Shape classes should emphasize shape differences

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Unformatted text preview: ame classes. Shape classes should emphasize shape differences among classes, while the shape variations within classes should not be reflected in the shape class description. • • Region identification Region identification assigns unique labels to image regions If non-repeating ordered numerical labels are used, the largest integer label gives the number of regions in the image. Faculty of Engineering Robotics Technology MECH 4041 29 B.Eng (Hons.) Mechatronics S. Venkannah • • • • • • • • • • Mechanical and Production Engineering Department Contour based shape descriptors Chain codes describe an object by a sequence of unit size line segments with a given orientation, called Freeman’s code Simple geometric border representations are based on geometric properties of described regions, e.g. o Boundary length o Curvature o Bending energy o Signature o Chord distribution Fourier shape descriptors can be applied to closed curves, co-ordinates of which can be treated as periodic signals. Shape can be represented as a sequence of segments with specified properties. If the segment type is known for all segments, the boundary can be described as a chain of segment types, a code word consisting of representatives of a type of alphabet. B-splines are piecewise polynomial curves whose shape is closely related to their control polygon- a chain of vertices giving a polygonal representation of a curve. B-splines of third order are most common, representing the lowest order which includes the change of curvature. Shape invariants represent properties of geometric configurations that remain unchanged under an appropriate class of transforms; machine vision is especially concerned with the class of projective transforms. Region based shape descriptors. Simple geometric region descriptors use geometric properties of described regions: o Area o Euler’s number o Projections o Height, width o Eccentricity o Elongatedness o Rectangularity o Direction o Compactness Statistical moments interpret a normalized gray-level image function as a pro...
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