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'''In Image Segmentation:''' | '''In 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]. | * 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]. | ||
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| + | '''In Artifical Intelligence:''' | ||
| + | * [[Case-based Reasoning_Old Kiwi]] | ||
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The applications of support vector machines in various fields can be found here. | The applications of support vector machines in various fields can be found here. | ||
[http://www.clopinet.com/isabelle/Projects/SVM/applist.html] | [http://www.clopinet.com/isabelle/Projects/SVM/applist.html] | ||
Revision as of 20:25, 23 March 2008
This page can be used to discuss the applications of pattern recognition in our daily research! This would provide us an intuitive understanding of course topics. Please discuss "applied" pattern recognition here. Instead of just mentioning the field, please explain in detail how a specific tool of pattern recognition can be used in research.
From yamini.nimmagadda.1 Sun Feb 3 15:44:44 -0500 2008 From: yamini.nimmagadda.1 Date: Sun, 03 Feb 2008 15:44:44 -0500 Subject: Maximum Likelihood Estimate Message-ID: <20080203154444-0500@https://engineering.purdue.edu>
In Wireless Communications:
- If the input sequence messages are equally likely, Convolutional decoders like Viterbi minimizes the probability of error using the maximum likelihood estimate between the output sequence and all the possible input sequences.
In Image Processing:
- 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]
In Signal Processing:
- automatic speech recognition
- face recognition
In Face Reconition:
- 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_Old Kiwi
In 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].
In Artifical Intelligence:
The applications of support vector machines in various fields can be found here.
[6]
