Camouflage Breaking: A Review of Contemporary Techniques

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1 Camouflage Breaking: A Review of Contemporary Techniques Amy Whicker University of South Carolina Columbia, South Carolina rossam2@cse.sc.edu Abstract. Camouflage is an attempt to make a target "invisible" by making the target appearance blend into the background. Camouflage related work is typically either in camouflage assessment and design or in camouflage breaking. This paper will discuss two current methods of camouflage breaking. Camouflage breaking is important because of the obvious military tactics, background subtraction, and general knowledge of object extraction. The first method is multiple camouflage breaking by co-occurrence and Canny. The second method is convexity-based camouflage breaking. Both methods seem to achieve desired results, but the convexity-based camouflage breaking method is clearly a more robust algorithm. 1 Introduction Camouflage is a way of making the foreground appear to be background, therefore concealing objects in plain view. The word camouflage comes from a French word camoufler, which means to blind or veil. In the late 1800 s Abbott Thayer observed that animals use counter-shading as a way to camouflage themselves. This observation was the beginning of modern day camouflage. In 1915, the French army created the first of what we now know as camouflage. Assessment and design of camouflage has been researched and developed ever since, to insure this best camouflage available. Although assessment and design has been thoroughly researched over the years, camouflage breaking seems to have gone unnoticed. Camouflage breaking is important because of the obvious military tactics, background subtraction, and in understanding detection of non-camouflaged objects. This paper will review two of the current techniques for camouflage breaking. 1.1 Co-occurrence and Canny P. Nagabhushan and Nagappa U. Bhajantri developed the co-occurrence and Canny method and published their finding in This method can be broken into two parts. The first part determines if there is a camouflage object within the image by calculating the gray level co-occurrence matrix of the image and comparing it with the

2 gray level co-occurrence matrix of the background. Once it is known that there is a camouflage object within the image then the second part of the process begins. The second part consists of a repeated application the Canny edge detection operator until effective visualization of the camouflage objects is achieved. 1.2 Convexity-based Ariel Tankus and Yehezkel Yeshurun developed convexity-base camouflage breaking and first published their finding in This method uses an operator (D arg ) to create an output image whose intensity level is a reflection of the convexity of the original image. The D arg operator is defined by the sum of Y arg, rotated 0, 90, 180, and 270. Y arg is the y-derivative of the polar coordinates of the gradient argument of the original image. Y arg detects the zero-crossing of the gradient argument. Thus Y arg detects convexity, because the zero-crossing of the 1 st derivative determines the local minimum and local maximum of the original function. Once the D arg output is obtained, then we threshold the image to find the most convex points. Therefore any object of interest should be labeled by this method, whether camouflaged or not. 2 Method The co-occurrence and Canny camouflage breaking and the convexity-based camouflage breaking are vastly different method of solving the same problem. In the following sections we will take an in-depth look at each of these method. 2.1 Co-occurrence and Canny Method As stated before, the co-occurrence and Canny method can be broken into two parts. Assessing the image for possible camouflaged object and then bringing those objects to the foreground. In this section we will describe each step of the co-occurrence and Canny method in detail. Step 1 Camouflage Detection. The gray level occurrence matrix is used to determine how often a pattern is repeated in an image. This allows for repeated pattern to be considered as a homogenous region. This can be very useful when analyzing an image with a noisy or cluttered background.

3 First we must determine the gray level co-occurrence matrix. The gray level cooccurrence matrix, P φ, d (a, b), is the matrix of relative frequencies of the occurrence of gray level configurations. Namely, P φ, d (a, b) is how frequently two pixels with gray level a, b appear in the image separated by distance d in direction φ. For better understanding we have included an example from [1] to illustrate how to calculate the gray level co-occurrence matrix. Notice that P φ, d (a, b) is a symmetric matrix and therefore in practice only half of the matrix should be calculated, but for clarity we have included the whole matrix. Once the gray level co-occurrence matrix for both the input image and the background of the input image are calculated, then a comparison of the texture parameters can be made to determine if any camouflaged object are contained within the image. The texture parameters used are energy, entropy, maximum probability, contrast, inverse difference moment, and correlation. 1. Energy the measure of homogeneity of an image, the more homogenous an image the higher the energy. Σ a, b P2 φ, d (a, b). (1) 2. Entropy the number of occurrences of a certain pattern, the higher the entropy the more number of occurrences. Σ a, b Pφ, d (a, b) log2 Pφ, d (a,b). (2) 3. Maximum Probability results in the most predominant pixel pair. max a,b Pφ, d (a, b). (3) 4. Contrast the measure of local image variations. Σ a, b a-b k Pλ φ, d (a, b), typically k=2, λ=1. (4)

4 5. Inverse Difference moment measures the smoothness of the image. Σ a, b ( Pλ φ, d (a, b) / a-b k ). (5) 6. Correlation the measure of image linearity. ( Σ a, b [(ab) Pφ, d (a, b)] - µx µy) / (σxσy), Where µx,µy are means and σx, σ y are standard deviations, µx =Σ a a Σ b Pφ, d (a, b), µy =Σ b b Pφ, d (a, b), σx =Σ a (a- µ x )2 Σ b Pφ, d (a, b), σy =Σ b (b-µ x)2 Σ a Pφ, d (a, b) (6) With these texture parameters the image is then analyzed for camouflaged objects, an image in determined to have a camouflaged object, then the second phase of the method begins. Part 2 Visualization of Camouflage Objects. To make the camouflaged object visible to the human eye we must repeatedly apply the Canny edge detection operator. This brings the object to the foreground, but it does not extract the object from the image. Further processing will be needed to extract the object. 2.2 Convexity-based Method The convexity-based method determines places of highest convexity. Most foreground objects are convex and therefore this method locates foreground objects regardless of colorization by camouflage. Since this method not an edge based method, but a convexity-based, it is extremely reliable in detecting objects that are camouflaged where the edges tend to be misleading. Now we look at the development of this algorithm. Let I(x, y) be an input image, the Cartesian representation of the gradient map is (7) Let us convert the gradient of I(x, y) into its polar representation. The gradient argument is defined by: (8) The polar coordinates are used because we generalize convexity on basic paraboloids. The y-derivative of the polar coordinates of a paraboloid tends to infinity at the negative x-axis, therefore giving us a basis for the D arg operator. [2]

5 So, the y-derivative of the gradient argument is Yarg, which is used to create Darg by summing the rotations of Yarg at 0, 90, 180, and 270. Yarg is represent by, (9) Fig. 1. (a) Paraboloidal intensity function: I(x, y) = 100x y2. (b) Gradient argument of (a). Discontinuity ray at the negative x-axis. (c) Y arg of (a) (derivative of (b)). (d) Rotation of (a) (90 c.c.w.), calculation of gradient argument. (e) Rotation of (a) (90 c.c.w.), calculation of Y arg. (f) Response of D arg, the isotropic operator.[4] As you can see in figure 1 the reaction of Y arg and D arg on a parabola is clear. This reaction creates an output image that highlights the convex areas of the original image. Using a threshold on the intensity of the output image, we can pinpoint the most convex areas of the image. 3 Implementation We plan to do future work in camouflage breaking, which will include the implementation and assessment of both the co-occurrence and Canny method and the convexitybased method. The implementation of these two methods will give more information on the ability of each algorithm to truly determine and find camouflaged objects within an image.

6 4 Analysis The results of each method are clearly different, as seen below. Each method has merit in what it accomplishes. In the following sections we will analysis the results of each method. 4.1 Co-occurrence and Canny Method This method has only been tested on synthetic images with known backgrounds. One of the drawbacks to this method is the fact that you must have the background image to use as a gage to see if there exist a camouflaged object within another image. Finding the background image is typically not an easy task in real application. Putting that drawback aside, let s look at the result from the synthetic images. Fig. 2. (a) Camouflaged image, l and I are camouflaged in 1. (b) The Canny edge detection results. [1] As you can see in figure 2, the repeated application of the Canny edge detector was able to bring out the foreign objects in this image. The outcome appears to achieve a desired result, but then in this form the process of extraction of these objects was never addressed. Also, there is some concern about the theory versus the application of this method. Will it be able to stand up to non-synthesized image data? Hopefully future analysis will prove more helpful after implementation of this method.

7 4.2 Convexity-based Method This method has been thoroughly tested with an array of input images. This algorithm is robust because it continues to produce desired results under various conditions, such as, different illuminations, variously scaled objects of interest, different orientations and cluttered or textured images as seen in figure 3 and figure 4. Fig. 3. Left: Robustness to illumination. D arg strongly reacts to the funnel, and is robust to changes in lighting direction. Middle: Robustness to scale changes. A vase is shown in 5 different scales. Right: Robustness to orientation variations. The flashlight changes its orientation from vertical to horizontal. D arg strongly reacts to the cylindric flashlight. The detection of the flashlight is independent of orientation. [6] Fig. 3. The main focus (right: vase, left: man) remains a dominant feature in these highly textured or cluttered images and is detected with D arg. [3]

8 The convexity-based approach is also invariance to derivable strongly monotonically increasing transformation of the gray-level function as seen in figure 5. Fig. 5. Notice that the D arg (bottom row) remains similar for all 3 images. [3] Now for camouflage, as seen in figure 6 and figure 7, the D arg operator does extremely well to determine the focus of an image even if the main focus is camouflaged. Figure 6 and figure 7 show a comparison between a known edge-based detection method and the convexity-based method. Clearly, in this application, the convexity-based method is a vastly superior algorithm. Fig. 5. D arg (top row) is able to unmistakably mark the hunter, where radial symmetry found the trees to the left of the hunter. [3]

9 Fig. 5. D arg (top row) is able to clearly mark the two soldiers, where radial symmetry marked various positions, most of which where not the soldiers. [3] As you can see, the convexity-based method clearly is a remarkable approach to camouflage detection. A non-edge based method is logical and has proven quite effective at solving this problem. I look forward to exploring the convexity-based methods use in face detection and implementing the algorithm for camouflage breaking. 5 Conclusion In conclusion both methods detect camouflaged objects within an image. Each method has its own strong points and weakness. The co-occurrence and Canny method is a simple algorithm, and creates a good outline of the object, but it does not extract the object, it must have the known background, and has only been tested on synthetic images and may not be effective in real application. Further processing must be done to actually exact the camouflaged object. The convexity-based method is a robust algorithm and is precise in finding foreground objects, but it does not extract the object, and a threshold must be determined, which can change the results. As you can see each method has some advantages and disadvantage. We hope we future work that we can explore each in more detail. References 1. Nagabhushan, P., Bhajantri, N. U.: Multiple Camouflage Breaking by Co-occurrence and Canny, University of Mysore, Manasa Ganotri, Tankus, A., Yeshurun, Y., Intrator, N.: Face Detection by Direct Convexity Estimation, Pattern Recognition Letters 18(9) (1997),

10 3. Tankus, A., Yeshurun, Y.: Detection of regions of interest and camouflage breaking by direct convexity estimation, IEEE International Workshop on Visual Surveillance, pages 42-48, Bombay, India (1998). In conjunction with ICCV (1998). 4. Tankus, A., Yeshurun, Y.: A model for visual camouflage breaking, 1st IEEE International Workshop on Biologically Motivated Computer Vision (BMCV), Seoul, Korea (2000) Tankus, A., Yeshurun, Y.: Convexity-based camouflage breaking, International Conference on Pattern Recognition (ICPR), Barcelona, Spain (2000) Tankus, A., Yeshurun, Y.: Convexity Based Visual Camouflage Breaking, Computer Vision and Image understanding 82, (2001)