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Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point

Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point. D. Lowe, IJCV 2004. Presenting – Anat Kaspi. The problem. Reliable object recognition in the presence of clutter and occlusion Find a reliable matching between different views of an object or scene. Approach.

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Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point

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  1. Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi

  2. The problem • Reliable object recognition in the presence of clutter and occlusion • Find a reliable matching between different views of an object or scene

  3. 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

  4. 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

  5. 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

  6. 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 The SIFT Algorithm

  7. 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

  8. Applications • Place recognition • Robot localization and mapping in unknown environment

  9. 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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