Image Analysis: Object Recognition
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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