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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.

What makes a good feature? • Want uniqueness • Leads to unambiguous matches in other images • Look for “interest points”: image regions that are unusual • How to define “unusual”? Finding interest points in an image Suppose we only consider a small window of pixels

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