- Low-level image processing Image enhancement, restoration, transformation

Transcription

1 () Representation and Description - Low-level image processing enhancement, restoration, transformation Enhancement Enhanced Restoration/ Transformation Restored/ Transformed - Mid-level image processing (image understanding) Object representation, description Restored/ Transformed Segmentation Segmented Object Representation/ Description Representation/ Description/ Features -

2 - High-level image processing (recognition and interpretation) Object recognition, interpretation of object relationships Representation/ Description/ Features Object recognition Objects Interpretation Meaning/ Relationships (a) Chain code - Chain codes: represent a boundary by a connected sequence of straight-line segments of specified length and direction Choose an appropriate grid to approximate objects -

3 - Chain code (clockwise): 4-direction: 0000, 8-direction: direction: 4-direction:

4 Problem : different starting points result in different chain codes * Solution: normalization redefine the starting point such that the chain code forms a smallest number * E.g.: Problem : object rotation results in different chain codes * Solution: difference code coding with the difference of directions (counter-clockwise) * E.g.: (normalization) (b) Polygonal approximation - Polygonal approximation: approximate a boundary using a polygon - Minimum perimeter polygons Choose an appropriate grid The boundary is enclosed by a set of concatenated cells Allow the boundary to shrink as a rubber band -4

5 - Merging techniques. Merge points along a boundary until the least square error line fit of the points merged so far exceeds a threshold. Record the the two end point of the line. Repeat Steps and until all boundary points are processed -5

6 - Splitting techniques Subdivide a boundary segment successively into two parts until a specified criterion is satisfied. Find two points on the boundary that are farthest away and draw a line Splitting line divides the boundary into two boundary segments. For each boundary segment, find a point on the boundary that has a -6

7 maximum perpendicular distance to its corresponding line. Draw two lines joining the point and the two end points, respectively, of the corresponding splitting line Subdivide the boundary segment into two parts 4. Repeat Steps ~4 until the perpendicular distance is less than a threshold a c b -7

8 (c) Signature - Signature: a D functional representation of a boundary Plot the distance from the centroid to the boundary as a function of angles: signature = r(θ), θ = 0 ~ π (d) Boundary descriptors - Descriptions of boundary Perimeter: length of the chain code -8

9 Diameter: max i,j (D(p i, p j )) p i Major axis (M a ) and minor axis (m a ) p j M a = max i,j (D(p i, p j )) m a M a and forms a basic rectangle m a M a Basic rectangle Eccentricity: M a /m a -9

10 - MATLAB p = bwperim(bw, conn): find perimeter pixels bw: binary image conn: 4 or 8 (connectivity) Perimeter pixels: -valued pixels that are connected to at least one 0-valued pixels p: returned perimeter binary image - MATLAB S = diameter(l): find descriptions of a boundary L: a labeled image S: a structure with the following fields * S.Diameter, S.MajorAxis, S.MinorAxis, S.BasicRectangle (e) Regional descriptors - Compactness: perimeter /area - Topological: Euler number, E = C H (number of connected -0

11 component number of holes) E = = E = = 0 E = = - MATLAB D = regionprops(l, properties) L: labeled image Properties: 'Area', 'BoundingBox', 'Centroid', 'ConvexArea', 'ConvexHull', 'Convex', 'Eccentricity', EquivDiameter', 'EulerNumber', 'Extent', 'Extreme', FilledArea', 'Filled', '', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'PixelList', 'Solidity', 'all' D: a structure with the fields specified when invoking regionprops( ) * E.g., D.Area -

12 - Texture Statisticcal approaches L * Intensity mean: = m z ( ) i= 0 i p zi, * n th L moment about the mean: µ = n i= 0 ( z i m) n p( z i ) Mean Moment Expression Discription L = z p( z ) Standard deviation i= 0 m Measure of average intensity σ = µ ( z) = σ i i z Measure of average contrast Smoothness R = ( + σ ) Measure of smoothness of intensity L Third moment µ = ( z ) ( ) i= 0 i m p zi Measure of skewness of the histogram; 0: symmetric, positive: skewed to the right; negative: skewed to the left L Uniformity = U p ( z ) i= 0 i Measure of uniformity; maximum: all graylevels are equal Entropy L p( z ) log = 0 ( ) i i p zi e = Measure of randomness -

13 Sprectral approache: based on Fourier spectrum Structural approach: structure of the texture primitives Texture primitives: (f) High-level image processing - High-level image processing (computer vision) Recognition and interpretation Object recognition: identify objects based on object models Interpretation: using artificial intelligence to infer the following -

14 * Properties of objects: identity, size, material, D/D position, orientation * Relationships among objects: occlusion, relative position Further inferrence: planning of path, operations, control - Applications: robotics, industrial automatic inspection, autonomous navigation, document image analysis, bioinformation recognition -4