Advanced Image Matching Methods

Presented by:Dr.Hamid Ebadi

Advanced Image Matching Methods

IntroductionReliability of Approach

Extracted FeaturesInterest PointsEdgesRegionsShapes

MethodsFeature & Shape Based approachRelational Based Approach

Flowchart of Shape Based Matching

Feature-Based Matching

FBM employs as conjugate entities features extracted from the original gray levels

Edges are the most important featuresStereo vision of Human eye is based on finding conjugate edges

Feature ExtractionInterest Points

To identify areas with high varianceInterest operator performs the extraction process Points with distinct features are called interest pointsOperators

Moravec(measures distinctness of an image patch compared to its

surroundings

Forstner(Rotation invariant and sub-pixel accuracyCorner points and Circular features are detected

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Extracted interest points

Feature Extraction

Edge DetectionInvolves the identification of edge pixels and grouping of edge pixels into entire edgesEdges correspond to brightness differences in the images

Sharp edgesSmooth edgesEdges occur at all orientations requiring a direction independent operatorn-order differences

Extracted Edges(LoG operator applied)

Feature Extraction

Extracting RegionsTo find areas in an image which are small, distinct, and invariant to illumination differences and perspective views Image segmentation

Histogram thresholdingTexture segmentation

Matching Interest Points

ABM is the easiest wayMore logical than ABM applied on the original imageProblems

Forshortening, breaklines, occolusion

Matching Interest Points(Forstner operator applied)

Matching Edge Pixels

Matching ScenarioTo Normalize the imagesTo Determine Search window in the matching patch (Its centre and dimension)To eliminate some candidates based on incompatible attributes (sign of Zero crossing, orientation , strengthTo determine all pixel parallaxes between pixel to be matched and candidate matches Dominant parallaxes can be found by analyzing the histogram of all possible parallaxes To consider additional edge attributes for solving ambiguous situations (strength)

Matching Edge Pixels

Matching Entire Edges

Matching entity is an entire edge rather than individual pixelsSuitable for relative orientationDepends on suitable representation of the shape characteristics of edgesSteps:

extractionRepresentationShape comparison

ApproachesΨ-S curveGeneralized Hough transform

Matching Entire Edges

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C C

B B

Ψ-S Approach

Representation of a line where the arc length S is the parameter of tangent Ψstraight line in spatial domain correspond to horizontal straight line in Ψ-S domainAdvantages:

Invariant wrt to edge’s position in the imageEdges are represented as a sequence of edge pixels, chain codesPossible to extract distinct shape features

Ψ-S Approach

Ψ-S Approach

To segment the Ψ-S curve into a sequence of straight linesTo extract shape features by analyzing the segmented Ψ-S curve To find similar verticesTo check local and global consistency

Generalized Hough Transform

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αr(mm)TangentPixel Polar Coordinates

Generalized Hough Transform

Relational Matching

Description of Primitives Relations

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Description of Primitives and Relations

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Description of Primitives and Relations

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Evaluation Function

Tree Search

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Template Matching

Matching Environment

IlluminationObject SpaceImage Space

Matching Strategy

Target Detection

Computational ApproachHistogram ThresholdingCross-CorrelationFeature-Based Matching

Precise Localization

Area-Based Methods

Feature-Based Methods

Area-Based Methods

Area-Based Methods

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Area-Based Methods

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References

T. Schenk, T. Schenk, ““ Digital Digital PhotogrammetryPhotogrammetry””, Terra , Terra Science, 1999Science, 1999M. M. KasserKasser and W. and W. EgelsEgels, , ““ Digital Digital PhotogrammetryPhotogrammetry””, Taylor and Francis, 2002, Taylor and Francis, 2002H. H. EbadiEbadi, , ““ Advanced Analytical Aerial Advanced Analytical Aerial TriangulationTriangulation””, Lecture Note, , Lecture Note, K.N.ToosiK.N.ToosiUniversity of Technology, 1999University of Technology, 1999T.C.TangT.C.Tang, , ““Digital Image CorrelationDigital Image Correlation””, , UCSEmUCSEm Report, 1988Report, 1988