2014 · 18 citations · 7 references
ComputingComputer ScienceComputer VisionFeature EngineeringMachine VisionDigital Image ProcessingFeature DetectionImage AnalysisImage Feature EngineeringImage PreprocessingObject RecognitionAdaptive Histogram EqualizationContrast-limited Adaptive Histogram EqualizationHistogram EqualizationIllumination-invariant MatchingInvariant Feature DescriptorsScale-invariant Feature DetectionScale-invariant Feature TransformScale-invariant Feature Transform (Sift)
In this paper a novel method is proposed to improve the performance of the SIFT (Scale Invariant Feature Transformation) algorithm in adverse illumination conditions (in an outdoor environment at night), for this research it is proposed to work with CLAHE (Contrast Limited Adaptive Histogram Equalization), adding a preprocessing stage to the traditional methodology of the SIFT algorithm, this will be applied to the building to be found in the scene, i.e., the image pattern. A comparison with different illumination conditions (day, evening and night) will be held to know the response that will have the SIFT algorithm and to identify which moment the algorithm has a better performance.
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Object recognition from local scale-invariant features
David Lowe · 1999 · 16.2K citations
Localization Based on Building Recognition
Wei Zhang, Jana Košecká · 2006 · 73 citations