Feature based geometric match stability issue

Question

I am trying to use Feature and  Edge based pattern matching. 
With both methods I get the same problem of lack  of consistency in the match results.
In one image the score is ie 990 and in the next frame there is no recognition even if the threshold of score is set to zero.
The difference between the images is marginal. They were taken 16.7 ms apart with close to a stationary object. 
I do not get any errors, the occlusion is set to 1-20, rotation -70 70, and scale factor 90-110
LV 2012 Pro. WinXp 2.4Ghz quad. New drivers.
I attached three images: the tempate and the two cases of found and not foun pattern. 
Can someone suggest a reason for that? I was using regular pattern matching so far and and I am not used to weird results like that.
Attachments:
FTemplate.png ‏26 KB
Found1.png ‏91 KB
Not Found.png ‏91 KB

Answer 1

Andrew, 
I appreciate your response. Please see mine.
Because of the nature of visions algorithms, their application is often more of an art than a science
I heard it dozens times Yet I claim it is science.
We did notice there was a lot of noise in the images
As you can see these are low resolution X-ray images and that combined with a tiny bit of motion makes it what they are.
I can see a couple of features that are slightly distorted between the two
X-ray images by its nature, are projections of 3D objects and certain amount of distortion is normal. This is when the part of algorithm responsible for occlusion, scaling, rotation and the right choice of minimum score kicks in. The issue here is no minimum score that can force the recognition of the second image that is almost identical.
...so we think some filtering may be useful
Possibly. However, I am not going to use filters until I know why this or that methods works (or doesn't) with close to perfect conditions. The two images are the ones of the best I get. To my surprise it sometimes works with images that are far worse. Filtering or not does not explain the behavior of the software.
As far as the template, we could try different regions of the implant
Not quite sure what you mean by it. I can use the bottom or the top. The top is a very complicated 3d object with drastically changing projections= failure. Choosing smaller regions will only create a flood of false negatives. Yet I still do not know what is wrong with the current one. 
Another thing to try could be multiple templates
I use that method with pattern template matches (intensity based). I try to choose two  patterns that represent the same feature but are fairly distinct from each other in order to span the variety of conditions and projections. Yeah, it is a sort of art Yet I never choose two templates that are almost identical because its just does not make sense. 
As I mentioned, I am fairly familiar with LV intensity based methods and not that familiar with LV edge/feature matching. I was hoping to find out if maybe I am missing something about the method. For example, I need to set up certain parameter higher/lower and there are quite q few to set up in the edge/feature matching. From the math point of view it does not make sense that a small perturbation of an input parameter changes the solution drastically. It means that the solution is pretty much wrong. Even though, assuming that the solution falls far away from the true value and if you are willing to accept big errors, you should get at least  "an" answer.  
Here I take the minimum score all the way down to zero and the algorithm finds no similarity at all, null, zilch, which can only mean two things I am doing something very wrong or there is something wrong with the algorithm software. If you take two random images you can probably find a 1% or less similarity but not finding it at all is kind of uncommon.
Is there something more I could provide to investigate it? I am not looking for alternatives but suggestions why this method with my implementation fails so miserably. 
With all due respect, the suggestions provided are so general (and also good but not what I asked for) in its nature that are probably in the textbook of the machine vision technical support under (applies to all methods)