Transcription of Lecture 6 Features and Image Matching
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Lecture 6 Features and Image Matching
Lecture 6 Features and Image Matching UW CSE vision facultySuppose you want to create a panoramaFrom Matthew BrownWhat is the first step?Need to match portions of imagesSolution: Match Image regions using local featuresAnother exampleby Diva Sianby swashfordHarder caseby Diva Sianby scgbtHarder still?NASA Mars Rover imagesNASA Mars Rover imageswith SIFT feature matchesFigure by Noah SnavelyAnswer below (look for tiny colored ) Features can also be used for object recognitionFeature DescriptorsWhy local featuresLocality Features are local, so robust to occlusion and clutterDistinctiveness: can differentiate a large database of objectsQuantity hundreds or thousands in a single imageEfficiency real-time performance achievableGenerality exploit different types of Features in different situationsApplications Features are used for.
How to achieve invariance in image matching Two steps: 1. Make sure your feature detector is invariant • Harris is invariant to translation and rotation • Scale is trickier – common approach is to detect features at many scales using a Gaussian pyramid (e.g., MOPS) – More sophisticated methods find “the best scale” to represent each
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