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IMAGE ANALYSIS AND PATTERN RECOGNITION Introduction Feature extraction:

Digital image processing Chapter 8 Image analysis and pattern recognition. IMAGE ANALYSIS AND PATTERN RECOGNITION Introduction Feature extraction: - spatial feature extraction - transform feature extraction

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IMAGE ANALYSIS AND PATTERN RECOGNITION Introduction Feature extraction:

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  1. Digital image processing Chapter 8 Image analysis and pattern recognition • IMAGE ANALYSIS AND PATTERN RECOGNITION • Introduction • Feature extraction: • - spatial feature extraction • - transform feature extraction • - edge feature extraction; edge detection • Objects representation by their boundaries: • - contour extraction • - contour descriptors   •   Objects representation by their regions: • - region extraction • - region representation • Shapes and structures for region-based object representation: • - object skeletons • - binary morphology • - shape descriptors (numerical shape descriptors) • Textures; texture analysis • Image segmentation • Grey level based segmentation/color based segmentation • Connected components analysis • Contour-based segmentation • Region-based segmentation   • Mixed techniques

  2. Digital image processing Chapter 8 Image analysis and pattern recognition Introduction Fig. 8.1 Image analysis system – block diagram Example image analysis tasks: License plate recognition

  3. Text detection & recognition or removal Trafic sign recognition Calcite localization/assessment Detect tissue on the slide Digital image processing Chapter 8 Image analysis and pattern recognition Introduction Example image analysis tasks:

  4. Digital image processing Chapter 8 Image analysis and pattern recognition • Introduction • Example image analysis tasks: • Ways to approach the image analysis task? •  - “straight forward” approach: analyze the content of the whole image => one will eventually find the entire information, including the information of interest – at too high computational complexity/cost • - “smart” approach: focus strictly on the information you’re looking for!and extract only this information from the image  focus on the part of the image containing the information of interest. • How? • describe somehow the main characteristics of the information of interest •  describe the discriminative features of the region containing the information you look for, which are not present in the other regions => represent the image in the best feature space, by a feature map Single object images; defect detection on the object

  5. Digital image processing Chapter 8 Image analysis and pattern recognition • E.g. on the images with apples – only interested in the defect area (if there is), and for this defect – it appears darker grey than the normal apple coloration, but not black => design a grey scale slicing to set to black everything else • Analyze only the information in the region of interest , using the previously defined feature space/feature map • e.g. Apples sort: no defects (defect area <2% of the apple); average defects; large defects… New feature space

  6. Tissue identification • by color coding • (e.g. violet) • Can measure afterwards the area, • describe the shape, • etc. Digital image processing Chapter 8 Image analysis and pattern recognition • Feature extraction: • Spatial features extraction: • Amplitude features: e.g. the brightness levels can identify regions of interest in the image: • Amplitude features may be discriminative enough if intensity is enough to distinguish wanted info from the rest of the scene • => defining the best parameters of the transformation for feature extraction – most difficult • => amplitude feature space representation is not necessarily binary; just that unwanted parts of the scenes should be represented uniquely (i.e. black) in the feature space • => sometimes adaptive thresholding/adaptive grey scale slicing is needed.

  7. Region of interest (ROI) Measurements ROI histogram Tissue of interest is well discriminated from the microscopic slide by the standard deviation of the local histogram Digital image processing Chapter 8 Image analysis and pattern recognition • Histogram based features: • Local histogram = a local statistical description of the image; • If u = an image pixel; x=a grey level => pu(x)=the probability of appearance of the grey level x in the image region = a value in the normalized histogram • => One can compute: the standard deviation; the entropy; the median; percentiles, of pu(x).

  8. Digital image processing Chapter 8 Image analysis and pattern recognition Transform features extraction Fig.8.2 Transform features extraction

  9. Digital image processing Chapter 8 Image analysis and pattern recognition Edge features extraction. Edge detection Fig. 8.3 Edge detection with gradient operators (8.5) (8.6)

  10. Digital image processing Chapter 8 Image analysis and pattern recognition Fig. 8.4 Edge detection by compass operators a b c d Fig. 8.5 Compass edge detectors (North direction) N NW W SW S SE E NE Fig. 8.6 Compass operators on different directions

  11. Digital image processing Chapter 8 Image analysis and pattern recognition gk(m,n) –compass gradient on the direction k0,...,7, The gradient in the spatial position (m,n) is defined as: (8.7) Laplace operators and the zero-crossings edge localization method: (8.8) Fig. 8.7 Edge detection by Laplace operators (the 1-D case) The Laplacian:

  12. Digital image processing Chapter 8 Image analysis and pattern recognition The Laplacian of Gaussian operator (LoG) The 2nd derivative (Laplacian of Gaussian) Gaussian filter The derivative of the Gaussian Discrete implementations:

  13. Digital image processing Chapter 8 Image analysis and pattern recognition Edge detection by different operators – comparison: Original image Sobel edge detection LoG edge detection; sigma=5 Roberts edge detection LoG edge detection; Sigma=10

  14. y s s θ Φ x Digital image processing Chapter 8 Image analysis and pattern recognition Objects representation by their boundaries: Contour extraction: Fig. 8.8 4-connectivity; 8-connectivity The Hough transform:

  15. s s s Approx. convergence => approx. straight line Curve convergence on horizontal axis=> Straight line through the origin No convergence point => The points are not on a straight line Digital image processing Chapter 8 Image analysis and pattern recognition The Hough transform of lines/random curves:

  16. Edge detection (gradient operator) + thresholding => Binary edge map Apply Hough transform: Extracting the contour of the triangle Input image Lower left object = Defined by the 3 line intersection points Edge map; Contours not yet extracted/ not yet labeled 8-connectivity: Correct labeling Contour extraction in 8-connectivity 4-connectivity: The contour is “broken” during labeling Digital image processing Chapter 8 Image analysis and pattern recognition Applications to contour extraction formed by line segments:

  17. Digital image processing Chapter 8 Image analysis and pattern recognition • Contour representation/contour descriptors: • Goal: for a given object, described by its contour, find a compact description, by numerical attributes, able to: • Represent the contour with no significant loss of information (regenerative descriptors) • -Generate (by a subset of attributes) descriptions of the contour/shape invariant to: scaling; rotation; translation; mirroring; projection distortions; small (limited) variations of shape (among different individual representations of the same shape) • Using the contour descriptors, one can recognize the shape by template matching or shape classification (contour descriptors classification) • General assumption: single object contour (all edge pixels connected); 1-pixel width!

  18. Start point Encode this contour by a chain code with 8 directions, Consider origin= the upper left point; contour following direction = arbitrary; the only possible positions are on the grid corners Contour approximation (“quantization”) on a rectangular grid Original contour Digital image processing Chapter 8 Image analysis and pattern recognition Chain codes and polygonal approximations: Fig. 8.9 Contour representation by chain codes Fig. 8.10 Polygonal approximation of the contours

  19. Digital image processing Chapter 8 Image analysis and pattern recognition Fourier descriptors: (8.12) (8.13) (8.14) (8.15) (8.16) leading to a new contour x'(n) y'(n), given by: (8.17) where (8.18) Table 8.1

  20. Digital image processing Chapter 8 Image analysis and pattern recognition Shape reconstruction from its Fourier descriptors: Original Reconstruction using the first 2 descriptors only Reconstruction using the first 6 descriptors … Reconstruction using the first 10 descriptors Reconstruction using the first 20 descriptors

  21. Model shape Macthing results: d<Thd Shapes dictionary Digital image processing Chapter 8 Image analysis and pattern recognition , then for a given translation u0, the distance d is minimal when: and where a(k)b*(k)c(k)ejk,  -2n0/N and c(k) is a real valued term.

  22. Digital image processing Chapter 8 Image analysis and pattern recognition Objects representation by their regions: Region extraction: same as for contours (use connected components analysis, in 4- or 8- connectivity)Region representation: Fig. 8.11 Quad-tree region representation => the quad-tree code : gbgbwwbwgbwwgbwwb ; decoding: g(bg(bwwb)wg(bwwg(bwwb)))

  23. Digital image processing Chapter 8 Image analysis and pattern recognition • ·Regions skeletons; medial axis transforms: • 8.23) • (m,n) : uk(m,n) uk(i,j) ,  (m,n;i,j) 1(8.24) • Fig. 8.12 Skeleton extraction Fig. 8.13 Examples skeletons • Fig. 8.14 2-pass contour thinning:2 logic rules: Fig. 8.15 Thinning result • R1: P1==1 && N(P1)==1 && 2<=NT(P1)<=6 && P2·P4·P6==0 && P2·P4·P8==0 • R2: P1==1 && N(P1)==1 && 2<=NT(P1)<=6 && P2·P6·P8==0 && P4·P6·P8==0 Shapes and structures for region-based object representation:

  24. Digital image processing Chapter 8 Image analysis and pattern recognition • ·Morphological processing. Binary image morphology Fig. 8.16 Algoritmi de transformare morfologica

  25. Digital image processing Chapter 8 Image analysis and pattern recognition • ·Syntactical representation • Fig. 8.17 Syntactical representation of an object

  26. Digital image processing Chapter 8 Image analysis and pattern recognition • Shape descriptors • Applications: shape recognition; quantitative measurements • Def.: The shape of an object = the profile of the object + its physical structure => “shape descriptors” • Classification: • (1) regenerative descriptors (contours; regions; high order statistics; structural and syntactic descriptors) • (2) geometrical shape descriptors (area, perimeter, max-min radii, eccentricity, corners, roundness, symmetry) • (3) moments

  27. y dy y(t) y(t+1) x(t) x(t+1) dx x Rmin Rmax Digital image processing Chapter 8 Image analysis and pattern recognition • Geometrical features (geometrical descriptors): • ·Perimeter: • t – some contour parameter • - Discrete => T = count of contour pixels • ·Area: • where: R and R – the object region and the object contour • - Discrete => A = count of pixels inside the object region • ·Min-max radii, Rmin and Rmax – the minimum and maximum distances from the center of mass of the object region to its contour (the Rmax/ Rmin ratio – gives a measure of the eccentricity or elongation of the object)

  28. compact necompact Compact Non-compact Digital image processing Chapter 8 Image analysis and pattern recognition • ·Roundness or compactness: • For a disc -  is minimum, =1. • ·    Symmetry: 2 types of shape symmetry: rotational and mirror • Moment-based features: • ·    Center of mass: • ·    The(p,q) order central moments: • ·    Orientation = the angle of the axis of the smallest moment of inertia • – found by minimizing: • with respect to :

  29. Artificial textures Natural textures Digital image processing Chapter 8 Image analysis and pattern recognition • Textures • ·The texture = the periodic repetition of some basic structures in an image area; the basic image structure is called texel • Texture analysis methods: statistical classification; structural classification • ·       Statistical classification techniques: • ·The auto-correlation function (ACF): the spatial dimensions of the texels are proportional to the width of the auto-correlation functions:: • where • ·  Several measures used to evaluate the distribution of the ACF to describe the texture:

  30. ACF for “Sand” ACF for “Wool” Digital image processing Chapter 8 Image analysis and pattern recognition Image transforms based approaches: (8.41) Fig. 8.18 Various masks in the frequency domain used for texture analysis

  31. Sobel Sobel Sand Raffia R=4, f=0 Grass Digital image processing Chapter 8 Image analysis and pattern recognition  The edge density – as texture classification feature: Histogram features for texture analysis: => the co-occurrence histogram: => various features can be extracted from the co-occurrence histogram: - Inertia: - The mean of the distribution: N0 – the total number of possible orientations. - The variance of the distribution: - The spread of the distribution:

  32. Digital image processing Chapter 8 Image analysis and pattern recognition Random texture models (8.47) Fig. 8.19 Texture analysis model

  33. 1) Local maxima detection Object 2) Local minima detection Objects are found between the local minima Digital image processing Chapter 8 Image analysis and pattern recognition Image segmentation Amplitude thresholding/ grey level window slicing Component labeling Pixel labeling. Region growing

  34. Digital image processing Chapter 8 Image analysis and pattern recognition Boundary-based image segmentation Fig. 8.23 Segmentation algorithm based on boundary detection Region-based segmentation; segmentation based on regions and boundaries Fig. 8.24 Region merging

  35. Digital image processing Chapter 8 Image analysis and pattern recognition Fig. 8.25 Segmentation by split and merge algorithm: a. input b. region splitting by quad-trees c. segmented regions

  36. a) Original image b) Region-based image segmentation c) Main objects detected, marked on the original image by their contour Digital image processing Chapter 8 Image analysis and pattern recognition

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