Visualization and Perception Laboratory Visualization and Perception Laboratory
Department of Computer Science and Engg., IIT Madras, Chennai – 600036, India.
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2 2 - - D TEXTURE SEGMENTATION D TEXTURE SEGMENTATION
Flow chart of the method of texture
classification
Filtering
Non-linearity
Smoothing
Normalizing non-linearity
Classifier Filter responses
Local energy function
Local energy estimates
Feature vectors
Input image
Segmented map
Image with five Texture regions
Segmentation result using:
Gabor + DWT, FCM
VP Lab, CSE, IITM
Results (Cont.)
I1 I2 I3 I4 I5
Segmented maps (DWT+DCT)
Input images
Results (Cont.)
I6 I7 I8 I9 I10
Input images
Segmented maps (DWT+DCT)
Filtering
Smoothing using 2-D asymmetric Gaussian filter
Self-Organizing feature Map (SOM)
Smoothing using 2-D symmetric Gaussian filter
Edge detection using Canny operator
Stages of processing for texture edge detection.
Edge map
Edge Linking
Input image Input image Edge map
Experimental results of texture edge detection.
Texture Edge Detection
Results
Input image Edge map Input image Edge map Input image Edge map
Integrating Region and Edge
Information for Texture Segmentation
We have used a modified constraint satisfaction neural networks termed as Constraint Satisfaction Neural Network for Complementary Information Integration (CSNN-CII), which integrates the region and edge based approaches.
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Input Image
Segmented map before integration (Ref: [Rao 2004])
Edge map before integration
(Ref: [Gupta 2006])
Segmented map and Edge map after integration
Results
Input Image
Segmented map before integration
Edge map before integration
Segmented map and Edge map after integration
Results
Results
Input Image
Segmented map before integration
Edge map before integration
Segmented map
and Edge map
after integration
Results (Cont.)
Input Image
Segmented map before integration (Ref: [Shivani 04])
Segmented map and Edge map after integration Segmented map
after integration as
given in [Munoz 02]
Scene Classification
Test image Segmentation using DWT features
Feature detection from segments
Modified PNN Output
class
Training samples
Classification of indoor vs. outdoor scenes using texture, color and shape features and probabilistic neural networks
Indoor image
Outdoor
image
Examples of correctly classified
indoor images
Examples of correctly classified
outdoor images
Examples of indoor images misclassified
as outdoor images
Examples of outdoor images misclassified
as indoor images
Results
83.1 89.6
Color + Texture + Shape
90.8 94.0
Color + Texture
83.8 90.4
Shape + Texture
71.5 89.2
Shape + Color
86.9 94.0
Texture
53.5 94.0
Color
66.5 63.5
Shape
Outdoor Indoor
Feature set
INDOOR VS. OUTDOOR
CLASSIFICATION ACCURACY (%) ON IITM-SCID WITHOUT INTEGRATING REGION AND EDGE INFROMATION
83.8 89.7
Color + Texture + Shape
92.3 94.3
Color + Texture
84.8 89.5
Shape + Texture
Outdoor Indoor
Feature set
INDOOR VS. OUTDOOR
CLASSIFICATION ACCURACY (%) ON IITM-SCID BY INTEGRATING REGION AND EDGE INFROMATION
Ref: [lalit06b]
