(New page: '''Image Matching:''' * Maximum likelihood estimates can be used in image matching (edge template matching and gray-level image matching). This can be applied to stereo matching and feat...) |
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'''Image Matching:''' | '''Image Matching:''' | ||
| − | + | * Maximum likelihood estimates can be used in image matching (edge template matching and gray-level image matching). This can be applied to stereo matching and feature tracking. More about this topic can be found here ... [http://portal.acm.org/citation.cfm?id=568226] | |
| − | + | * Maximum likelihood can also be used in image reconstruction or restoration. Surprisingly, I found the usage of this in compression artifact removal also. See this paper [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=901102&isnumber=19490] | |
'''Face Recognition:''' | '''Face Recognition:''' | ||
| − | + | * Fisher Linear Discriminant (FLD) is widely used in face recognition. Here is a paper for reference: [http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel4/5726/15322/00711956.pdf]. Also variants of FLD are used for face recognition such as DiaFLD [http://linkinghub.elsevier.com/retrieve/pii/S0925231206000877]. It has been observed that FLD works better than Principal Component Analysis in classifying the facial features. | |
[Face detection vs Face recognition] | [Face detection vs Face recognition] | ||
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'''Image Segmentation:''' | '''Image Segmentation:''' | ||
| − | + | *Image Segmentation is performed by conventional graphical methods, but many a times, some pixels not belonging to the same object are classified into the same segment. Also, in images where a wide background is separated by a thin boundary line, image segmentation can be performed by obtaining features from FLD. I experimented this personally and found that the results are better than the conventional methods. This paper gives a starting point in doing this [http://ict.ewi.tudelft.nl/~duin/papers/asci_99_diabolo.pdf]. | |
Revision as of 14:47, 29 March 2008
Image Matching:
- Maximum likelihood estimates can be used in image matching (edge template matching and gray-level image matching). This can be applied to stereo matching and feature tracking. More about this topic can be found here ... [1]
- Maximum likelihood can also be used in image reconstruction or restoration. Surprisingly, I found the usage of this in compression artifact removal also. See this paper [2]
Face Recognition:
- Fisher Linear Discriminant (FLD) is widely used in face recognition. Here is a paper for reference: [3]. Also variants of FLD are used for face recognition such as DiaFLD [4]. It has been observed that FLD works better than Principal Component Analysis in classifying the facial features.
[Face detection vs Face recognition]
Image Segmentation:
- Image Segmentation is performed by conventional graphical methods, but many a times, some pixels not belonging to the same object are classified into the same segment. Also, in images where a wide background is separated by a thin boundary line, image segmentation can be performed by obtaining features from FLD. I experimented this personally and found that the results are better than the conventional methods. This paper gives a starting point in doing this [5].
