteen image fusion methods are applied to these images. ll the fusion results are evaluated both visually and quantitatively. Here we only present the visual result for two images due to space limitations. Figure 4 and 5 show the fused image for one test image for each fusion method. Tables 1 through 9 give the objective evaluation results for the fused image of each test image. Tables 1 through 9 also give the objective evaluation results for the individual IR and visual images. It is noted that all the evaluation computations are applied to the extracted body part only instead of whole image. Three of the evaluation methods require a reference image. Generally, the reference image should be an ideal comp osition of the IR and visual images, which is normally unknown. For CWD, the ideal fusion result should preserve the high-resolution information of the visual image and combine the important complementary (4) (5) (6) (7) (8) (9) Figure 3. Images used for test

5 (1)Visual image ()IR image (3)Ref. image (1)Visual image ()IR image (3)Ref. image (a) PC (b) W (c) MX (a) PC (b) W (c) MX (d) Nonlinear (e) aplacian (f) FSD (d) Nonlinear (e) aplacian (f) FSD (g) Contrast (h) Gradient (i)morphological (g) Contrast (h) Gradient (i)morphological ( DWT-1 ( DWT- (l) DWT-3 ( DWT-1 ( DWT- (l) DWT-3 (m) DWT-4 (n) DWT-5 (o) Our (m) DWT-4 (n) DWT-5 (o) Our Figure 4. Image fusion results Figure 5. Image fusion results

6 Table 1. Results of statistical evaluation for image 1 Image 1 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table. Results of statistical evaluation for image Image RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 3. Results of statistical evaluation for image 3 Image 3 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 4. Results of statistical evaluation for image 4 Image 4 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 5. Results of statistical evaluation for image 5 Image 5 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 6. Results of statistical evaluation for image 6 Image 6 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR

7 Table 7. Results of statistical evaluation for image 7 Image 7 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 8. Results of statistical evaluation for image 8 Image 8 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Table 9. Results of statistical evaluation for image 9 Image 9 RMSE CORR PSNR SD Entropy CE C D E F G H I J K M N O VI IR Discussion First we consider a visual evaluation of different fusion methods. For most of the images, all of the methods are acceptable except method j (DWT-1). Method j (DWT, single scale grouping without consistency verification) is good when applied to multi-focus visual (digital camera) images. However, it is not suitable for detecting a concealed weapon. s shown in Figure 4(, there are many algorithm-created spots in the images. The reason for this phenomenon is due to the significant differences between visual image and IR image which makes using grouping unsuitable. The spots in the image can be alleviated by applying consistency verification. ased on a visual evaluation, method c (pixel-level maximum) and method o (our new algorithm) are best for producing a detailed view of the person s face and a recognizable view of the object of interest (the gun). This can be explained by the characteristics of an IR image. The concealed weapon is brighter and the face is darker in the IR image relative to the visual image. When using the maximum rule, the gun in the IR image and the face in the visual image will be well represented in the fused image. Next consider the quantitative evaluation of different fusion methods. From the data shown in the Tables 1 through 9, we find method a, d, e, f, h, k, l, m, n have a relatively small value for RMSE and PSNR and a relatively large value for CORR. The reason for our method having a relatively large value for RMSE and PSNR and a relatively small value for CORR can be explained as follows. The images from the IR camera measure relative heat emissions to form an image. In the IR test images, we find that not only the concealed gun (which we are interested in) has a different gray level from its surrounding area, but also some parts of the person s clothing, which do not have contact with the human body, will also have a different temperature. In fact these parts of the person s clothing may have a similar gray-level as the concealed weapon. Thus, it is difficult to differentiate them just based on the information from visual and IR images. s a result, the fused image using our method will contain some false detection which will effect the evaluation result. nother contributing factor is due to the limitations of the quantitative evaluation algorithm. Clearly, improved quantitative evaluation methods are needed. Finally, we have found that the exact method used to compose the reference image will generally have great influence on the statistical evaluation results (Tables 1 through 9). Standard deviation and entropy can give us some information on the contrast of the image. From the relevant columns of Tables 1 through 9, we find that the images generated by method c (choosing max), e (aplacian pyramid), g (contrast pyramid), i

8 (morphological pyramid), j (DWT-1), o (our method) are better than the others in this regard. y comparing the statistical results to the visual results, we find the statistical evaluation results do not always agree with the visual evaluation results. Some methods that result in poor visual evaluation results may have relatively good statistical evaluation results (such as those methods with DWT and grouping). This is partly due to the global property of the evaluation algorithm. For CWD, we pay more attention to the face and the weapon, while these evaluation algorithms do not put more weight on these. The IR sensor we employed has some limitations when applied to our CWD application. Since IR cameras measure relative heat emissions to form an image, a concealed weapon would have to be at a different temperature fro m the body and its temperature difference must be measurable under clothing. In fact a weapon under heavy clothing may be very hard to detect. On the other hand, as we said it before, some parts of clothing which do not have contact with the human body will have a different temperature and may have a similar gray-level as the concealed weapon. This can make the detection more difficult. This problem can be partially alleviated by fusing visual, IR and MMW images. 6 Conclusion In this paper, we applied several different general image fusion methods to detect a concealed weapon under a person s clothing. We compared and evaluated those methods both subjectively and objectively. References [1] H. i,. Manjunath and S. Mitra, Multisensor image fusion using the wavelet transform, Graphical Models and Image Processing, vol.57, pp.35-45, May [5] C. W. Teherrien and W. K. Krebs, n daptive Technique for the Enhanced Fusion of ow-ight Visible With Uncooled Thermal Infrared Imagery, IEEE International Conference on Image Processing (ICIP '97) Santa a rbara, pp , October 1997 [6] Z. Zhang and R. S. lum, Categorization of Multiscale-Decomposition-ased Image Fusion Schemes with Performance Study for Digital Camera pplication, Proceedings of IEEE, vol. 87, no. 8, pp , [7] P. J. urt, and E. H. delson, The aplacian Pyramid as a Compact Image Code, IEEE Transactions on Communications, VO. COM-31, No.4, pp , pril 1983 [8] C. H. nderson, Filter-Subtract-Decimate Hierarchical Pyramid Signal nalyzing and Synthesizing Technique, United States Patent 4,718,104, Washington, D.C., [9]. Toet,. J. van Ruyven, and J. M. Valeton, Merging Thermal and Visual Images y a Contrast Pyramid, Opt. Eng. 8(7), pp , 1989 [10] P. urt, " Gradient Pyramid asis for Pattern- Selective Image Fusion," in Proceedings of the Society for Information Display, pp , SPIE, 199. [11]. Toet, Morphological Pyramidal Image Decomposition, Pattern Recognition etter, Vol1. 9, pp , 1989 [1] P. J. urt and E. H. delson, Merging images through pattern decomposition, Proceedings of SPIE, 1985, vol. 575, pp [13] M. Unser, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing, vol.4, No. 11, November 1995 [] P. K. Varshney, H. Chen, M. Uner, Registration and fusion of infrared and millimeter wave images for concealed weapon detection, in Proc. of International Conference on Image Processing, Japan, vol. 3, pp. 53-6, Oct [3] O. Rockinger, and T. Fechner, Pixel-level image fusion: the case of image sequences, Proceedings of the SPIE, vol. 3374, pp ,1998 [4] E. allier and M. Farooq, Real Time Pixel- evel ased Image Fusion Via daptive Weight veraging, ISIF 000, pwec3_3-wec3_13.

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