Recombinant Skeleton Using Junction Points in Skeleton Based Images

862019 Recombinant Skeleton Using Junction Points in Skeleton Based Images

httpslidepdfcomreaderfullrecombinant-skeleton-using-junction-points-in-skeleton-based-images 15

(IJACSA) International Journal of Advanced Computer Science and ApplicationsSpecial Issue on Image Processing and Analysis

49 | P a g e wwwijacsathesaiorg

Recombinant Skeleton Using Junction Points inSkeleton Based Images

Mrs Komala LakshmiAssistant Professor

Department of Computer ScienceCoimbaote-6 Tamil Nadu India

DrMPunithavalli

Dean School of Computer sciencesDrSNS Rajalakshmi College of Arts and SNRSONSCOLLEGE

Coimbatore Tamil Nadu India

Abstract mdash We perform the task of combining two skeletonimages and to produce the recombinant skeleton We propose therecombinant skeleton algorithm to produce the recombinantskeletons The existing skeleton representation has been takenand the merge vertex detection algorithm was used beforeapplying the recombinant skeleton algorithm We can design andapply this recombinant skeleton in motion detection imagematching tracking panorama stitching 3D modeling and objectrecognition We can generate or manufacture the true real timeobject from the recombinant skeleton produced The proposedmethod utilize local search algorithm for junction validation Ourframe work suggests the range of possibility in getting therecombinant skeleton The boundary is essential for anytransformation hence the bamboo skeleton algorithm is deployedfor computing the boundary and for storing the skeleton togetherwith the boundary Thus our representation is skeleton withborder or outline From this new skeleton representation theproposed recombinant is achieved

Keywords- Recombinant skeleton bamboo skeleton valance skeleton point (VSP) core skeleton point(CSP) junction skeleton points (JSP)

I INTRODUCTION

In the field of computer vision recognition of objects in theimages is a main and important task which should beperformed for understanding the scene This task may be doneusing different features of the objects with respect to relatedapplication Some of these features are shapes of objects colorbrightness motion vector etc [1] Based on the application afeature or a combination of them may be useful and may beapplied Shape of object is a rich and effective feature of theobject which has a key role for recognition of objectsIn thispaper we aim to reconstruct the objects using their skeletonshape representation [2] Interest point detection is a recentterminology in computer vision that refers to the detection of interest points for subsequent processing An interest point is a

point in the image which in general can be characterized asfollows it has a clear preferably mathematically well-foundeddefinition it has a well-defined position in image space thelocal image structure around the interest point is rich in termsof local information contents such that the use of interestpoints simplify further processing in the vision system it isstable under local and global perturbations in the imagedomain including deformations as those arising fromperspective transformations (sometimes reduced to affine

transformations scale changes rotations andor translations) aswell as illuminationbrightness variations such that the interestpoints can be reliably computed with high degree of reproducibility Optionally the notion of interest point shouldinclude an attribute of scale to make it possible to computeinterest points from real-life images as well as under scalechanges By applying the selected interest point as junctionpoint [3] we can produce the recombinant skeleton that is a

unique stable that satisfies the skeleton topology II BACKGROUD STUDY

The skeleton [4] is important for object representation andrecognition in different areas such as image retrieval andcomputer graphics character recognition image processingand the analysis of biomedical images Skeleton-basedrepresentations are the abstraction of objects which containboth shape features and topological structures of originalobjects Many researchers have made great efforts to recognizethe generic shape by matching skeleton structures represented

by graphs or trees Because of the skeletonrsquos importance manyskeletonization algorithms[5] have been developed to representand measure different shapes We can distinguish skeletonpoints into various types as follows

A Simple PointsA simple point [6][7][8] is an object point which can be

removed without changing the topology of the object

B Regular pointsRegular points on a 1D that have exactly two neighbors

C End-points

End-points of a curve that have exactly one neighbor

D Joints

The thinness property can be easily checked if the junction

points are known in advance Some skeletonization methodsdirectly identify junction points [9] If junction points are notknown in advance they have to be identified with anothermethod

E Generating PointsEvery Skelton Point is linked to the boundary points that

are tangential to its maximal circle These are called generatingpoints





F Junction pointsJunction points (where curves meet) which can have three

or more neighbors A junction point[10] should satisfy thefollowing skeleton properties

1) it should preserve the topological information of theoriginal object

2) the position of the skeleton should be accurate

3)

it should be stable under small deformations4) it should contain the centers of maximal disks whichcan be used for reconstruction of original object

5) it should be invariant under Euclidean transformationssuch as rotations and translations and

6) it should represent significant visual parts of objectsAlso if we remove that junction point then the resulting

skeleton will not be a best skeletal extraction of the originalimage[11]

Fig21(a) shape of an elephant (b) boundary and skeleton (c)

skeleton with junction points (d) connectivity graph of the skeleton

III SIGNIFICANCE OF JUCTION POINTS

Great progress has been made that define a significancemeasure for skeleton points and remove points whosesignificance is low In [12] give a complete analysis andcompare such pruning methods Propagation velocity maximalthickness radius function axis arc length and the length of theboundary unfolded belong to the common significancemeasures of skeleton points In [13] present a few significancemeasures for pruning complex Voronoi skeletons withoutdisconnecting the skeletons In [14] they combine a fluxmeasurement with the thinning process to extract a robust andaccurate connected skeleton

IV USAGE OF JUNCTION POINTS

One of the biggest uses of skeletons is in analysis of scientific data where complex topologies can be easilyexplained using line-like drawings Furthermore skeletons canbe used for reduced modeling and to explain simple physicalphenomena Examples include plume visualization [15] vortexcore extraction [16] feature tracking [17] and many others Inall the above usage junction points play a vital role Usingthese junction points it is possible to segment a digital image

topologically into meaningful partsThis junction points playthe vital role in the applications of skeletons namely computervision image analysis and digital image processing includingoptical character recognition fingerprint recognition visualinspection pattern recognition binary image compression andprotein folding [18]

V METHODOLOGY

A Step1Using bamboo skeleton algorithm to identify theboundary points[19]

B Step 2

Using merge vertex detection algorithm to extract themerging point[20]

C Step3

Deploying recombinant skeleton algorithm formerging the selected interest point (merge vertexderived from step 2) to obtain the junction point forthe recombinant skeleton

D Step4Deploy the validation check for the skeletonproperties and the junction point to confirm therecombinant skeleton produced

VI RECOMBINANT SKELETON ALGORITHM

1 For the first input image compute the boundary pointsusing bamboo skeleton algorithm

a) Bi euro IP1 for all i=1 to n n is the set of all Pixels onthe boundary

b) Bj euro IP2 for all j=1 to m m is the set of all pixelson the boundary

2 For each Bi euro IP1 using merge vertex detection algorithmcompute

a) VSPi for all i= 1- nb) Fore each Bj euro IP2 using merge vertex detection

algorithm computec) VSPj for all j= 1- m

3 Using visual Data explorationLet rs be the number of boundary points in the recombinantskeleton

rs=m+nCompute the VSPi and VSPj combination to merge togetherso as to produce the junction points JSPx for x=1 ndash rs

Check for the validity of JSP using local search algorithm[21]4 Confirm the JSP and connect the two images IP1 and IP2 toproduce recombinant skeleton The output recombinantskeleton produced is unique and stable and preserves theskeleton topology





VII FLOW DIAGRAM

VIII RESULTS AND DISCUSSION

Fig 81The main idea is to extract the boundary points [22] of the

input image using bamboo skeleton algorithm Say vsp1vsp2vsp3vsp4 as in the fig 81then compute the mergingpoint [23] by testing the validity of the junction point and thestability of the skeleton and topological preservation

Fig 82

Fig 83Combining the vertex points after the validity check and

performance evaluation to obtain the recombinant skeletonstructure The results of the proposed method is depicted in fig82 fig 83

A Contribution and DiscussionWe have presented an unconventional approach to shape

representation and recognition by using skeletons

1 The boundary point selected from the contour is

independent of the threshold value Whatever be thethreshold value the boundary points should not beremoved and it is much more mandatory to removethe boundary deformation In the existing discreteskeleton evolution method the author utilizes thethreshold value using global measure and hence thereare Varity of different skeleton representation basedon the threshold value[22] Where as in the proposedmethod we do not remove the interested boundarypoint

2 The boundary point selected is checked for itspositive maximum curvature The boundary is formedwith the pixels having positive maximum curvature

value[25]3 Protrusion strength is used to check the interested

skeleton point as the junction point[26]to decomposethe skeleton representation into meaningful parts Inthis

Proposed method when merging the two images theprotrusion strength of the merging point becomes the junctionpoint of the recombinant shape representation





This point is having negative minima of curvature and theend point of that recombinant skeletal arc becomes theboundary point of the newly formed recombinant skeletalrepresentation and the end point on that boundary should havethe positive minima of curvature

That is the two end points of the segment is of oppositeextremum[27]

IX PERFORMANCE EVALUATION As the bamboo skeleton algorithm is free from boundary

deformation [24] and the mergevertx detection algorithm isunique and stable the proposed recombinant skeletonalgorithm [29] is also stable and preserves skeleton topology[28] As the boundary is preserved any shape matchingalgorithm can be used to retrieve the shape and recognize theobjects

X CONCLUSION AND FUTURE SCOPE

The obtained skeleton is stable and unique and from thatskeleton we can produce or reconstruct object from skeletonthat will definitely provides a platform for the researchprofessionals to manufacture machinery parts from theskeletons of the various images in the databases Soon we canproduce galaxy man may be aliens to some other planets

As the skeleton representation is derived from the inputimage by using digital image processing techniques instead of moving to the recognition process we are directed toreconstruct the given input image or new 2D objects from therecombinant skeleton representation

Any modifications developed in the generated real timeobjects can also be studied and accordingly the work can beextended from objects point of view and its representation Theproposed tracking algorithms can be used for transforming theinput image into the modified output image that could be usedfor further image processing Also it is possible to reconstructthe real time objects from the digital representation of theproposed method The experimental results prove that it couldbe possible and achievable

ACKNOWLEDGMENT

We thank X Bai and L Latecki for the skeleton andcurvature code and we also thank the authors of [22] Theauthor would like to express her gratitude to Mr Dr VSengoden Principal and secretary SNR SONS COLLEGECOIMBATORE for providing necessary infrastructure and forhis constant encouragement that led to improvise thepresentation of the quality of the paper

REFERENCES [1] H Blum A Transformation for Extraction New Descriptors of Shape

Models for the Perception of Speech and Visual Form MIT Press 1967

[2] TY Kong A Rosenfeld Digital topology Introduction and surveyComp Vision Graphics and Image Proc 48(3)357-393 1989

[3] Mrs JKo malaLakshmiand DrMPunithavallirdquoA Survey on junction points in digital Image Processingrdquo Paper accepted for the internationalconference IACCrsquo09 IEEE Delhi Section Thapar University Patiala-147004 Punjab India

[4] JKomala Lakshmi and MPunitha ValliA Survey on skeletons in digitalimage processing in the international conference proceedings of IEEEComputer Society 2009260-269

[5] JKomala Lakshmi and MPunitha ValliA Survey on skeletonization indigital image processing in the international conference proceedings of Managing Next Generations Software Applications08Sponsored byCSIR NewDelhi2008825-839

[6] G Bertrand and Z Aktouf A three-dimensional thinning algorithm usingsubfields Vision Geometry III 2356113-124 SPIE 1994


[8] TY Kong AW Roscoe A Rosenfeld Concepts of digital topologyTopology and its App 46(3)219-262 Elsevier 1992

[9] J Chuang C Tsai Min-Chi Ko Skeletonization of Three-DimensionalObject Using Generalized Potential Field IEEE PAMI 22(11)1241-1251 2000

[10] Detection and characterization of junctions in a 2D image ComputerVision and Image Understanding Volume 93 Issue 3 March 2004Pages288-309

[11] FF Leymarie 3D Shape Representation via Shock Flows PhD thesisBrown University May 2003

[12] D Shaken and A M Bruckstein Pruning Medial Axes ComputerVision and Image Understanding69(2) 156-169 1998

[13] R L Ogniewicz O Kuumlbler Hierarchic Voronoi skeletons Pattern

Recognition 28 (3) 343 ndash 3591995[14] K Siddiqi S Bouix A R Tannenbaum S W Zucker Hamilton-

Jacobi Skeletons International Journal of Computer Vision 48(3) 215-231 2002

[15] K Santilli K Bemis D Silver J Dastur P Rona Generating realisticimages from hydrothermal plume data IEEE Vis 2004

[16] DC Banks BA Singer Vortex tubes in turbulent flows identificationrepresentation reconstruction IEEE Vis 1994

[17] B Vrolijk F Reinders FH Post Feature tracking with skeleton graphsin Data Visualization The State of the Art pp 37--52 KluwerAcademic Publishers 2003

[18] A Transformation for Extracting New Descriptors of Shape by H Blumin Models for the Perception of Speech and Visual Form W Whaten-Dunn (Ed) MIT Press Cambridge MA pp 362 ndash 380R Bergevin andA Bubel

[19 ] JKomala Lakshmi and MPunitha Vallirdquodiscrete skeleton reconstructionusing bamboo skeletonrdquocommunitcated to IEEE transaction on imageprocessing

[20] JKomala Lakshmi and MPunithaValli ldquoImpact of Boundary points inskeleton based images ldquo in the international journal of advancedengineering and applications IJAEA An International Journal of Advanced Engineering amp Applications Year 2010 Volume-1 JuneIssuePrint ISSN 0975 ndash 7783Online ISSN 0975 ndash 7791 steps-indiacom ijaea cfphtml

[21]Shape Recognition by Clustering and Matching of Skeletons HamidrezaZaboli Amirkabir University of Technology Tehran Iran

[22] Xiang Bai and Longin Jan Latecki Discrete Skeleton EvolutionEMMCVPREzhou China 2007 (Applications visual skeletons cornerdetection shape decompostion)

[23] JKomala Lakshmi and MPunitha Valli rdquo Published an article titledrdquoComputation of merging points in skeleton based digital imagesrdquo in theGlobal Journal Of computer science and technology GJCST Volume 9-Number 4 September 2009 Issue wwwglobaljournalsorg Print ISSN 0975 ndash 4350Online ISSN 0975 ndash 4172

[24] Xiang Bai Longin Jan Latecki and Wen- Yu Liu ldquoSkeleton Pruning byContour partitioning with Discrete Curve Evolutionrdquo IEEE TransPAMI 29(3) 449-462 2007

[25] Aslan C Erdem A Erdem E Tari S Microsoft Corp RedmondWA Disconnected Skeleton Shape at Its Absolute Scale patternanalysis and machine intelligenceieee transaction onIEEE computersociety Volume 30 Issue 12 02 February 2008





[26] JingTing Zeng Rolf Lakaemper XingWei Yang Xin LiTempleUniversity Philadelphia PA 2D Shape Decomposition Based onCombined Skeleton-Boundary Features Lecture Notes in ComputerScience

[27]Michael Leyton Symmetry-Curvature Duality computer vision graphicsand image processing 38 327-341 (1987)

[28] J komala lakshmi and Mpunithavalli Published an article titled rdquo 2Dshape reconstruction based on combined skeleton boundary featuresrdquo inthe international journal of image processing

ISSN (Online) 1985-2304Volume 4 ndash Issue 4 September 2010Malaysia wwwcscjournalsorg

[29] J komala lakshmi and Mpunithavalli ldquoa survey on Performanceevaluation in object detection techniques of Digital Image Processing Internation journal of Computer science Issues vol 7Issue 6November2010ISSN Online 1694-0814IJCSIMaritius

AUTHORS PROFILE

The author Mrs JKomala Lakshmi has finished herBSc Mathematics from SeethaLakshmi Achi Collegefor women Pallathur during 1995She has completedher MCA from JJcollege Of arts and SciencesPudhukottai during 1998She has been Awarded herMPhil Computer ScienceBharathiar University during2008She Has been Awarded with the Silver meadal forher academic Excellence in MCA from the Institution

from She has Nine years of teaching experience in Collegiate service She is

currently working as Assistant Professor Department of Computer ScienceSNR SONS COLLEGE Coimbatore She has presented her papers in threeinternational conferences She has published three papers in four international

journals She also contributed a book publications to the computer sciencestudents She was a Graduate Student Member of IEEE She has been awardedwith the recognition from Whorsquos Wh o in the World 2011 28th Edition forher notable contribution to the society





F Junction pointsJunction points (where curves meet) which can have three

or more neighbors A junction point[10] should satisfy thefollowing skeleton properties

1) it should preserve the topological information of theoriginal object

2) the position of the skeleton should be accurate

3)

it should be stable under small deformations4) it should contain the centers of maximal disks whichcan be used for reconstruction of original object

5) it should be invariant under Euclidean transformationssuch as rotations and translations and

6) it should represent significant visual parts of objectsAlso if we remove that junction point then the resulting

skeleton will not be a best skeletal extraction of the originalimage[11]

Fig21(a) shape of an elephant (b) boundary and skeleton (c)

skeleton with junction points (d) connectivity graph of the skeleton

III SIGNIFICANCE OF JUCTION POINTS

Great progress has been made that define a significancemeasure for skeleton points and remove points whosesignificance is low In [12] give a complete analysis andcompare such pruning methods Propagation velocity maximalthickness radius function axis arc length and the length of theboundary unfolded belong to the common significancemeasures of skeleton points In [13] present a few significancemeasures for pruning complex Voronoi skeletons withoutdisconnecting the skeletons In [14] they combine a fluxmeasurement with the thinning process to extract a robust andaccurate connected skeleton

IV USAGE OF JUNCTION POINTS

One of the biggest uses of skeletons is in analysis of scientific data where complex topologies can be easilyexplained using line-like drawings Furthermore skeletons canbe used for reduced modeling and to explain simple physicalphenomena Examples include plume visualization [15] vortexcore extraction [16] feature tracking [17] and many others Inall the above usage junction points play a vital role Usingthese junction points it is possible to segment a digital image

topologically into meaningful partsThis junction points playthe vital role in the applications of skeletons namely computervision image analysis and digital image processing includingoptical character recognition fingerprint recognition visualinspection pattern recognition binary image compression andprotein folding [18]

V METHODOLOGY

A Step1Using bamboo skeleton algorithm to identify theboundary points[19]

B Step 2

Using merge vertex detection algorithm to extract themerging point[20]

C Step3

Deploying recombinant skeleton algorithm formerging the selected interest point (merge vertexderived from step 2) to obtain the junction point forthe recombinant skeleton

D Step4Deploy the validation check for the skeletonproperties and the junction point to confirm therecombinant skeleton produced

VI RECOMBINANT SKELETON ALGORITHM

1 For the first input image compute the boundary pointsusing bamboo skeleton algorithm

a) Bi euro IP1 for all i=1 to n n is the set of all Pixels onthe boundary

b) Bj euro IP2 for all j=1 to m m is the set of all pixelson the boundary

2 For each Bi euro IP1 using merge vertex detection algorithmcompute

a) VSPi for all i= 1- nb) Fore each Bj euro IP2 using merge vertex detection

algorithm computec) VSPj for all j= 1- m

3 Using visual Data explorationLet rs be the number of boundary points in the recombinantskeleton

rs=m+nCompute the VSPi and VSPj combination to merge togetherso as to produce the junction points JSPx for x=1 ndash rs

Check for the validity of JSP using local search algorithm[21]4 Confirm the JSP and connect the two images IP1 and IP2 toproduce recombinant skeleton The output recombinantskeleton produced is unique and stable and preserves theskeleton topology





VII FLOW DIAGRAM




Fig 82























ACKNOWLEDGMENT






































AUTHORS PROFILE









VII FLOW DIAGRAM




Fig 82























ACKNOWLEDGMENT






































AUTHORS PROFILE

















ACKNOWLEDGMENT






































AUTHORS PROFILE














AUTHORS PROFILE





Documents

Recombinant Skeleton Using Junction Points in Skeleton Based Images