An Improved Convexity Based Segmentation Algorithm for Heavily Camouflaged Images

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1 I.J. Image, Graphics and Signal Processing, 013, 3, Published Online March 013 in MECS ( DOI: /ijigsp An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images Amarjot Singh, N Sumanth Kumar Department of Electrical and Electronics Engineering, National Institute of Technolog, Warangal, India amarjotsingh@ieee.org, sumanthkumar@ieee.org Abstract The paper proposes an advanced conveit based segmentation algorithm for heavil camouflaged images. The conveit of the intensit function is used to detect camouflaged objects from comple environments. We take advantage of D operator for the detection of 3D concave or conve gralevels to ehibit the effectiveness of camouflage breaking based on conveit. The biological motivation behind D operator and its high robustness make it suitable for camouflage breaking. The traditional conveit based algorithm identifies the desired tets but in addition also identifies sub-tets due to their three dimensional behavior. The problem is overcome b combining the conventional algorithm with thresholding. The proposed method is able to eliminate the sub-tets leaving behind onl the tet of interest in the input image. The proposed method is compared with the conventional D operator. It is also compared with some conventional edge based operator for performance evaluation. Inde Terms Conveit, Camouflage, Sobel, Roberts, Cann, Edge Detection, Camouflage I. INTRODUCTION Intelligent image processing sstems are becoming increasingl important and have been applied in various fields like biomechanics [1], [], education [3], [4], [5], [6], medical [7], [8], [9], photograph [10], biometrics [11], [1], [13], [14], [15], [16], [17] motion tracking [18], [19], [0], [1], [], [3], [4], defense [5], surveillance [6], character recognition [7], [8], [9], bioinformatics [30], [31], etc. Camouflage is an attempt to obscure the signature of a tet and also to match its background [1].Work related to camouflage can be roughl divided into two sections: camouflage assessment and design [] and camouflage breaking. Despite the ongoing research, onl little has been said in the computer vision literature on visual camouflage breaking [3], [4], [5], [6]. The paper presents evidence that gra level conveit can be effectivel used for segmenting heav camouflaged images. This is based on Thaer s principle of counter-shading [7], which observes that some animals use apathetic coloration to prevent their image (under sun light) from appearing as conve gralevels to a viewer. This indicates that other animals might perform camouflage breaking activit depending on the conveit of gralevels the see (or else there was no need in such an apathetic coloration). We inspired from this approach aim at detection of heavil camouflaged objects using 3D concavit and conveit. The purpose of the operator is detection of conve or concave objects in highl cluttered scenes, and in particular under camouflage conditions. In order to perform this task, we directl appl the suggested operator D on the intensit function. D responds to smooth 3D concave and conve domains based on 3D structure of objects. The operator is robust as it is neither limited to an particular light source nor on the reflectance function. The robustness and invariance characterizing D [7] as well as the biological motivation make it suitable for camouflage breaking, even for camouflages that might delude a human viewer. As compared to the present attempts for camouflage breaking, this operator is independent of conditions. The tet should be 3D conve of concave was the onl previous assumption. D operator is able to segment the camouflaged object effectivel from the background in majorit of images. In certain cases, the operator also etracts additions sub tets with the object of interest due to the significant three dimensional responses of the sub tets. The problem is solved with an improved algorithm proposed in this paper. The algorithm combines the abilit of conveit based algorithm with thresholding. The improved algorithm was able to segment teted object effectivel and overcome the inabilit of the conventional conveit algorithm. The proposed method is compared with the conventional D operator. It is also compared with some conventional edge based operator for performance evaluation. We define the conveit based operator D in the net section. Intuition for D is given in section.1 which also gives particular importance for understanding its behavior. Camouflage breaking using D is given in section 3. The evidence for camouflage breaking b gre

2 56 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images level conveit is given in section 3.1. The advanced method is proposed in section 4. The edge operators are presented in section 5 while the eperimentation results are elaborated in section 6. Finall, section 7 gives the concluding remarks. II. OPERATOR FOR CONVEXITY DETECTION This section the operator used for detecting the 3D objects with smooth conve and concave domains. Assuming I(, ) as the input image, its gradient map in Cartesian representation is I(, ) ( ( I(, )), ( I(, ))). Let us convert I (, ) into its polar representation. We can define gradient ument b: (, ) ( I (, )) arctan ( ( I(, )), ( I(, ))) (11) where the two dimensional arc tangent function is: arctan( ) arctan(, ) arctan( ) arctan( ) (1) and the one dimension arctan( r ) denotes the inverse function of tan( r) so that: arctan( r) :[, ] [, ]. The idea behind the computation of the conveit of the object, which is denoted as Y is simple -derivative of ument map: Y (, ) arctan ( ( I(, )), ( I(, ))) (13) We obtain isotropic operator b appling Y operator on each image rotating it each time b 0, 90, 180, 70 and rotating them back to the original positions. The result of operator D is the sum of the four responses. derivative approach infinit. It implies that when Y cross the zero ais, it approaches infinite when moving towards the negative -ais of the arctan function. Zero crossings of the gradient ument is known b infinite limit.. Wh detect zero crossings of gradient ument? Zero crossings of the gradient ument applied on the intensit function I(, ) is detected using the function Y. The eistence of zero crossings of the gradient ument enforces a certain range of values on the gradient ument (values which are tpical near = 0; < 0). Assuming that I(, ) as a surface in 3, then the normal to its surface represents the direction of gradient ument. A range of values of gradient ument implies the certain range of directions which are normal to the intensit surface. This enforces on the intensit surface itself. The structure of intensit surface as either a paraboloidal structure or an strongl monotonicall increasing transformation which can be derivable of a paraboloidal structure is characterized in [8]. The local approimation of 3D conve and concave surfaces can be done b paraboloids since the are arbitraril curved surfaces. (Recall, that our input is discrete, and the continuous functions are onl an approimation!). The domains of detected intensit surfaces are ehibited b 3D concave and conve structure. The conveit is three dimensional, because this is the conveit of the intensit surface I(; ) (= D surface in R3;), and not conveit of contours (= 1D surface in R. This 3D conveit of the intensit surface is characteristic of intensit surfaces emanating from smooth 3D conve bodies. 3. Intuition summar With the infinite response of Y at negative -ais (of arctan), zero-crossings of gradient ument is detected. When the intensit surface is 3D conve or concave, zero crossings of arctangent occur. 3D conve intensit surfaces are generall produced b conve smooth 3D objects. Therefore, b detecting the infinite responses in Y lead to detection of intensit surface domains which characterize 3D smooth concave and conve subjects. A. Intuition for Y Intuition for described below. Y is divided into three sections as 1. What does Y detect? Zero-crossings of the gradient ument is detected b the function Y. This stems from the last step of the gradient ument calculation: the two-dimensional arctangent function. In the negative part of -ais, the arctangent function is discontinuous which cause its - Fig. 1. Thaer's principle of countershading is eplained using ra tracing when applied to clinder

3 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images 57 Fig.. (a) Flow of chart of standard conveit operator (b) Flow of chart of advanced proposed conveit operator III. CAMOUFLAGE BREAKING Through stud on robustness of operator under various conditions like illumination, scale, orientation, teture was performed in [7]. As a result, the smoothness condition of the detected 3D conve objects can be relaed. In this paper, robustness demands are further increased b introducing ver strong camouflage and further comparing the efficienc of the method with some standard edge operators. A. Biological motivation We now ehibit the biological evidence of camouflage breaking using conveit of the intensit function. This matches our idea of camouflage breaking b conveit detection (using D ). B further evidence, we can show that not onl can intensit conveit break camouflage, but also there are animals whose coloring is suited to prevent this specific technique of camouflage breaking. We ver well know that a smooth 3D conve object produces a conve intensit function under directional light. Eplaining this in biological aspect is that when an animal trunk (subject) is eposed to lighting of sun, the observer can see the shades which are conve intensit function. Thus, these shades can probabl reveal the animal, especiall in surroundings which break up shadows (e.g. woods) [7]. The biological evidence given above encourages us to use D modus operand for camouflage breaking which detects the conveit of intensit function. During thousands of ears of evolution, the abilit to trace an animal based on these shadow effects which can be dissolved b the coloration of animals. The effect of counter shading coloration was observed first during the earlier centur which can be known from Thaer s principle. Portmann [7] describes Thaer s principle: If we paint a clinder or sphere in graded tints of gra, the darkest part facing toward the source light, and the lightest awa from it, the bod s own shade so balances this color scheme that the outlines becomes dissolved. Such graded tints are tpical of vertebrates and of man other animals. Thaer's principle of countershading is eplained using ra tracing when applied to clinder s is shown in figure 1. When the animal is under top lighting (sun light), the gradual change of albedo neutralizes the conveit of the intensit function. If no countershading has been used, the intensit function would have been conve, eposing the animal to conveit based detectors (such as D ). B the effect of countershading, conveit of intensit function neutralizes, thus disabling conveit based detection. The eistence of counter-measures to conveit based detectors implies that there might eist predators who use conveit based detectors similar to D. The flow chart of the conveit operator is shown in fig. (a).

4 58 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images IV. PROPOSED METHOD The proposed method is a combination of the conveit operator and thresholding resulting into an advanced method capable of spotting the tet of interest while eliminating the sub-tets. Once the conveit operator is applied on the image, the operator identifies the object of interest due to its three dimensional propert and acknowledges the features points on the object. In addition, the operator also identifies the features points on other sub-tets due to their three dimensionalit. In order to eliminate the response on the sub-tets thresholding is applied resulting into onl the desired tets. The flow chart of the proposed method is shown in fig. (b). V. STANDARD SEGMENTATION ALGORITHMS A number of effective algorithms have been used in the past for edge detection. The basic aim of an edge detection algorithm is to locate the points in an image where its piel intensit changes sharpl. In general, edges in an image define the corresponding object and hence are helpful in identification of objects in a image or video sequence. Some of the edge detection algorithms are briefl discussed below: A. Cann edge detector The operator uses an initiall noise removal methodolog on the image under scrutin followed b edge detection. The different steps involved in this method are: Noise Reduction: The raw image is convolved with a Gaussian filter which results in a slightl blurred version of the original which is not affected b noise to an significant degree. Finding the intensit gradient of the image: In the blurred image, horizontal, vertical and diagonal edges are detected b using the algorithm. The edge detection operator returns a value for the first derivative in both horizontal and vertical directions from which the gradient and its direction can be determined. P P P (1) To the gradient magnitude non-maimal suppression is applied. B this, we get a set of edge points in the form of a binar image. Double Thresholding: In this method onl a certain edge piels of the image which have not been covered till the end of step 3 are marked. The edge piels above the high threshold are marked as strong and the ones below the low threshold are suppressed. The edge piels between the two are marked as weak. Edge Tracking b hsteresis: Strong edges are included in the final image and weak edges are included onl if the are linked to strong edges. Obviousl noise won t result in a strong edge and hence strong edges are considered to be true edges. Weak edges can either be due to true edges or noise variations. The weak edges due to true edges are more likel to be linked to strong edges. B. Sobel Operator It is a differentiation operator which roughl computes the gradient of the function that smbolizes the intensit of an image. The Sobel operator performs a -D spatial gradient measurement on an image and underlines regions of high spatial frequenc smbolizing the edges. The operator comprises of a pair of 33 convolution kernels as shown: P P Fig 3. Kernals that are convoluted with source image for sobel operator These kernals are convolved with the source image to find the derivatives in both horizontal and vertical directions. The absolute magnitude of the gradient at each point of the image is given b: P P P () The gradient can be approimated as P P P (3) The direction of the gradient is given b P arctan P The convolution kernals of the Sobel edge detector are ler due to which the input image is smoothed to a ler etent, thus making it less sensitive to noise. C. Roberts Edge Detector The Robert Cross operator performs a -D spatial gradient measurement on a source image. It gives us the regions of high spatial frequenc corresponding to edges. This operator consists of a pair of convolution kernals as shown: (4)

5 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images 59 response. The Prewitt edge detector otherwise doesn't differ much from the Sobel operator. P P Fig. 4. Kernals that are convoluted with source image for Roberts Edge Detector The source image is convolved with these two kernals resulting in both horizontal and vertical gradients. The absolute magnitude of gradient at a point is given b: P P P (5) The gradient can be approimated as: P P P (6) The direction of the gradient is given b: P arctan P The advantage of the Roberts edge detector is that it works quite fast because of its small size. However it is irresistant to noise and also fails in detecting ver sharp edges. D. Prewitt Edge Detector (7) This edge detector calculates the maimum response for a set of convolution kernels and finds the orientation of edges of each piel of the image under consideration. In most of the edge detection techniques finding the magnitude of orientation of the edges in and directions is tedious. The Prewitt edge detector overcomes this problem b finding the orientation straightforward from the kernel with the maimum E. Laplacian of Gaussian This edge detector involves a combination of Gaussian filtering and the Laplacian operator. In this method of edge detection initiall the noise in an image is decreased b convoluting that particular image with a Gaussian filter. B this all the nois points are filtered out or removed from the image. After this step a gradient measurement is done on the image and the edges of the image are identified. This is done b detecting the zero-crossings of the second order difference of the image. The mechanism of working of the laplacian of Gaussian is as follows: The image is first smoothed b convolution with a Gaussian kernel of width to filter out all the noise present in the image. The Gaussian kernel in its standard terms is given b ( ) 1 G (, ) e (8) The laplacian of the image whose intensit values are represented as f (, ) is defined as f L(, ) f Since the input image is shown as having discrete piels, we need to approimate the second derivatives in the equation for laplacian operator. As the convolution operation is associative, the Gaussian filter can be convolved with the laplacian filter and then the hbrid filter can be convolved with the image to get the results. The -D LoG function is given b: 1 LoG(, ) 1 e 4 (9) (10)

6 60 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images

7 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images 61 Fig. 8. Comparison of standard conveit operator with the advanced operator (a) Output of standard operator applied on first input image showing tet and sub-tets (a1) Output of proposed operator applied on first input image showing tet (b) Output of standard operator applied on second input image showing tet and sub-tets (b1) Output of proposed operator applied on second input image showing tet (c) Output of standard operator applied on third input image showing tet and sub-tets (c1) Output of proposed operator applied on third input image showing tet

8 6 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images Fig. 9. The output of Sobel applied on the input images is shown in Fig. (a), (a1), (a) respectivel, the output of Cann applied on the input images is shown in Fig. (b), (b1), (b) respectivel, The output of Roberts applied on the input images is shown in Fig. (c), (c1), (c) respectivel, the output of laplacian of Gaussian applied on the input images is shown in Fig. (d), (d1), (d) respectivel, the output of Prewitts applied on the input images is shown in Fig. (e), (e1), (e) respectivel VI. EXPERIMENTAL RESULTS The section eplains and elaborates the results obtained for the simulations performed upon the highl camouflaged militar images. The section compares the results of the proposed algorithm with the results obtained b appling the standard conveit operator and standard edge operators on the dataset. The eperiments were performed on MATLAB version on.ghz processor. The standard conveit operator is first applied on the images dataset of militar camouflage tanks as shown in fig. 5. The tet of interest and the sub-tets can be seen in the figure. The operator identifies the tanks in the input image due to the three dimensional response as shown in fig. 6 (a). The operator is applied on additional two images to prove the effectives on the method as shown in Fig. 6 (b) and Fig. 6 (c). In addition to the identification of tet of interest, the operator also spotted undesired sub-tets in the image which also displaced three dimensional responses. In order to eliminate the undesired response, we used the advanced method proposed in the paper. The result for the advanced method, after application on the input images is shown in fig. 7. It is evident that thresholding eliminates the response from the sub-tets leaving onl the tets of interest. Similar results are obtained for application on other two images as shown in fig. 7 (b) and fig. 7 (c).

9 An Improved Conveit Based Segmentation Algorithm for Heavil Camouflaged Images 63 Further, a comparison is performed between the results obtained from the conventional operator with the proposed operator as shown in fig. 8. It is clearl evident that the sub-tet is eliminated due to thresholding introduced in the advanced method. Similar results are obtained for application on other two images as shown in fig. 8 (b) and fig. 8 (c). The efficienc of the proposed method is also proved across five standard edge operators namel Sobel, Cann, Roberts, laplacian of Gaussian and Prewitts. The edge operator work on grascale variation using the edges to identif the object. It is observed that the edges in the image are weak leading to poor results as shown in fig. 9. The output of Sobel applied on the input images is shown in Fig. 9 (a), (a1), (a) respectivel, the output of Cann applied on the input images is shown in Fig. 9 (b), (b1), (b) respectivel, The output of Roberts applied on the input images is shown in Fig. 9 (c), (c1), (c) respectivel, the output of laplacian of Gaussian applied on the input images is shown in Fig. 9 (d), (d1), (d) respectivel, the output of Prewitts applied on the input images is shown in Fig. 9 (e), (e1), (e) respectivel On the other hand, the D advanced operator uses the intensit of the three dimensionalit of the object for identification leading to efficient results. VII. CONCLUSION In order to eliminate the unwanted tets thresholding is combined to get the desired tets, which resulted into highl efficient results. The effectiveness of advanced conveit operator is demonstrated b multiple eperiments performed in the paper. Finall, the superiorit of the proposed method is proved over the standard conveit camouflage breaker ( D ) and edge-based operators namel Cann edge detector, Sobel operator, Roberts edge detector, Prewitt edge detector and Laplacian of Gaussian. Conveitbased camouflage breaking was found ver robust and in all the cases much more effective. REFERENCES [1]. A. C. Copeland and M. M. Trivedi. Models and metrics for signature strength evaluation of camouflaged tets. Proceedings of the SPIE, 3070:194{199, []. G. E. Irvin and M. G. Dowler. Phsiologicall based computational approach to camouflage and masking patterns. Proceedings of SPIE, 1700:481{488, 199. [3]. F. M. Gretzmacher, G. S. Ruppert, and S. Nberg. Camouflage assessment considering humanperception data. Proceedings of the SPIE, 3375:58{67, [4]. L. B. Hepinger. Camouflage simulation and effectiveness assessment for the individual soldier. Proceedings of SPIE, 1311:77{83, [5]. I. V. Ternovski and T. Jannson. Mappingsingularities-based motion estimation. Proceedings of the SPIE, 3173:317{31, [6]. S. Marouani, A. Huertas, and G. Medioni. Modelbased aircraft recognition in perspective aerial imager. In Proceedings of the International Smposium on Computer Vision, pages 371{376, Coral Gables, Florida, USA, November [7]. Ariel Tankus and Yehezkel Yeshurun, "Conveitbased Visual Camouflage Breaking" Supported b grants from: Minerva Minkowski center for geometr, Israel Academ of Science for Geometric Computing, and the Moscona fund. Amarjot Singh is a Research Engineer with Tropical Marine Science Institute at National Universit of Singapore (NUS). He completed his Bachelors in Electrical and Electronics Engineering from National Institute of Technolog Warangal. He is the recipient of Gold Medal for Ecellence in research for Batch of Electrical Department from National Institute of Technolog Warangal. He has authored and co-authored 48 International Journal and Conference Publications. He holds the record in Asia Book of Records (India Book of Record Chapter) for having "Maimum Number (18) of International Research Publications b an Undergraduate Student". He has also been recognized for his research at multiple international platforms and has been awarded 3rd position in IEEE Region 10 Paper Contest across Asia-Pacific Region and shortlisted as world finalist (Top 15) at IEEE President Change the World Competition. He is the founder and chairman of Illuminati, a potential research groups of students at National Institute of Technolog Warangal (Well Known across a Number of Countries in Europe and Asia). He has worked with number of research organizations including INRIA-Sophia Antipolis (France), Universit of Bonn (German), Gfar Research (German), Twtbuck (India), Indian Institute of Technolog Kanpur (India), Indian Institute of Science Bangalore (India) and Defense Research and Development Organization (DRDO), Hderabad (India). His research interests involve Computer Vision, Computational Photograph, Motion Tracking etc.