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Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi
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The problem Reliable object recognition in the presence of clutter and occlusion Find a reliable matching between different views of an object or scene
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Approach The paper combines several robust approaches to create a powerful recognition system. The basic stages include: Key point detection SIFT – Scale Invariant Feature Transform Clustering matching with Hough Transform
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Previous Approaches The related research Harris corner detector (1992) (compare with key point detection) Schmid and Mohr (1997) (compare with SIFT) Disadvantage very sensitive to changes in scale
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The SIFT algorithm Scale space extrema detection - Identify potential interest points that are invariant to scale and orientation using Gaussian function Key point localization – perform a detailed fit to the nearby data of each key point for location, scale and curvature. Some initial key points are rejected
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The SIFT Algorithm Orientation assignment – One or more orientation are assign to each key point location based on local image gradient direction Key point descriptor – compute a descriptor for the local image region that is highly distinctive
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Advantage of SIFT Distinctiveness Key points which enable correct matching from a large database Large number of key points with near real time performance on standard PC Invariant to image rotation, scale, affine distortion, noise, illumination
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Applications Place recognition Robot localization and mapping in unknown environment
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Plan Replace key point detection with some available interest point detection, e.g., Harris corner detection (1 week) Implement the heart of the algorithm – the key point descriptor (2 weeks) Thanksgiving Use graph matching algorithm to match to images (1 week) Testing and improving (2 weeks)
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