TrackMate_: performance test of the new DoG detector.

Lesssons learned:
- You do need to compute the DoG image on FloatType. Otherwise the loss
  of precision seriously harms the results. This is why we cannot use
  directly the DogDetection class of Tobias.
- Also ran a few tests to assess multi-threading power: If we try to
  process a 512x512x10 over 100 frames, and process nTP frames
  simultaneously (1 thread per frame), here is the total time for the
  whole movie:
nThreads    time(s)
1           154.8340
2           78.5560
3           54.0030
4           40.7660
5           33.3190
6           28.9020
7           25.3300
8           22.0690
9           20.5570
10          18.9640
11          18.2150
12          17.8070
13          16.9900
14          16.9540
15          16.0790
16          16.2240
17          15.8810
18          15.3120
19          15.6810
20          14.7760
21          14.6230
22          14.6800
23          14.5240
24          14.9360
Note that I use HyperThreading, which may influence the saturation we observe
above 10 threads.

  If we now always use the 25 threads, but choose to allocate several threads
per frame (for instance to parallelize the Gauss3 calls), here is what we get:
simultaneous timepoints       threads per TP      time (s)
24                            1                   14.1920
12                            2                   13.4080
8                             3                   12.9400
6                             4                   12.7500
4                             6                   12.8320
3                             8                   13.2150
2                             12                  13.8700
1                             24                  17.1470

Signed-off-by: Jean-Yves Tinevez <jean-yves.tinevez@pasteur.fr>