Search results for "local image descriptor"

showing 4 items of 4 documents

RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2

2020

This study introduces an extension of the shifting gradient local orientation histogram doubled (sGLOH2) local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state-of-the-art non-deep descriptors in recent evaluation contests dealing with both planar and non-planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happe…

FEATURE EXTRACTIONLOCAL FEATUREComputer scienceFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformFEATURE MATCHING02 engineering and technologyRobustness (computer science)Euclidean geometryComputer Science::Multimedia0202 electrical engineering electronic engineering information engineeringBeneficial effectsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabusiness.industryImage matching020206 networking & telecommunicationsPattern recognitionCOMPUTER VISIONImage Matching Local Image Descriptors RootSIFT sGLOH2Computer Science::Computer Vision and Pattern RecognitionEmbedding020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareSquare rootingIMAGE MATCHING
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SIFT Matching by Context Exposed

2023

This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetri…

FOS: Computer and information sciencesArtificial neural networkSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniBenchmark testingRANSAClocal image descriptorSettore INF/01 - InformaticaApplied MathematicsComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionTransformDetectorDelaunay triangulationMerginglocal spatial filterimage contextComputational Theory and MathematicsArtificial IntelligenceKeypoint matchingSIFTPipelineTrainingComputer Vision and Pattern RecognitionSoftware
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Which Is Which? Evaluation of Local Descriptors for Image Matching in Real-World Scenarios

2019

Matching with local image descriptors is a fundamental task in many computer vision applications. This paper describes the WISW contest held within the framework of the CAIP 2019 conference, aimed at benchmarking recent descriptors in challenging planar and non-planar real image matching scenarios. According to the contest results, the descriptors submitted to the competition, most of which based on deep learning, perform significantly better than the current state-of-the-art in image matching. Nonetheless, there is still room for improvement, especially in the case of non-planar scenes.

Matching (statistics)Computer scienceDeep descriptorVisual descriptorsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology010501 environmental sciencesMachine learningcomputer.software_genreCONTEST01 natural sciencesTask (project management)Local image descriptors0202 electrical engineering electronic engineering information engineering0105 earth and related environmental sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniLocal image descriptors Image matching Deep descriptorsImage matchingSettore INF/01 - Informaticabusiness.industryImage matchingDeep learningBenchmarkingReal image020201 artificial intelligence & image processingArtificial intelligencebusinesscomputer
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An evaluation of recent local image descriptors for real-world applications of image matching

2019

This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but als…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaImage matchingComputer sciencebusiness.industryVisual descriptorsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognition02 engineering and technology03 medical and health sciences0302 clinical medicineLocal Image Descriptors; Image MatchingRobustness (computer science)Computer Science::Computer Vision and Pattern RecognitionComputer Science::Multimedia030221 ophthalmology & optometry0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessImage matching Data-driven approach Descriptors Evaluation results Local descriptors Local image descriptors Performance degradation Real-worldScene structure Computer vision
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