<p>A common preprocessing tactic is to smooth the image prior to feature extraction using for instance a Gaussian kernel or a low pass filter. This allows for the high frequency spurious details to be removed.</p>

CC BY-SA 3.0
2013-07-25T17:57:34.460Z
2013-07-25T17:57:34.460Z
Phil
<p>I have a set of images (e.g. cars) and trying to perform feature detection using SIFT (actually, SIFT modification - A-SIFT, which is more robust to affine transformations) and I'm getting an extremely low results in matching even on the same models, same color and closely same view point.<br>
I'm barely sure, that the problem is car's surface is glossy (that's leads to huge variation of reflections on surface) and shadows &amp; lighting (that's depends on location where photo was taken). Is there's image processing algorithms that can help me clean up images and/or decrease influence of that factors?
I had a quick look at CiteSeerX and Google Scholar but found nothing.</p>

CC BY-SA 3.0
2011-07-16T14:41:05.070Z
The question is probably more suitable for MetaOptimize or Stats.SE.
Kaveh
CC BY-SA 3.0
2011-07-16T16:31:21.000Z
I'm not quite sure about MetaOptimize and Stats - question isn't about noticeably large amounts of data and complicated decisions inherent to machine learning. At least not directly. Nor it refer to _theoretical_ computer science, but I've seen here a couple computer vision questions so I've placed it here.
om-nom-nom
CC BY-SA 3.0
2011-07-16T17:56:19.353Z
I'm afraid this isn't quite the right place for this question (which I personally find somewhat interesting).
Suresh Venkat
CC BY-SA 3.0
2011-07-16T22:34:47.083Z
IIRC, CV is in the scope of MetaOptimize.
Kaveh
CC BY-SA 3.0
2011-07-17T01:06:25.293Z
@kaveh, @suresh-venkat, I'll try to ask my question at more general thematic [so].
om-nom-nom
CC BY-SA 3.0
2011-07-17T02:25:42.310Z
There is [a proposal for CV](http://area51.stackexchange.com/proposals/11036/computer-vision) on area 51, but it still needs more commits.
Kaveh
CC BY-SA 3.0
2013-08-02T15:55:22.793Z
wikipedia on [SIFT, scale invariant feature transform](http://en.wikipedia.org/wiki/Scale-invariant_feature_transform)
vzn
CC BY-SA 3.0
2013-08-02T15:57:19.320Z
it would be helpful to clarify & add more details what "feature detection" you are attempting.
vzn
<image-processing><cv.computer-vision>
Cleaning images in order to perform feature detection
om-nom-nom
# Cleaning images in order to perform feature detection

(Asked by: username_0 on 2011-07-16)

I have a set of images (e.g. cars) and trying to perform feature detection using SIFT (actually, SIFT modification - A-SIFT, which is more robust to affine transformations) and I'm getting an extremely low results in matching even on the same models, same color and closely same view point.  
I'm barely sure, that the problem is car's surface is glossy (that's leads to huge variation of reflections on surface) and shadows & lighting (that's depends on location where photo was taken). Is there's image processing algorithms that can help me clean up images and/or decrease influence of that factors?
I had a quick look at CiteSeerX and Google Scholar but found nothing.

**Tags:** image-processing, cv.computer-vision

> **Comments:**
> **username_1**: The question is probably more suitable for MetaOptimize or Stats.SE.  
> **username_0**: I'm not quite sure about MetaOptimize and Stats - question isn't about noticeably large amounts of data and complicated decisions inherent to machine learning. At least not directly. Nor it refer to _theoretical_ computer science, but I've seen here a couple computer vision questions so I've placed it here.  
> **username_2**: I'm afraid this isn't quite the right place for this question (which I personally find somewhat interesting).  
> **username_1**: IIRC, CV is in the scope of MetaOptimize.  
> **username_0**: @kaveh, @suresh-venkat, I'll try to ask my question at more general thematic [so].  
> **username_1**: There is [a proposal for CV](http://area51.stackexchange.com/proposals/11036/computer-vision) on area 51, but it still needs more commits.  
> **username_3**: wikipedia on [SIFT, scale invariant feature transform](http://en.wikipedia.org/wiki/Scale-invariant_feature_transform)  
> **username_3**: it would be helpful to clarify & add more details what "feature detection" you are attempting.  

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## 1 answers

### Answer by username_4 on 2013-07-25. Score: 2

A common preprocessing tactic is to smooth the image prior to feature extraction using for instance a Gaussian kernel or a low pass filter. This allows for the high frequency spurious details to be removed.


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