K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and                 Applications (IJERA) ...
K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and                 Applications (IJERA) ...
K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and                 Applications (IJERA) ...
K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and                 Applications (IJERA) ...
K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and                Applications (IJERA) I...
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  1. 1. K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.498-502Constrained Active Contour For Interactive Image Segmentation K.Gomathy#1, D.Arun Shunmugam M.E.,#2 # P.G.Scholar, Department Of Cse, P.S.R.Engineering College,Sivakasi. # Assistant Professor, Department Of Cse, P.S.R.Engineering College,Sivakasi.Abstract Interactive image segmentation categories: boundary-based approaches and region-algortihms incorporates small amount of user based approaches.interaction to define the desired content to be Boundary-based approaches, the user is often askedextracted, has received much attention in the to specify an initial area that is “close” to therecent years. We propose a robust and accurate desirable boundary.interactive method based on the recently The "active contours" start with andeveloped continuous-domain convex active initialized contour and actively deform themselves tocontour model. The proposed method exhibits the desired border while reducing the defined energymany desirable properties of an effective in each iteration until convergence.The activeinteractive image segmentation algorithm, contours/Snake method [2] proposes the top downincluding robustness to user inputs and different approach instead of using previous bottom upinitializations with an efficient and light-weight approaches and it attempts to evolve an initialsolution for rendering smooth shadow boundaries contour toward the object boundary. To find a paththat do not reveal the tessellation of the shadow- between boundary seed points specified by the usercasting geometry. Our algorithm reconstructs the Dijkstra’s shortest path algorithm is applied in thesmooth contours of the underlying mesh and then methods based upon intelligent scissors [3], [4]extrudes shadow volumes from the smooth Boundary-based approaches requires greatsilhouettes to render the shadows. For this care to specify the boundary area or the boundarypurpose we propose an improved silhouette points, especially for complex shapes, most recentreconstruction using the vertex normal of the interactive image segmentation algorithms take theunderlying smooth mesh. Then our method regional information as the input. In region-basedsubdivides the silhouette loops until the contours approaches, the user is often asked to draw two typesare sufficiently smooth and project to smooth of strokes to label some pixels as foreground orshadow boundaries. Here we solve the two background, after which the algorithm completes theproblems in a unified framework. Gradient labeling of all other pixels. The region basedcontrolled partial differential equation (PDE) interactive segmentation algorithms, includessurfaces to express terrain surfaces, in which the RandomWalks based Methods [7]–[9], Graph Cut-surface shapes can be globally determined by the based methods [5], [6], and Geodesic methods [10],contours, their locations, and height and gradient [11]. All the above methods basically treat an imagevalues. The surface generated by this method is as a weighted graph with the nodes correspondingaccurate in the sense of exactly coinciding with to pixels in the image and edges being placedthe original contours and smooth with C1 between neighboring pixels, and to minimize a(contour active convex region) continuity certain energy function on the graph for producingeverywhere. The method can reveal smooth segmentation.saddle shapes caused by surface branching of one In the problem of interactive imageto more and can make rational interpolated sub- segmentation with the input of foreground andcontours between two or more neighbouring background strokes, which requires only a smallcontours. amount of interaction from the user. First the region-based methods are overly sensitive to smallKeywords— contour, interpolation, mesh. variations in the interactions provided by the user Assilhouette, shadow-casting, gradient in [12], the Graph Cut algorithm is sensitive to the number of seeds, while the Random Walk and Geodesic algorithms are sensitive to locations ofI) INTRODUCTION seeds is mainly due to the different behaviors of the Interactive image segmentation, incorporates different energy functions.small amount of user interaction to define the The boundaries generated by the region-desired content to be extracted, has received much based approaches, especially those generated by RWattention in the recent years. Already many and approaches based on the geodesic, are ofteninteractive image segmentation algorithms have jaggy and they do not adhere to the geometrybeen proposed. In general, interactive image features present in the image additional refinementsegmentation algorithms can be classified into two step is often needed to improve the segmentation 498 | P a g e
  2. 2. K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.498-502performance of the region-based methods. Most of normalizing the intensity of the individual particlesthe state-of-the-art interactive image segmentation images, removing reflections, and masking portionsmethods [6],[7], [9], [10] rely on additional user of images. Image preprocessing is the technique ofinputs to either globally or locally refine the enhancing data images prior to computationalboundary. However, when dealing with complex processing. The median filter is a nonlinear digitalimages, the user is often required to provide a lot of filtering technique, often used to remove noise. Suchadditional strokes or boundary points and thus noise reduction is a typical pre-processing step tostruggles with laborious refinement/editing. Another improve the results of later processing .Medianway for boundary refinement is to use the active filtering is very widely used in digital imagecontours/Snakes model [1] to refine the initial processing because, under certain conditions, itboundary contour produced by a region-based preserves edges while removing noise.segmentation approach. The refinement based onSnakes is only able to change the contour locally for B. Initialization of Contoursmoothness but the approach is incapable of Initialization of contour is done using theevolving the entire contour to snap to geometry region based method. Based on the initializedfeatures/edges, and also incapable of handling contour pre segmented output is obtained. In thistopology changes of the evolving contour. using the region based method value similarity and The mathematical tool of the new method is spatial similarity is calculated and pre segmentedthe continuous-domain convex active contour model output is obtained by initializing the contour.[14], which makes use of both the boundary and theregional information to find a global “optimal” C. Convex Active Contour Modelsolution. Continuous-domain convex methods have The convex active contour model consistsstarted to receive attention since they avoid the of two terms: a regional term formulation andinherent grid bias in all discrete graph-based boundary term formulation.methods, and also have fast and global numericalsolvers through convex optimization [15], [16]. 1) Regional Term Formulation:However, the convex active contour model so far The foreground and background seeds givehas mainly been applied for automatic image an excellent description about the color distributionssegmentation, which often results in over- of the foreground and background regions.segmentation with trivial solutions for complex Foreground/background Gaussian mixture modelsimages [14], [16]. (GMMs) introduced in [20] are estimated from foreground/background seeds, and used to representThe major contribution of this includes the the color distributions of the foreground andfollowing. background regions. Let Pr(x|F) and Pr(x|B) denote The powerful continuous-domain convex the probabilities that pixel x denotes the foregroundactive contour with one of the region based and background GMMs, respectively.methods, Geodesic/random walk where the region- PF (x) = −log Pr(x|F)/−log Pr(x|F) − log Pr(x|B)based method is used in the first step to generate an andinitial contour, and the convex active contour is then PB(x) = −log Pr(x|B)/−log Pr(x|F) − log Pr(x|B).applied to optimize the contour. Such integration (2)utilizes the seed propagation and the locationfeatures introduced by Geodesic/Random Walk to We incorporate this regional information derivedreduce the possible “small cut” problem in the from foreground/background strokes into theconvex active contour, and also the powerful contour regional term of theevolving capability provided by the convex active convex active contour model as hr (x) = PB(x) − PFcontour model to absorb the non robustness of the (x). (3)region-based approaches. Then our method This definition of hr ensures that the activesubdivides the silhouette loops until the contours are contour evolves toward the one complying with thesufficiently smooth and project to smooth shadow known GMM models. For instance, for a pixel x, ifboundaries. PB(x) > PF (x) (respectively, PB(x) < PF (x)) and PB(x)− PF (x) is positive (respectively, negative),II) CONSTRAINED ACTIVE CONTOUR u(x) tends to decrease (respectively,increase) duringMODEL the contour evolution in order to minimize (1),which In this we describe the continuous domain can lead to u(x) ≤ T (respectively, u(x) > T) and theconvex active contour model which extends the classification of the pixel belonging to theconvex active contour model. background (respectively, the foreground). The hr definition of (3)fails in the case thatA. Preprocessing the foreground and background color models are not Preprocessing images commonly involves well separated. Thus, to avoid this problem and alsoremoving low-frequency background noise, to make use of the segmentation result obtained by 499 | P a g e
  3. 3. K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.498-502the Geodesic algorithm in step 1, we further propose matte alpha is computed. With these components weto incorporate the probability map P(x) into the can paste the object onto a new Background ifregion term hr as desired with no noticeable visual artifacts by thehr (x) = α(PB(x) − PF (x)) + (1 − α)(1 − 2P(x)) (4) simple matting equation. where α, α ∈ [0, 1], is a tradeoff factor. The E. Silhouette Reconstructionsecond term (1−2P(x)) in (10) prevents the refined A silhouette is the image of a person, ancontour drifting too far apart from the initial object or scene represented as a solid shape of asegmentation. Specifically, when P(x) > 0.5 and (1 − single color, usually black, its edges matching the2P(x)) are negative, u(x) tends to increase in order to outline of the subject. The interior of a silhouette isminimize (1), which favors classifying the pixel as a basically featureless, and the whole is typicallyforeground pixel, and vice versa. presented on a light background, usually white, or none at all. In addition, it can be observed that when hr The silhouette differs from an outline which(x) → +∞(respectively, hr (x) → −∞), the regional depicts the edge of an object in a linear form, while aterm forces u(x) = 0 (respectively, u(x) = 1) to silhouette appears as a solid shape. Silhouetteminimize This observation allows us to enforce images may be created in any visual artistic media.some hard constraints in the contour evolution Our algorithm reconstructs the smoothprocess. In particular, for those pixels that have no contours of the underlying mesh and then extrudesambiguity in classification, including the pixels shadow volumes from the smooth silhouettes tolying on the foreground/background strokes and the render the shadows. For this purpose we propose anpixels having very large or very small P(x) values improved silhouette reconstruction using the vertex(P(x) > 0.9 or P(x) < 0.1), we treat them as hard normal of the underlying smooth mesh. Then ourconstraints in the contour evolution process. We method subdivides the silhouette loops until thedirectly assign a negative hr value and a positive hr contours are sufficiently smooth and project tovalue, both with extremely large magnitude, to these smooth shadow boundaries. Here we solve the twoconfirmed foreground and background pixels, problems in a unified framework. Gradientrespectively. In this way, we guarantee that the controlled partial differential equation (PDE)refined result complies with the user input and also surfaces to express terrain surfaces, in which theexploit more information from the initial surface shapes can be globally determined by thesegmentation result. contours, their locations, and height and gradient values. The surface generated by this method is2) Boundary Term Formulation: accurate in the sense of exactly coinciding with the The boundary term of ∫Ω gb(x)|∇ u| dx in original contours and smooth with C1 (contour(1) is essentially a weighed total variation of active convex region) continuity everywhere. Thefunction u, where the weight gb plays an important method can reveal smooth saddle shapes caused byrole.The definition of gb in is effective in the sense surface branching of one to more and can makethat it encourages the segmentation along the curves rational interpolated sub-contours between two orwhere the edge detection function is minimal. The more neighboring contours.problem with is that at locations with weak edges theboundary is likely to be smoothed out. Thus, in this III) CONCLUSIONpaper, we propose to incorporate the GMM In this paper, we have proposed a robustprobability map PF (x) to enhance the edge and accurate interactive image segmentation methoddetection. Particularly, we define gb as based on the continuous domain convex active contour model. We have demonstrated that ourgb = β · gc + (1 − β) · ge (6) method outperforms the state-of-the-art interactive segmentation methods. It exhibits many desirable where gc and ge are the results of applying properties for a good segmentation tool, includingthe edge detection to the GMM probability map PF the robustness to user inputs and different(x) and the original image, respectively, and β, β ∈ initializations, the ability to produce a smooth and[0, 1], is a tradeoff factor computed in a similar way accurate boundary contour, and the ability to handleas α given above formula. topology changes. In this we also proposed improved silhouette reconstruction for handlingD. Constrained Active Contour Model sophisticated shapes Based on the above calculated values theconstrained active contour model is constructed, IV) FUTURE WORKbased on the calculated values Compute the alpha This paper can be extended in a few ways.channel inside the band, once distance is obtained. For example, it might be beneficial to apply theEstimate Foreground and Background components continuous-domain convex active contour model forin Luv space for each pixel inside the band, after 500 | P a g e
  4. 4. K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.498-502other segmentation problems, such as image mattingor video segmentation.V) RESULTS 501 | P a g e
  5. 5. K.Gomathy, D.Arun Shunmugam M.E. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.498-502REFERENCES 13. F. Chan, F. Lam, P. Poon, H. Zhu, and K. 1. Thi Nhat Anh Nguyen, Jianfei Cai, Senior Chan, “Object boundary location by region Member, IEEE, Juyong Zhang, and Jianmin and contour deformation,” IEE Proc. Vis., Zheng “Robust Interactive Image Image Signal Process., vol. 143, no. 6, pp. Segmentation Using Convex Active 353–360, Dec. 2002. Contours” IEEE trans.,image process, vol. 14. X. Bresson, S. Esedo glu, P. 21, no. 8, august 2012 Vandergheynst, J.-P. Thiran, and S. Osher, 2. M. Kass, A. Witkin, and D. Terzopoulos, “Fast global minimization of the active “Snakes: Active contour models,” Int. J. contour/snake model,” J. Math. Imaging Comput. Vis., vol. 1, no. 4, pp. 321–331, Vis., vol. 28, no. 2, pp. 151–167, 2007. 1988. 15. Nguyen Thi Nhat Anh, Jianfei Cai, Juyong 3. E. Mortensen and W. Barrett, “Interactive Zhang, Jianmin Zheng “Constrained Active segmentation with intelligent scissors,” Contours for Boundary Refinement in Graph. Models Image Process., vol. 60, no. Interactive Image Segmentation” in IEEE 5, pp. 349–384,Sep. 1998. International Symposium on Circuits And 4. A. Falcao, J. Udupa, and F. Miyazawa, “An Systems (ICSAS) may2012,PP-870-873. ultrafast user-steered image segmentation 16. T. Chan, S. Esedoglu, and M. Nikolova, paradigm: Live wire on the fly,” IEEE “Algorithms for finding global minimizers Trans. Med. Imag., vol. 19, no. 1, pp. 55– of image segmentation and denoising 62, Jan. 2002. models,” SIAM J. Appl. Math., vol. 66, no. 5. Y. Boykov and M. Jolly, “Interactive graph 5, pp. 1632–1648, 2006. cuts for optimal boundary and region 17. T. Goldstein, X. Bresson, and S. Osher, segmentation of objects in N-D images,” in “Geometric applications of the split Proc. IEEE Int. Conf. Comput. Vis., vol. 1. Bregman method: Segmentation and Vancouver, BC, Canada, Jul. 2001, pp. surface reconstruction,” J. Sci. Comput., 105– 112. vol. 45, nos. 1–3, pp. 272–293, 2010. 6. C. Rother, V. Kolmogorov, and A. Blake, 18. V. Caselles, R. Kimmel, and G. Sapiro, “Grabcut: Interactive foreground extraction “Geodesic active contours,” Int.J. Comput. using iterated graph cuts,” ACM Vis., vol. 22, no. 1, pp. 61–79, 1997. SIGGRAPH, vol. 23, no. 3, pp. 1–6, 2004. 19. T. Chan and L. Vese, “Active contours 7. L. Grady, “Random walks for image without edges,” IEEE Trans. Image segmentation,” IEEE Trans. Pattern Anal. Process., vol. 10, no. 2, pp. 266–277, Feb. Mach. Intell., vol. 28, no. 11, pp. 1768– 2001. 1783, Nov. 2006. 20. Y. Wang, W. Yin, and Y. Zhang, “A fast 8. J. Zhang, J. Zheng, and J. Cai, “A diffusion algorithm for image deblurring with total approach to seeded image segmentation,” in variation regularization,” Dept. Comput. Proc. IEEE Comput. Vis. Pattern Recognit., Appl. Math., Rice Univ., Houston, TX, San Francisco, CA, Jun. 2010, pp. 2125– Tech. Rep. TR07-10, 2007. 2132. 21. C. Yang, R. Duraiswami, N. Gumerov, and 9. W. Yang, J. Cai, J. Zheng, and J. Luo, L. Davis, “Improved fast Gauss transform “User-friendly interactive image and efficient kernel density estimation,” in segmentation through unified combinatorial Proc. IEEE Int. Conf. Comput. Vis., vol. 1. user inputs,” IEEE Trans. Image Process., Nice, France, 2003, pp. 664–671. vol. 19, no. 9, pp. 2470–2479, Sep. 2010. 22. A. Blake, C. Rother, M. Brown, P. Pérez, 10. X. Bai and G. Sapiro, “A geodesic H. Philip, and S. Torr,“Interactive image framework for fast interactive image and segmentation using an adaptive GMMRF video segmentation and matting,” in Proc. model,” in Proc. Eur. Conf. Comput. Vis., IEEE Int. Conf. Comput. Vis., Rio de Cambridge, U.K., 2004, pp. 428–441. Janeiro, Brazil, Oct. 2007, pp. 1–8. 23. O. Duchenne, J.-Y. Audibert, and R. 11. A. Criminisi, T. Sharp, and A. Blake, Keriven, “Geos: Geodesic image segmentation,” in 24. “Segmentation by transduction,”in Proc. Proc. Eur. Conf. Comput. Vis., Cambridge, IEEE Comput. Vis. Pattern Recog., U.K., 2008, pp. 99–112. Anchorage, AK, Jun. 2008, pp. 1–8. 12. A. Sinop and L. Grady, “A seeded image 25. K. McGuinness and N. E. O’Connor, segmentation framework unifying graph “Toward automated evaluation of cuts and random walker which yields a new interactive segmentation,” Comput. Vis. algorithm,” in Proc. IEEE Int. Conf. Image Understand., vol. 115,no. 6, pp. Comput. Vis., Rio de Janeiro, Brazil, Oct. 868–884, 2011. 2007, pp. 1–8. 502 | P a g e