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Goal: Identify sudden changes (discontinuities) in an image This is where most information in an image is coded Example: line drawings. Edge detection. What causes an edge?. Depth discontinuity Surface orientation discontinuity Changes in surface properties
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Goal: Identify sudden changes (discontinuities) in an image This is where most information in an image is coded Example: line drawings Edge detection
What causes an edge? • Depth discontinuity • Surface orientation discontinuity • Changes in surface properties • Light discontinuities (e.g. shadows)
Scale Increased scale: • Eliminates noisy edges • Makes edges smoother and thicker • Removes fine details
Suppression of non-maxima: • Choose the local maximum point along a perpendicular cross section of the edge.
Example: Suppression of non-maxima courtesy of G. Loy Non-maxima suppressed Original image Gradient magnitude
Example: Canny Edge Detection Using Matlab with default thresholds
Corner Detector • Compute x- and y-derivatives with a Gaussian filter • Form the orientation tensor M for every pixel • Compute the product of eigen-values, i.e., the determinant of M • If both eigenvalues large (product is a local maximum), then it is a corner!
Corners can be detected where the product of the ellipse axes are local maxima
Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Query
Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Query
Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Query
Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Matched? Query
Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Matched? Query
Baseline System – Bags of Words • Interest point detection (position, scale, orientation) - Differences of Gaussian/Harris
Baseline System – Bags of Words • Interest point detection - Differences of Gaussian/Harris • Feature extraction (feature vector e g R^128) - SIFT/SURF/DAISY
Baseline System – Bags of Words • Interest point detection - Differences of Gaussian/Harris • Feature extraction - SIFT/SURF/DAISY • Generating vocabularies – quantization - hierarchical k-means (Nister, Stewenius CVPR’06) - approximate k-means (Philbin et al. CVPR’08)