Search results for "Deep descriptor"

showing 2 items of 2 documents

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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Is There Anything New to Say About SIFT Matching?

2020

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. Th…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabusiness.industryComputer scienceImage matchingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformPattern recognition02 engineering and technologySIFT sGLOH2 Quantization Binary descriptors Symmetric matching Hierarchical cascade filtering Deep descriptors Keypoint patch orientation Approximated overlap errorDiscriminative modelArtificial IntelligenceHistogramComputer data storage0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceSIFTsGLOH2quantizationbinary descriptorssymmetric matchinghierarchical cascade filteringdeep descriptorskeypoint patch orientationapproximated overlap errorbusinessQuantization (image processing)SoftwareInternational Journal of Computer Vision
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