Search results for "local features"

showing 2 items of 2 documents

Keypoint descriptor matching with context-based orientation estimation

2014

Abstract This paper presents a matching strategy to improve the discriminative power of histogram-based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation. This can be done when the descriptor uses a circular grid and quantized orientation steps, by computing or providing a global reference orientation based on the feature matches. The proposed matching strategy is compared with the standard approaches used with the SIFT and GLOH descriptors and the recent rotation invariant MROGH and LIOP descriptors. A new evaluation protocol based on an approximated overlap error is presented to provide an effective an…

GLOHComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformContext basedReference orientationImage descriptorLIOPDiscriminative modelMROGHHistogramKeypoint matchingSIFTComputer Science::MultimediaComputer visionInvariant (mathematics)MathematicsDominant orientationSettore INF/01 - Informaticabusiness.industryPattern recognitionGridLocal featureRotation invarianceComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingImage descriptors; Local features; Dominant orientation; Rotation invariance; Keypoint matching; SIFT; LIOP; MROGHComputer Vision and Pattern RecognitionArtificial intelligencebusiness
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PHOTOGRAMMETRY NOW AND THEN - FROM HAND-CRAFTED TO DEEP-LEARNING TIE POINTS

2022

Abstract. Historical images provide a valuable source of information exploited by several kinds of applications, such as the monitoring of cities and territories, the reconstruction of destroyed buildings, and are increasingly being shared for cultural promotion projects through virtual reality or augmented reality applications. Finding reliable and accurate matches between historical and present images is a fundamental step for such tasks since they require to co-register the present 3D scene with the past one. Classical image matching solutions are sensitive to strong radiometric variations within the images, which are particularly relevant in these multi-temporal contexts due to differen…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRANSACtie pointsSettore INF/01 - Informaticadeep learningimage matchingcultural heritageHistorical imageslocal features
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