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Automatic Objects Removal for Scene Completion Jianjun Yang Yin Wang Honggang Wang Department of Computer Science Department of Mathematics and Department of Electrical University of North Georgia Computer Science and Computer Engineering Oakwood, GA 30566, USA Lawrence Technological University University Massachusetts Dartmouth, USA Email: [email protected] Southfield, Michigan 48075, USA Email: [email protected] Email: [email protected] 5 Kun Hua Ju Shen 1 Wei Wang Department of Electrical and Department of Computer Science 0 Department of Electrical Engineering 2 Computer Engineering University of Kentucky and Computer Science Lawrence Technological University Lexington, KY 40506, USA n South Dakota State University, USA a Southfield, Michigan 48075, USA Email: [email protected] Email: [email protected] J Email: [email protected] 3 2 ] V Abstract—With the explosive growth of web-based cameras images from online photo collections, such as Flickr and C and mobile devices, billions of photographs are uploaded to streetviewdatabasesfromGoogleandMicrosoft.However, the internet. We can trivially collect a huge number of photo . it is a challenging task as the photo streams are neither s streams for various goals, such as 3D scene reconstruction c and other big data applications. However, this is not an aligned nor calibrated since they are taken in different [ easy task due to the fact the retrieved photos are neither temporal, spatial, and personal perspectives. Furthermore, aligned nor calibrated. Furthermore, with the occlusion of with the occlusion of unexpected foreground objects, it is 1 v unexpected foreground objects like people, vehicles, it is even evenmoredifficulttorecoverthewholesceneoraccurately more challenging to find feature correspondences and recon- 0 identify overlapping regions between different photos. struct realistic scenes. In this paper, we propose a structure- 7 To resolve the above issue, image in-painting is an based image completion algorithm for object removal that 9 produces visually plausible content with consistent structure effective solution. In this paper, we propose an automatic 5 and scene texture. We use an edge matching technique to object removalalgorithm for scene completion, which ben- 0 . infer the potential structure of the unknown region. Driven efits subsequent large imagery processing. The core of our 1 by the estimated structure, texture synthesis is performed method is based on the structure and texture consistency. 0 automatically along the estimated curves. We evaluate the Our proposed approach has two major contributions. First, 5 proposed method on different types of images: from highly 1 structured indoor environment to the natural scenes. Our wedevelopacurveestimationapproachtoinferthepotential : experimental results demonstrate satisfactory performance structure of the occluded region on the image. Second, an v thatcanbepotentiallyusedforsubsequentbigdataprocessing: orientated patch matching algorithm is designed for texture i X 3D scene reconstruction and location recognition. propagation. Our work has a broad range of applications r including image localization [7] [8], privacy protection [9] a Keywords: Image Completion, Texture Synthesis, On- [10] [11], and other network based applications [12] [13] line Photos, Scene Reconstruction, Object Removal [14] [15] [16]. I. INTRODUCTION II. RELATEDWORKS In the past few years, the massive collectionsof imagery Intheliterature,imagecompletionorin-paintinghasbeen on the Internet have inspired a wave of work on many intensively studied: in [17], Efros and Leung used a one- interesting big data topics: scene reconstruction, location pass greedy algorithm to render unknown pixels based on recognition, and online sharing of personal photo streams theassumptionthattheprobabilitydistributionofthepixel’s [2] [3] [4]. For example, one can easily download a huge brightnessis independentto the rest of the image when the numberof photostreams associated with a particularplace. spatialneighborhoodisgiven.In[18], theauthorsproposed Byusingfeatures(e.g.SIFT),itispossibletoautomatically an example-based approach to fill in the missing regions. estimate correspondence information and reconstruct 3D It worked well in filling in small gaps but not in large geometry for the scene [5] [6]. Imagine building a world- ones. The weakness of such approach is that it fails to scale location recognition engine from all of the geotagged preserve the potential structures. Jia et al. [19] designed 2 (a) (b) (c) (d) (e) (f) Fig.1. Scenerecovery byremovingspecifiedforegroundobject (a)Original Image(b)Ourresult(c)Contourdetection byusingOWT-UCM method [1](d)Edgeextraction (e)Structure generation intheocclusion regionbyidentifying corresponding edgepairs(f)Somedenotations inouralgorithm. animagein-paintingmethodbasedontexture-segmentation coordinates on the image. The surrounding neighborhood and tensor-voting that created smooth linking structures in centeredat (x,y) is often called as a patch,denoted as Ψ . p the occluded regions. This method sometimes introduces The coordinates of pixels inside the patch Ψ should be in p noticeableartifactduetothetextureinconsistency.Criminisi the range: [x±∆x,y±∆y]. These concepts are illustrated et al. [20] made an improvement by assigning in-painting in Figure 1(f). In our framework, there are three phases orders based on the edge strength levels. Their algorithm involvedto achieve the scene recovery:structureestimation, used a confidence map and the image edges to determine structure propagation, and remaining part filling. thepatchcompletionpriority.However,thestructuresinthe A. Structure Estimation resultingimagesarenotwellpreserved.Themethodin[21] producedabetterresultviastructurepropagation,whilethis In this phase, we estimate the potential structure in Ω approach requires more interaction. The completion results by finding all the possible edges. This procedure can be largelydependontheanimator’sindividualtechnique.Some furtherdecomposedintotwosteps:ContourDetectioninΦ other existing work also explored in [22] [23] [24]. and Curve Generation in Ω. 1) ContourDetectioninΦ: We firstsegmenttheregion III. OUR APPROACH Φ by using gPb Contour Detector [25]. It is based on the Theprocessofourframeworkis:foragivenimage,users ideaofcomputingtheorientedgradientsignalG(x,y,θ)on specify the object for removalby drawing a closed contour thefourchannelsofitstransformedimage:brightness,color around it. The enclosure is considered an unknown region a, color b and texture channel. G(x,y,θ) is the gradient that is inferredand replacedby the remainingregion of the signal, where (x,y) indicates the center location of the image.Figure1(a)showsanexample:theredcarisselected circle mask that is drawn on the image and θ indicates the as the removing object. In the resulting image Figure 1(b), orientation.ThegPbDetectoriscomposedoftwoimportant theoccludedregionisautomaticallyrecoveredbasedonthe components:mPbEdgeDetectorandsPbSpectralDetector surrounding environment. [25].WeapplylinearcombinationonmPbandsPb(factored First let us define a set of notations for the rest of our byβandγ)accordingtothegradientascentonF-measure: paper. For an image I, the target region for in-painting is denoted as Ω; the remaining part of the image is denoted gPb(x,y,θ)=β·mPb(x,y,θ)+γ·sPb(x,y,θ) (1) as φ(=I−Ω), which is also known as source region. The boundarycontouralongΩisdenotedas∂Ω.Apixel’svalue Thus a set of edges in Φ can be retrieved via gPb. is represented by p = I(x,y), where x and y are the 2D However, these edges are not in close form and have 3 classificationambiguities.Tosolvethisproblem,weusethe regions < φ ,φ > which is partitioned by the edge x1 x2 Oriented Watershed Transform [25] and Ultrametric Con- E are used to compare with the corresponding regions of s tour Map [1] (OWT-UCM) algorithm to find the potential anotheredge E . This procedureis describedon lines 7−9 t contours by segmenting the image into different regions. of the algorithm III.1. Each neighboring region is obtained TheoutputofOWT-UCM is a set of differentcontours{C } by lowing down the threshold value t to faint out more i and their corresponding boundary strength levels {L } as detailed contours as Figure 1(d) shows. i Figure 1(c) shows. To compute the similarity between regions, we use the 2) Curve Generation in Ω: After obtainingthe contours Jensen-Shannon divergence [26] method that works on the {C } fromthe aboveprocedure,salientboundariesinΦ can color histograms: i be foundby traversing{C }. Our method for generatingthe i curves in Ω is based on the assumption: for the edges on n 2·Hi 2·Hi the boundary in Φ that intersects with the ∂Ω, it either d(H1,H2)=X{Hi1·logHi +H1i +Hi2·logHi +H2i} ends inside Ω or passes through the missing region Ω and i=1 1 2 2 1 (2) exits at another point of ∂Ω. Below is our algorithm for where H and H are the histograms of the two regions identifying the curve segments in Ω: 1 2 φ ,φ ;i indicatesthe indexofhistogrambin. Forany two 1 2 edge(E ,E ),thesimilaritybetweenthemcanbeexpressed Algorithm III.1 Identifying curve segments in Ω s t as: Require: Construct curve segments in Ω. Ensure: The generated curves have smooth transition between known edges. ||L −L || M(E ,E )= s t ·min{d(H ,H )+d(H ,H )} 1: Initial t=1.0 s t L si ti sj tj max 2: For t=t−∆t (3) 3: if ∃e∈{C}:E∩∂Ω6=∅ i and j are the exclusive numbers in {1,2}, where 1 and 4: Insert e into {E} 2 representthe indices of the two neighboringregionsin φ 5: End if t<δT aroundaparticularedge.The L isthe maxvalueofthe 6: Set t=t0, retrieve all the contours in {Ci} with Li>t max 7: Obtain <φx1,φx2> for each Ex two comparing edges’ strength levels. The first multiplier 8: DPon{<φ01,φ02 >,<φ11,φ12 >,...}tofindoptimalpairs is a penalty term for big difference between the strength from the list. levels of the two edges. To find the optimal pairs among 9: According to the optimal pairs, retrieve all the corresponding the edge list, dynamic programming is used to minimize edge-pairs: {(E ,E ),(E ,E ),...)}. x1 x2 x3 x4 the global distance: M(E ,E ), where s 6= t and 10: Compute a transition curve Cst for each (Es,Et). Ps,t s t s,t ∈ {0,1,...,size({E })}. To enhance the accuracy, a i maximumconstraintisusedtolimittheregions’difference: In algorithm III.1, it has three main parts: (a) collect all d(H ,H ) < δ . If the individual distance is bigger than 1 2 H potentialedges{E } in Φ thathits ∂Ω;(b)identifyoptimal x the pre-specified threshold δ , the corresponding region H edge pairs {(E ,E )} from {E }; (c) construct a curve C s t x st matching is not considered. In this way, it ensures if there for each edge pair (E ,E ). s t are no similar edges existed, no matching pairs would be EdgesCollection:TheoutputofOWT-UCMarecontours identified. sets {C } and their corresponding boundary strength levels i Generate Curves for each (E ,E ) : we adopt the idea s t {L }. Given different thresholds t, one can remove those i offittingtheclothoidsegmentswithpolylinestokedatafirst contours C with weak L. Motivated by this, we use the beforegenerating a curve [27]. Initially, a series of discrete Region-Split scheme to gradually demerge the whole Φ pointsalong the two edgesE and E are selected, denoted s t into multiple sub-regions and extract those salient curves. as{p ,p ,...,p ,p ,p ,...,p }.Thesepointshavea s0 s1 sn t0 t1 tm This process is carried out on lines 1-9: at the beginning distance with each other by a pre-specified value ∆ . For d the whole region Φ is considered as one contour; then any three adjacent points {p ,p ,p }, the correspond- i−1 i i+1 iteratively decrease t to let potential sub-contours{C } faint i ing curvature k could be computed according to [28]: i out according the boundary strength; Every time when any edgesefromthenewlyemergedcontours{C}weredetected 2·det(p −p ,p −p ) of intersecting with ∂Ω, they are put into the set {E}. k = i i−1 i+1 i (4) i ||p −p ||·||p −p ||·||p −p || OptimalEdgePairs:thereasonofidentifyingedgepairs i i−1 i+1 i i+1 i−1 is based on the assumption if an edge is broken up by Combining the above curvature factors, a sequence of Ω, there must exist a pair of corresponding contour edges polyline are used to fit these points. The polylines are in Φ that intersect with ∂Ω. To find the potential pairs expected to have a possibly small number of line seg- {(E ,E )} from the edge list {E }, we measure the cor- ments while preserving the minimal distance against the s t x responding enclosed regions similarities. The neighboring original data. Dynamic programming is used to find the 4 most satisfied polyline sequence by giving a penalty for Adaptive Patch by defining a new formulas for E and M each additional line segment. A set of clothoid segments in the structure propagation. can be derived corresponding to each line segment. After Traditionally, the node energy E (t ) is defined as the i i a series rotations and translations over the clothoid, a final Sum of Square Difference(SSD) by comparing the known curve C is obtained by connecting each adjacent pair with pixels in each patch Ψ with the candidate corresponding i G2 continuity [27]. Figure 1(e) demonstrates the curve portion in Ψ^ . But this method limits the salient structure ti generation result. directions. Instead of using SSD on the two patches, a series of rotations are performed on the candidate patch B. Structure Propagation: beforecomputingthesimilarity.Mathematically,Ei(ti)can be formulated as: After the potential curves are generated in Ω, a set foofuntedxtfuroremptahtechreesm,adineinnogterdegaiosn{ΨΦ0,aΨnd1,p..l.a}c,endeaeldontgo tbhee Ei(ti)=αλ·P·X||Ψi−R˙(θ)·Ψ^ti||2λ (5) estimated curves by overlapping with each other with a Where R˙ represents different rotations on the patch Ψ^ . ti certain proportion. Similar to [21], an energy minimization Since the size of a patch is usually small, the rotation based method is proposed in a Belief Propagation (BP) can be specified with an arbitrary number of angles. In framework. However, we have different definitions for the our experiment, it is specified as θ ∈ {0,±π,±π,π}. The 4 2 energy and message passing functions. The details are in parameter λ represents the number of known pixels in Ψ i the algorithm III.2. that overlap with the rotated patch Ψ^ . P is a penalty ti term, the more number of overlapping pixels, the higher Algorithm III.2 BP Propagation Algorithm of similarity is assigned. So we use P to discourage the Require: Render the texture for each patch Ψ in Ω along the patches with smaller number of sharing pixels. Here, the i estimated structures. percentage is expressed as P = λ (l is the length of Ψ). l2 Ensure: Findthebestmatchingpatcheswhileensuringtheglobal α is the corresponding normalized scalar. Thus the best λ coherence and consistency. matching patch Ψ^ is represented by two factors: index t i 1: ForeachcurveC inΩ, defineaseriesof anchor pointsonit, and rotation R . {a ,|i=1 n}. i 2: Coillectexe→mplar-texturepatches{Ψ^ti}inΦ,whereti ∈[1,m] canInbaeseixmpirleasrsewdaya,s:the energy Eij(ti,tj) on each edge Eij 3: Setup a factor graph G ={V,E} based on {C} and {ai} 4: Disetfihneiningdethxeinen[e1r,gMy f].unction E for each ai: Ei(ti), where ti Eij(ti,tj)=αλ·P·X||Ψi(ti,θti)−Ψj(tj,θtj)||2λ (6) 5: DefiningthemessagefunctionMij foreachedgeE inG,with Hereiandjaretheindicesofthetwoadjacentpatchesin initial value M 0 ij 6: Iteratively update←all the messages Mij passed between {ai} Ω.Apenaltyschemeisappliedtothesimilaritycomparison. 7: Mij ←minai{Ei(ti)+Eij(ti,tj)+Pk∈N(i),k6=jMki} The two parameters for Ψi indicate the index and rotation 98:: Aensdsigunntitlhe∆bMesijt<maδt,ch∀iin,gjt(ebxytuCreonpvaetrcghenfrcoem) {Ψ^t} for each ai ifsordtehreivesodufrrcoempatthcehersesiunlt{sΨôtfi}t.hTehaebomveesseangeersgyprfoupnacgtaiotinosn. that argmin {P E (t )+P E (t ,t )}. Here T [T,R] i∈V i i (i,j)∈E ij i j We adopta similarmethodas[21],wherethemessageM is an n dimensional vector [t ,t ,...,t ], where i ∈ [1,n]; ij 1 2 n R is also an n dimensional vector [r1,r2,...,rn] with each passes by patches Ψi is defined as: element representing the orientation of source patch Ψ^ . ti M =E (t )+E (t ,t ) (7) ij i i ij i j Inthe algorithm,the anchorpointsare evenlydistributed Through iterative updating on the BP graph, an optimal alongthecurveswithanequaldistancefromeachother∆d. decisionof{ti} forthepatchesin {Ψi}ismadebyminimiz- These points represent the center where the patches {Ψ } ing the nodes’ energy. This principle can be formulated in i (l×l) are synthesized,as shown in Figure1(f). In practice, the definition below: wedefine∆d= 1·l.The{Ψ^ }isthesourcetexturepatches in Φ. They are c4hosen on ftrom the neighborhood around ^ti =argmin{Ei(ti)+XMki} (8) ∂Ω. For the factor graph building, we consider each ai as ti k a vertex V and E =a a , where i, j are the two adjacent Where k is one of the neighbors of the patch Ψ : k ∈ i ij i j i points. N(i). ^t is the optimal index for the matching patch. To i In previous works [21] [20], each Ψ have the same achieveminimumglobalenergycost,dynamicprogramming i orientation as Ψ^ , which limits the varieties in the source is used. Each assignment for Ψ or a is considered as a texture. Noticintgi that different patch orientations could stage. In each stage, the choicesiof Ψ^ i represent different ti produce different results, we introduce a scheme called states.TheedgeE representsthetransitcostfromstateΨ^ ij ti 5 at stage i to state Ψ^ at stage j. Starting from i = 0, an on the confidence and isophote terms. One can notice both tj optimalsolutionis achievedby minimizingthe totalenergy the triangle and the circles are well completed in our result ξ (t ) from last step: Figure 2(d) comparing with Criminisi’s method in Figure i i 2(c). To further demonstrate the performance, a set of images ξ (t )=E (t )+min{E (t ,t )+ξ (t )} (9) i i i i ij i j i−1 i−1 are used for scene recovery: ranging from indoor envi- where ξ (t ) represents a set of different total energy ronment to natural scenes. Figure 3(e) shows an indoor i i values at current stage i. In the situation of multiple inter- case where highly structured patterns often present, such sectionsamongcurvesC,weadoptedtheideain[21],where as the furniture, windows, walls. The green bottle on the readers can refer for further details. officepartitionissuccessfullyremovedwhilepreservingthe remainingstructure.Inthisexample,threepairsofedgesare C. Remaining Part Filling: identified and connected by the corresponding curves that AfterthecurvesaregeneratedinΩ,wefilltheremaining are generated in the occluded region Ω. Figure 3(g) and regions by using the exemplar-based approach in [20]. 3(f)showtheresultsofremovingtreesinthenaturescenes. The ∂Ω is getting smaller and smaller by spreading out Several curves are inferred by matching the broken edges the known pixels Φ in a certain order. To enhance the along ∂Ω and maximizing the continuity. We can notice accuracy,all the pixels in the above generate patches along the three layers of the scene (sky, background trees, and the estimated curves are assigned with a pre-computed grass land) are well completed. In Figure 3(h), it shows a confidence value based on the confidence updating rule in case that a perching bird is removed from the tree. Our [20]. structure estimation successfully completes the tree branch with smooth geometric and texture transitions. IV. EXPERIMENTS V. 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