Difference between revisions of "Image processing"

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#redirect [[Image processing notes]]
 
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==Feature detection==
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Where features tend to include:
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* Points -
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* Blobs - smooth areas that won't (necessarily) be detected by point detection. Their approximate centers may also be considered interest points
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* Edges -
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** a relatively one-dimensional feature, though with a direction
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* Corners - Detects things like intersections and ends of sharp lines
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** a relatively two-dimensional kind of feature
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* Ridges
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In comparisons between similar images, one should keep in mind that blob-centers can become interest points, gradients can become edges, etc., and that the difference to detectors can be and/or should be fuzzy.
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* Interest point - could be said to group the above and more
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** preferably has a clear definition
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** has a well-defined position
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** preferably quite reproducible, that is, stable under relatively minor image alterations such as scale, rotation, translation, brightness.
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** useful in their direct image context - corners, endpoints, intersections
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* Region of interest
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See also:
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* http://en.wikipedia.org/wiki/Feature_detection_(computer_vision)
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* http://en.wikipedia.org/wiki/Corner_detection
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* http://en.wikipedia.org/wiki/Edge_detection
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* http://en.wikipedia.org/wiki/Blob_detection
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* http://en.wikipedia.org/wiki/Ridge_detection
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* http://en.wikipedia.org/wiki/Interest_point_detection
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* http://en.wikipedia.org/wiki/Region_of_interest
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<!--
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* http://eom.springer.de/E/e120040.htm
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-->
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===Edge detection===
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<!--
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In theory:
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* Step edges - sudden change in signal. (often not strictly immediate because of smapling. May be smoother)
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* impulse edges - change to a value and back - lines (of some thickness)
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* 'roof edge' - relatively soft, difference in direction/derivative
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Edge detector ideally
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* localization (often )
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* edge orientation
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* gives edge magnitude (lets us filter the most significant)
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In sampled raster images, there is always noise
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-->
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* Canny [http://en.wikipedia.org/wiki/Canny_edge_detector]
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* Canny-Deriche [http://en.wikipedia.org/wiki/Canny_edge_detector#Conclusion]
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* Differential [http://en.wikipedia.org/wiki/Edge_detection#Differential_edge_detection]
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* Sobel [http://en.wikipedia.org/wiki/Sobel_operator]
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* Prewitt [http://en.wikipedia.org/wiki/Prewitt]
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* Roberts Cross [http://en.wikipedia.org/wiki/Roberts_Cross]
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===Interest point / corner detection===
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* Harris operator [http://en.wikipedia.org/wiki/Corner_detection#The_Harris_.26_Stephens_.2F_Plessey_corner_detection_algorithm]
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* Shi and Tomasi [http://en.wikipedia.org/wiki/Corner_detection#The_Shi_and_Tomasi_corner_detection_algorithm]
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* Level curve curvature [http://en.wikipedia.org/wiki/Corner_detection#The_level_curve_curvature_approach]
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* SUSAN [http://en.wikipedia.org/wiki/Corner_detection#The_SUSAN_corner_detector]
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* FAST [http://en.wikipedia.org/wiki/Corner_detection#The_FAST_feature_detector]
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===Blob detection ===
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* Laplacian of Gaussian (LoG)
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* Difference of Gaussians (DoG)
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* Determinant of Hessian (DoH)
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* Maximally stable extremal regions
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* PCBR
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===Object detection===
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Tends to refer to detecting anything more complex than a point, edge, blob, or corner.
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Regularly by example.
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===Unsorted===
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Feature extraction:
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* SIFT (Scale-Invariant Feature Transform)
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** [http://en.wikipedia.org/wiki/Scale-invariant_feature_transform]
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* SURF (Speeded Up Robust Features)
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** faster than SIFT, performs similarly
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** http://en.wikipedia.org/wiki/SURF
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* GLOH (Gradient Location and Orientation Histogram)
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** http://en.wikipedia.org/wiki/GLOH
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* MSER (Maximally Stable Extremal Regions)
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** (primarily blob detection)
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** http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
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* LESH (Local Energy based Shape Histogram)
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** http://en.wikipedia.org/wiki/LESH
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* Scale-space
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** http://en.wikipedia.org/wiki/Scale-space
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Unsorted
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* Hough transform
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* Structure tensor
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* SPIN, RIFT (but SIFT usually works better{{verify}})
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Latest revision as of 12:53, 11 July 2013