The deformable contour model is a popular technique to segment

object in image processing. Its applications range from edge and curve detection, to shape modeling and visual tracking

Foon, Dah Way

Rajeswari, Mandava

Ramachandram, Dhanesh

English

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c......o 110 /i..A--' I uVY I ~'Deformable Boundary Initialization for Object Detection inNatural Images. Usin'g ,Multiple' Scale EdgesFoon Dah Way Mandava Rajeswari Dhan.esh RamachandramSchool of Computer Science School of Computer Science School of Computer ScienceUniversity-Science Malaysia, University Science Malaysia University SCience Malaysi-a1-1800 Minden, Penang, Malaysia. 11800 Minden, Penang, Malaysia. 11800 Minden, Penang, Malaysia04-6533888,4393. '60 04..0533888,2157.60 04-6533888,4046.60dwfoon@cs.usm.my mandava@cs.usm.my dhaneshr@usm.myABSTRACTThe defonnable contour model is a popular teclmique to segmentobject in image processing. Its applications range from edge andcurve detection, to shape modeling and visual tracking. However,the perfonnance of a defonnable contour to model any arbitraryshape is heavily dependent upon proper contour initialization. Toalleviate this problem, we propose a multi-scale method to obtaina suitable edge map to aid the contour initialization problem. Theproposed technique utilizes wavelet edge detection, multi-scaleedge linking coupled with a method of classifYing relevant edges.Several parameters from the scale evolution of the multiple scaletdges detected by a continuous wavelet decomposition of anImage is used in a clustering algorithm to classifY the edgesbelonging to either the background or the object(s) of interest.This paper presents the approach and prel~inary results whichare encouraging.General TermsAlgorithmsKeywordsdefonnable contour, multiple scale edges, wavelet, initialcontour, object detection, segmentation.1. INTRODUCTIONIn computer vision, segmentation is a fundamental step prior tofur¢er processing and analysis operations perfonned on images.AU segmentation approaches. regardless of the type of imagestfiey operate on, aim to segment an object of interest from the restof the image structure. Image segmentation methods may bebroa~ly divided into three categories: region based segmentation,contour based segmentation and morphological· basedsegmentation [1). This work presented in this paper focuses on thecontour based segmentation approach.Edges are recognized as important features in shape recognition.The'human visual system locates an object by its outline wherePennissiun. to make digital or hard copies of all, or part of this work forpersonal or classroom use is granted without fee provided that copies arenQt made or distributed for profit or commercial advantage and thatCOpies bear this notice and the full citation on the first page. To copyotherw,isc, or,.republish, to post on servers or to redistribute to lists,requiresWior specific pcnnission andlor a fce. 'ColloqUium '04, May 25-26, 20Q4, Pcnang, Malaysia.COPyright,2004 is held by the author(s).sharp changes in intensity profile takes place [2]. However, edgedetection should not be considered as a simple operation to labelthe location of intensity changes: instead edge detection shouldinvolve some fom of feature extraction in order to facilitateobject recognition. Among many edge detection approaches thatare available, thp multi~scale approach to edge detection hasreceived much attention due to its close resemblance to the humanvisuaf perception model [3]: MaUat [2][4] uses multiple scaleedges to characterize different types of edges such as ramp edge,step edge, discontinuities and dirac, and shows that the edgescarry enough information to reconstruct the image to a qualitythat a human observer can visualize. Williams [5] suggests thatmultiple scale edges can be tracked from a coarse to fme scale togive satisfactory and complete edge detection. An application ofmultiscale edges on real world application has been demonstratedby Siegel [6J, who uses multiple scale infonnation in remotevisual inspection of scratches on aircraft skin.Edge detection is an important step in the image segmentationprocess. One such class of segmentation approach is thedefonnable contour method. A few exampl/is of the deformablecontour methods include the snake deformable model [7], elastictemplate [8] and the Non-Uniform Rational B-spline (NURBS)[9] [10J. The snake algorithm, introduced by Kass [7J shows theability to deform to arbitrary shape of an object. Snakes workbased on energy minimization methods where controlledcontinuity splines are allowed to move under 1he influence ofexternal image dependent forces, internal forces, and certainconstraints set by the user ~ A later development by Bimbo [8)uses an elastic template matching based on modeling of apiecewise fourth order B-spline polynomial function, that isparameterized with respect to the arc length. In each interval ofdefonnation, the B-spline points are updated by recalculating theB-spline formulation through a gradient descent algorithm. Ma [9]introduces NUJ:tBS to represent curve and contour. Liang [10]uses NURBS warping method to obtain an object's contour. This'-contour is guided by gradient vector flow (OVF) to provide anefficient convergence to the object contour. Although these~ defonnable contour methods yield fast and accurate segmentationof the desired object in the image, however, they need a userinitialized contour to start the deformatfon process.There have been some. reported. works on defonnable contourmodels that are ins~sitive to proper contour initialization. Yezzi[l1J combined the curve evolution theory with the classical snakemodel to enable the deformable contour spHt and merge accordingto topological changes in the image. In a more recent work by2. OVERVIEW OF THE APPROACHclustered using an automatic clustering algorithm. Finally, usingmorphological processing, the clustered edges are linked to form aclosed contour. The steps are summarized in Figure 13. REFERENCES[1] D. M. Tsai and H. J. wang, "Segmenting FocusedObjects in Complex Visual Images", In PatternRecognition Letters, Vol. 19, pp. 929-940,1998.[2] S. Mallat, A Wavelet Tour of Signal Processing,Academic Press, UK, 1998.[3] J. F. Canny, "A Computational Approach to EdgeDetection", In iEEE Transactions on Pattern AnalysisandMachine intelligence, Vol. 8, pp. 679-698, 1986.[4] S. JVlallat .and S. Zhong, "Characterization of Signalsfrom Multiscale Edges", In IEEE Transactions onParternAnalysis andMachine Intelligence, Vol. 14, No.7, pp. 710-732, 1992.[5] - D. 1. Williams and M. Shah. Edge, "Contours UsingMultiple Scales", In Computer Vision, Graphics, andImage Processing, Vol. 51, pp. 256-274,1990.[6] M. Siegel and P. Gunatilake, HEnhanced Remote VisualInspe,ction of Aircraft Skin", In Proceedings of theIntelligent NDF Sciences for Aging and FuturisticAircraft Workshops, p.p. 101-112, Sept, 1997.[7] M. Kass, "Snakes: Active Contour Models",International Journal ofComputer Vision, Vol. 3. No.6, pp.321-331, 1988.[8] A. D. Bimbo and P. Pala, "Image Retrieval by ElasticMatching of User Sketches", IEEE Transactions onPatternAnaiysis and Machine Intelligence, Vol. 19, No.2, pp. 121-132, 1997.[9] W. Ma and J. P. Kruth, "NURB3 curve and surfacefitting and interpolation", I!). M.Dachlen, T.Lyche, andL.L.Schumaker (ed.), Mathematical Methods inComputer Aided Geometry DeSign, Academic Press.Boston, pp. 918-927, 1995.[10J K. M. Liang, M. Rajeswari and B. E. Khoo, "Free FormShape Representation Using NURBS Modeling", In TheTenth international Conference in Central Europe onComputer GraphiCS, .Visualization and ComputerVision, Plenz, Chech Republic, pp. 67-73.[II] A. Yezzi. S. Kichenassamy, A. Kumar, P. Olver and A.Tannenbaum,. HA Geometric Snake Model forSegmentation of Medical Imagery", In IEEETransaction on Medical Imaging, Vol. 16, No.2, pp.199-209, April, 1997.[12] N. Piragios, O. Mellina-Gottardo and V. Ramesh,"Gradient Vector Flow Fast Geometric ActiveContours", In IEEE Transactions on Pattern AnalysisandMachine Intelligence, Vol. 26, No.3, pp. 402407,March,200.1Nor-.-rna.K'r.TI,as .....p pressi onI nitie.1 ·Con1:our[.,.1:ra -scale Edg_Link.ingFigure 1: The overview of the proposed approach.In this work, edge detection is performed using wavelet transfonnmodulus maxima to obtain multiple scale edges. In order to obtainthe complete edges, non-maxima suppression of edges and intra-scale edge linking are utilized. Edge detection is performed usingthe non-maxima suppression process, which produces a collectionof local maximum points. This is equivalent to points of highestintensity .changes in the image. Following this. the intra-scaleedge linking is performed resulting in edge segments and theirrespective .lengths. The lengths are used to discard the shortedges, which are most likely due to noise produced by the edgedetection. The remaining edges are then linked using an inter-scale edge linking. algorithm. After inter-scale edge linking iscomplete, a feature vector is then calculated for•• each edgesegment. These feature vectors consist of infonnation about the~dge strength, types of edges such as ramp edge or step edge andthe width of the ·edges. These feature vectors, once computed, areParagios [12], gradient vector flow geometric active contourshows the ability to handle topology changes by splitting andmerging and the method is invariant to~initial contour location.These approaches however suffer from high computation cost andtime.The major aim of this work is to perform image segmentationthrough edge detection using multi-scale edges. The segmentationresult can be used as the initial contour for a more sophisticated~nd complete segmentation .us~g deformable models, such assnakes or NURBS. This work solves the problem of manuallyinitializing the starting contour as noted earlier and .ensuresconvergence of the deformable contour to the desire objectboundary.

Deformable Boundary Initialization For Object Detection In

Natural  Images Using, Multiple Scale Edges.

http://eprints.usm.my/8102/1/Deformable_Boundary_Initialization_for_Object_Detection_in_Natural_Images_Using_(PPSKomputer).pdf

Deformable Boundary Initialization For Object Detection In Natural Images Using, Multiple Scale Edges.

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