Image Segmentation Image Analysis Object Recognition Image Segmentation

Image Segmentation

Image Analysis: Object Recognition Image Segmentation INPUT IMAGE OBJECT IMAGE Image Segmentation: each object in the image is identified and isolated from the rest of the image

Image Analysis: Object Recognition Feature Extraction OBJECT IMAGE x 1 x 2 FEATURE VECTORS x … 3 xn Feature Extraction: measurements or “features” are computed on each object identified during the segmentation step

xn x 2 x 1 The feature vector for a given pixel consists of the corresponding pixels from each feature image; the feature vector for an object would be computed from pixels comprising the object, from each feature image.

Image Analysis: Object Recognition FEATURE VECTORS Classification OBJECT TYPE “WRENCH” Classification: each object is assigned to a class

Image Analysis: Object Recognition Image Segmentation INPUT IMAGE OBJECT IMAGE Feature Extraction FEATURE VECTOR Classification OBJECT TYPE “WRENCH”

Example: an automated fruit sorting system

Example: an automated fruit sorting system segmentation: identify the fruit objects the image is partitioned to isolate individual fruit objects

Example: an automated fruit sorting system segmentation: identify the fruit objects feature extraction: compute a size and color feature for each segmented region in the image size - diameter of each object color - red-to-green brightness ratio (redness measure)

Example: an automated fruit sorting system segmentation: identify the fruit objects feature extraction: compute a size and color feature for each segmented region in the image classification: partition the “fruit” objects in feature space

Automatic (unsupervised) image Segementation : difficult problem 1) attempt to control imaging conditions (industrial applications) 2) choose sensor which enhance objects of interest (infared imaging)

Two Types of Segmentation Algorithms: - Identify discontinuities between homogeneous regions - Identify similarity of pixel values within a region

Discontinuity based Segmentation Algorithms: Identify the boundaries between differing regions in the image. Two popular techniques use: - Spatial filters, gradients, edge linking - Identification of zero-crossings, thresholding

Discontinuity based Segmentation: detect points, lines and edges in an image

Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 8 -1 -1

Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 8 -1 -1 2 2 2 -1 -1 2 -1 -1 -1 2 -1 -1 -1 2

Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 8 -1 -1 2 2 2 -1 -1 2 -1 -1 0 1 -2 0 2 -1 0 1 -1 -1 2 -1 -1 -1 2 -1 -2 -1 0 0 0 1 2 1

Discontinuity based Segmentation: detect points, lines and edges in an image -1 -2 -1 0 0 0 1 2 1 -1 0 1 -2 0 2 -1 0 1 Gx Gy

Discontinuity based Segmentation: detect points, lines and edges in an image -1 -2 -1 0 0 0 1 2 1 -1 0 1 -2 0 2 -1 0 1 Gx Gy

Discontinuity based Segmentation: Gradient vector Gx Gy Edge Linking - used to create connected boundaries

Discontinuity based Segmentation: Gradient vector Gx Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked

Discontinuity based Segmentation: Gradient vector Gx Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector [ Gx 2 + Gy 2 ] 1 2

Discontinuity based Segmentation: Gradient vector Gx Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector [ Gx 2 + Gy 2 ] 1 2 approximated as | Gx | + | Gy |

Discontinuity based Segmentation: Gx Gy Gradient vector Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector orientation of edges -1 ang(x, y) = tan ( Gy ) Gx

Discontinuity based Segmentation: Gradient vector Gx Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector orientation of edges

Discontinuity based Segmentation: Identify zero crossings

Discontinuity based Segmentation: Identify zero crossings 0 -1 4 -1 0 Laplacian Filter

Discontinuity based Segmentation: Identify zero crossings 0 0 -1 0 0 0 -1 -2 -1 0 -1 -2 16 -2 0 0 -1 -2 -1 0 0 0 -1 0 0 Laplacian Of a Gaussian

Discontinuity based Segmentation: Identify zero crossings Original image Lo. G

Discontinuity based Segmentation: Identify zero crossings Original image Thresholded Lo. G Outline of Thresholded Lo. G

Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

Single Level Thresholding T[g] = 0, g < TH G - 1, TH # g

Single Level Thresholding T[g] = 0, g < TH G - 1, TH <= g

Single Level Thresholding

Single Level Thresholding T[g] = 0, g < TH G - 1, TH <= g

Multiple Level Thresholding T[g] = 0, g < TH 1 G - 1, TH 1 <= g <= TH 2 0, g > TH 2

Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

Split and Merge 1) split region into four disjoint quadrants if P(Rj) = FALSE 2) merge any adjacent regions Rj and Rk if P(Rj URk) = TRUE 3) stop when no splitting or merging is possible

Split and Merge

Split and Merge

Split and Merge

Split and Merge

Split and Merge

Split and Merge

Split and Merge

Split and Merge