Image Stitching - A Brief Review
Image Stitching BasicsNovel MethodsImage Stitching - a Brief ReviewAriyan ZareiUniversity of Arizonaariyanzarei@email.arizona.eduJanuary 29, 2021References
Image Stitching BasicsOverview1Image Stitching Basics2Novel Methods3ReferencesNovel MethodsReferences
Image Stitching BasicsNovel MethodsImage Stitching BasicsReferences
Image Stitching BasicsNovel MethodsMotivationImage Stitching and Geo-correctionLarge scale ( 7000 10000)Low overlap (30% horizontally, 9% vertically)Geo-Referencing is importantCommercial softwares breakReferences
Image Stitching BasicsNovel MethodsReferencesImage StitchingImage Alignment and Stitching: Process of finding geometricrelation between overlapping images and generating a final imagewith larger field of view by mosaicing and blending the individualimages into a specific frame.Image acquisition platformsHand-held camerasSatelite ImageryAerial ImageryDrone ImageryGround VehiclesApplicationsRemove SensingAgriculture managementDisaster managementPanorama generation
Image Stitching BasicsNovel MethodsImage Stitching ComponentsKeypoint/Feature DetectionKeypoint/Feature Correspondence Calculation (Matching)Transformation EstimationIntensity basedFeature basedImage AlignmentImage Warping and StitchingImage Blending and Seamless StitchingReferences
Image Stitching BasicsNovel MethodsKeypoint/Feature DetectionVisually important points on the image.HARRISSIFTSURFReferences
Image Stitching BasicsNovel MethodsKeypoint/Feature MatchingFinding CorrespondencesKNNVoronoi diagrams Cross correlationReferences
Image Stitching BasicsNovel MethodsTransformation EstimationGeometric relation of images depends on many things:Homography (Perspective Projection)Scene is planar (or far away)Camera rotating around optical centerSimilarityLeveled camera (no tilting)TranslationReferences
Image Stitching BasicsNovel MethodsTransformation EstimationUsing correspondences to estimate transformationSimple LSQBe robust to outliersRANSACBAY-SACReferences
Image Stitching BasicsNovel MethodsTransformation EstimationMore complex warps and transformationsAddress the non-planar scenesParallax ving half-projectiveReferences
Image Stitching BasicsNovel MethodsReferencesImage AlignmentMajor challenge in stitching multiple images: Accumulation ofminor errors Drift. Can be solved by:Global alignment (such as bundle adjustment)Incorporating prior knowledge
Image Stitching BasicsNovel MethodsImage Blending and Seamless StitchingGenerate visually pleasant mosaics:Seamless StitchingGraph cut methodBlending techniquesLinear averagingMulti-band blendingReferences
Image Stitching BasicsNovel MethodsReferencesMultiple Image AlignmentImage stitching is a difficult field to be innovative in.Noticeable number of papers only make the previous methodsfancier (my point of view).
Image Stitching BasicsNovel MethodsReferencesMultiple Image AlignmentAlready discussed:Automatic panoramic image stitching using invariant features (Brown and Lowe)MGRAPH: A Multigraph Homography Method to Generate Incremental Mosaicsin Real-Time From UAV SwarmsIn this presentation:Image mosaicing using sequential bundle adjustmentA Novel Adjustment Model for Mosaicking Low-Overlap Sweeping ImagesNatural Image Stitching with the Global Similarity PriorRobust UAV Thermal Infrared Remote Sensing Images Stitching ViaOverlap-Prior-Based Global Similarity Prior Model
Image Stitching BasicsNovel MethodsReferencesSequential Bundle AdjustmentImage mosaicing using sequential bundle adjustmentRelatively an old paper.First paper that suggests using bundle adjustment to do image stitching (not3D reconstruction).Initial and approximate orientation of the images need to be given manually.They show how to do bundle adjustment using points as well as lines as features.Key point or line locations are found by using corner detection and Houghtransform respectively.RANSAC and initial homographies used to find correspondences.Variable State Dimension Filter (VSDF) is used to solve bundle adjustment.
Image Stitching BasicsNovel MethodsReferencesSequential Bundle AdjustmentBundle adjustmentf (x) 1X Zi (X ) Wi Zi (X )2i Zi (X ) Z i Zi (X )Zi Zi (X ): predicted value of the feature i corresponding tothe observation from parameters X .Z i : observed value of feature i.Wi : some weights.Solve the optimization formula
Image Stitching BasicsNovel MethodsSequential Bundle AdjustmentVariable State Dimension Filter (VSDF)Uses Levenberg Marquardt algorithmUpdates the state vector iterativelyReferences
Image Stitching BasicsNovel MethodsMosaicking of Low-Overlap ImagesA Novel Adjustment Model for Mosaicking Low-OverlapSweeping ImagesThis paper makes the best use of available prior knowledge.Works on UAV images.Stitching error source:Camera parametersExterior orientation of the UAVProjection plane in the object spaceParameters are separated and a new error model is definedbased on these error groups.Projection is defined by using the lat, lon,orientation(compass), pitch, yaw, and role.Optimization function using this error model is minimized.References
Image Stitching BasicsNovel MethodsReferencesMosaicking of Low-Overlap ImagesProjection is defined as:v He PV(1)Inputs/known parameters:v is the image pointP is the projective matrix. (calculated using the given cameraparameters and exterior orientation parameters)Outputs/Unknown parameters:He is the error homograph (error model)V is the 3D scene point
Image Stitching BasicsNovel MethodsReferencesMosaicking of Low-Overlap ImagesError model: built as a homographHe (He3 He2 He1 ) 1He1 : Camera parameter error. Scale and translation.He2 : Projection plane error. (?)He3 : Exterior Orientation Error. Accounts for the error inGPS, compass information and tilts (pitch, yaw and role).Optimization is done by an approach similar to that of bundleadjustment (nonlinear least squares).(2)
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelNatural Image Stitching with the Global Similarity PriorThe proposed method is built the as-projective-as-possible paper.They use a mesh deformation approach to align, warp and stitch images.An optimization function made up of global and local alignment terms is definedand minimized to find the alignment and warping parameters.
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelSteps:SIFT features are detected. APAP is used to define the grid and locally alignthe overlapping images.The warped mesh from APAP is considered. The vertexes of this mesh are thenew matched points which are used in this method.An energy function is defined which is consists of the following components:Alignment Term: ΨaLocal Similarity Term: ΨlGlobal Similarity Term: Ψg
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelAlignment Term:Ensures the alignment quality after deformation between theimages.Equation:Ψa (V ) N XXX ν̃(pkij ) ν̃(Φ(pkij )) 2i 1 (i,j) J p ij M ijkV is the list of vertexes of the meshJ is the list of overlapping imagesM is the list of all matched points between i,jν̃ expresses points position as a linear combination of fourvertex positions that surround the keypoint.Φ returns the correspondence for a given keypoint.(3)
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelLocal Similarity Term:Locally avoids distortion on non-overlapping regions of theimages.Ensure that each quad undergoes a similarity transform.Equation:Ψl (V ) NXX (ν̃ki ν̃ji ) Sjki (νki νji ) 2(4)i 1 (j,k) Eiνki is the location of an original vertex k in image i.ν̃ki is the position of the vertex after deformation (parameter).Sjki is a similarity transformation for each edge. Theparameters of this are expressed as linear combination of thevertexes.
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelGlobal Similarity Term:Preserves the naturalness of the stitched image.Ensure that each image undergoes a similarity transform.Equation:Ψg (V ) N XXw (eji )2 [(c(eji ) si cosθi )2 (s(eji ) si sinθi )2 ] (5)i 1 e i Eijeji is an edge in image i.w is a weight function that assigns more weight to the edgesthat are far from the overlapped region.The rotation and scale parameters are estimated using theinitial homographies from APAP (decomposition).
Image Stitching BasicsNovel MethodsReferencesGlobal Similarity Prior ModelThey proposed different methods for finding the best rotation forthe final stitched image (naturalness).The final optimization function is:Ṽ arg min Ψa (V ) λl Ψl (V ) Ψg (V )(6)ṼThey use a sparse linear solver to solve this optimization function.
Image Stitching BasicsNovel MethodsReferencesThermal Image Stitching using GSPRobust UAV Thermal Infrared Remote Sensing ImagesStitching Via Overlap-Prior-Based Global Similarity PriorModelVery similar to the previous paper.It is used on Thermal IR images.Probability of outliers is higher than RGB.Simply weight the GSP formula based on the overlap ratio ofthe images.
Image Stitching BasicsNovel MethodsReferencesReferencesBrown, Matthew, and David G. Lowe. ”Automatic panoramic image stitchingusing invariant features.” International journal of computer vision 74.1 (2007):59-73.Ruiz, Juan Jesus, Fernando Caballero, and Luis Merino. ”MGRAPH: Amultigraph homography method to generate incremental mosaics in real-timefrom UAV swarms.” Ieee Robotics and Automation Letters 3.4 (2018):2838-2845.McLauchlan, Philip F., and Allan Jaenicke. ”Image mosaicing using sequentialbundle adjustment.” Image and Vision computing 20.9-10 (2002): 751-759.Liu, Jianchen, et al. ”A novel adjustment model for mosaicking low-overlapsweeping images.” IEEE Transactions on Geoscience and Remote Sensing 55.7(2017): 4089-4097.Chen, Yu-Sheng, and Yung-Yu Chuang. ”Natural image stitching with theglobal similarity prior.” European conference on computer vision. Springer,Cham, 2016.Cui, Jiguang, et al. ”Robust UAV Thermal Infrared Remote Sensing ImagesStitching Via Overlap-Prior-Based Global Similarity Prior Model.” IEEE Journalof Selected Topics in Applied Earth Observations and Remote Sensing 14(2020): 270-282
Image Stitching BasicsNovel MethodsThank you for your attentionI will post the slides to my homepage ces
Image Stitching Basics Novel Methods References Motivation Image Stitching and Geo-correction Large scale ( 7000 10000) Low overlap (30% horizontally, 9% vertically)
image stitching software. The example is shown for the stitching of a neighboring image to a 'target' image. The second step shows the identification of an overlap region, based on several corresponding features. As seen in the third step, a candidate stitching path is identified by searching the two images for a low-variation (called
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This tutorial reviews image alignment and image stitching algorithms. Image align-ment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video sta-bilization, summarization, and the creation of panoramic photographs. Image stitch-
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