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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 - PowerPoint PPT Presentation
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Edge detection
• Goal: Identify sudden changes (discontinuities) in an image
• This is where most information in an image is coded
• Example: line drawings
What causes an edge?
• Depth discontinuity
• Surface orientation discontinuity
• Changes in surface properties
• Light discontinuities (e.g. shadows)
ScaleIncreased 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
Original image Gradient magnitudeNon-maxima suppressed
Differentiation with a Gaussian filter
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!
Gradient directions
Blow-up of gradient directions
Corners can be detected where the product of the ellipse axes are local maxima
Fast (bottom-up) - some methods scale
Fast (bottom-up) - some methods scale
Fast (bottom-up) - some don’t
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
mat
ch
Query
Task: Image Retrieval• Oxford Building Data (Philbin et al. CVPR’07)
Good
mat
ch
Matched?
Query
Task: Image Retrieval• Oxford Building Data (Philbin et al. CVPR’07)
Good
mat
ch
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)
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