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

FEATURE VECTORS

xxx…x

Feature Extraction: measurements or “features” are computed on each object identified during the segmentation step

Image Analysis: Object Recognition

1

2

3

n

x 1

x 2

x n

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.

Classification: each object is assigned to a class

FEATURE VECTORS

Image Analysis: Object Recognition

Classification

OBJECT TYPE

“WRENCH”

Image Analysis: Object Recognition

Image Segmentation

Feature Extraction

Classification

INPUT IMAGE OBJECT IMAGE

FEATURE VECTOR

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 objectcolor - 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)

Segmentation Algorithms:

- discontinuities between homogeneous regions

- similarity of pixel values within a region

Discontinuity based Segmentation:

detect points, lines and edges in an image

Discontinuity based Segmentation:

detect points, lines and edges in an image

-1 -1 -1-1 8 -1-1 -1 -1

Discontinuity based Segmentation:

detect points, lines and edges in an image

-1 -1 -1-1 8 -1-1 -1 -1

-1 -1 -1 2 2 2-1 -1 -1

-1 2 -1-1 2 -1-1 2 -1

-1 -1 2-1 2 -1 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 -1-1 8 -1-1 -1 -1

-1 -1 -1 2 2 2-1 -1 -1

-1 2 -1-1 2 -1-1 2 -1

-1 -1 2-1 2 -1 2 -1 -1

2 -1 -1-1 2 -1-1 -1 2

-1 -2 -1 0 0 0 1 2 1

-1 0 1-2 0 2-1 0 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:

GxGy

Gradient vector

Edge Linking - used to create connected boundaries

Discontinuity based Segmentation:

GxGy

Gradient vector

Edge Linking - used to create connected boundaries

- similar points within a neighborhood are linked

Discontinuity based Segmentation:

GxGy

Gradient vector

Edge Linking - used to create connected boundaries

- similar points within a neighborhood are linked

magnitude of gradient vector

[ Gx + Gy ]2 22

1

Discontinuity based Segmentation:

GxGy

Gradient vector

Edge Linking - used to create connected boundaries

- similar points within a neighborhood are linked

magnitude of gradient vector

[ Gx + Gy ]

approximated as | Gx | + | Gy |

2 22

1

Discontinuity based Segmentation:

GxGy

Gradient vector

Edge Linking - used to create connected boundaries

- similar points within a neighborhood are linked

magnitude of gradient vector

orientation of edges

ang(x,y) = tan ( )-1 Gy

Gx

Discontinuity based Segmentation:

GxGy

Gradient vector

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 0-1 4 -1 0 -1 0

Discontinuity based Segmentation:

Identify zero crossings

Discontinuity based Segmentation:

Identify zero crossings

Discontinuity based Segmentation:

Identify zero crossings

Discontinuity based Segmentation:

Identify zero crossings

Discontinuity based Segmentation:

Identify zero crossings

Similarity based Segmentation:

- Simple thresholding- Split and Merge- Recursive thresholding

Similarity based Segmentation:

- Simple thresholding- Split and Merge- Recursive thresholding

Single Level Thresholding

0, g < TH

G - 1, TH gT[g] =

Single Level Thresholding

0, g < TH

G - 1, TH gT[g] =

Single Level Thresholding

Single Level Thresholding

0, g < TH

G - 1, TH gT[g] =

Multiple Level Thresholding

0, g < TH1

G - 1, TH1 g <= TH2

0, g > TH2

T[g] =

Similarity based Segmentation:

- Simple thresholding- Split and Merge- Recursive thresholding

U

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 Rk) = 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