SSIP 2005 2
©Team
Delia Mitrea – Technical University of Cluj-Napoca, Romania
Sándor Szolyka – Budapest Tech, Hungary
Imre Hajagos – University of Szeged, Hungary
Szabolcs Berecz - Budapest Tech, Hungary
Gergely Grósz – University of Veszprém Georgikon Faculty of Agricultural, Hungary
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The Problem• Input: Landsat images of terrain, plus sample
images of fields, sea, forests or etc.
• Aim: Segmentation of scene based on texture and colour.
• Output: Label scene.
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The Solution
Solution 1. - Histogram matching I.Step 1. Decompose the image into small
cells. Step 2. Compute the histogram in the RGB
levels (All grid has three (red, green, blue) histograms.).
Step 3. Classification based on the correlation of histograms.
Step 4. Segment the image.
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The Solution
Solution 2. - Histogram matching II.Convert the histograms to a greyscale.
(Y=0,299 R+0,587 G+0,114 B)
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The Solution
Solution 3. – Markov Random Fields•Statistics based classifier algorithm.
•Uses spatial information.
•Driven by energy minimization.
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The SolutionSolution 4. - Texture-based recognition
Features used:
•Average edge frequency (density)
•Average edge contrast
•GLCM (Gray Level Cooccurrence Matrix) homogeneity
•GLCM (Gray Level Cooccurrence Matrix) entropy
•GLCM (Gray Level Cooccurrence Matrix) variance
•GLCM (Gray Level Cooccurrence Matrix) energy
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The Solution
Solution 4. - Texture-based recognitionStep 1. Learning
• Select a known region int the image (forest mountains or water)
• Compute GLCM features and edge-based features
• Store the feature vector in the training set for the corresponding class
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The SolutionSolution 4. - Texture-based
recognitionStep 2. Recognition
•Select an unknown area in the image in order to classify it: forest mountains or water
•Compute the GLCM features and the edge-based features
•Compare the feature vectors with the data int he training set: euclidean distance
•Use the k-nn method and decide the class
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References• M. Berthod, Z. Kato, S. Yu, J. Zerubia: Bayesian
imageclassification using Markov random fields. Image and Vision Computing,14(1996): 285-295, 1996.
• Z. Kato: Multi-scale Markovian Modelisation in Computer Vision withApplications to SPOT Image Segmentation. PhD thesis, INRIA SophiaAntipolis, France, 1994.
• Z. Kato, J. Zerubia and M. Berthod: Satellite image classification using amodified Metropolis dynamics Proc. IEEE International Conf. on Acoust., Speechand Sig. Proc., vol. 3, pp. 573-576, San Francisco, CA, March 23-26,1992.