ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision‘, Graz, 2002
contrast to the shape-based matching. By all means, it should be
pointed out that the computation time of PatMax® in this exam-
ple is much slower than in the example above. Thus, in this case
our new approaches are not only dramatically faster than the tra-
ditional methods but also considerably faster than PatMax®. The
reason for the totally different computation times of PatMax® in
the two example sequences is the automatic computation of the
coarse grain limit (see Section 3.2). During the first sequence us-
ing the shifted IC the grain limit was automatically set to 3.72 and
during the second sequence using the rotated IC the grain limit
was automatically set to only 2.92. There is no obvious reason
for this difference, because the object was the same in both cases.
Experiments have shown that the automatic computation of the
grain limit may result in a totally different value if the region of
interest is shifted by just 1 pixel without changing the number of
edge points within the region.
4 CONCLUSIONS
We presented an extensive performance evaluation of six object
recognition methods. For this purpose, the normalized cross cor-
relation and the Hausdorff distance as two standard similarity
measures in industrial applications were compared to PatMax®
— an object recognition tool developed by (Cognex, 2000) —
and two novel recognition methods that we have developed with
the aim to fulfill increasing industrial demands. Additionally, a
new method for refining the object’s pose based on a least-squares
adjustment was included in our analysis. We showed that our
new approaches have considerable advantages and are substan-
tially superior to the existing traditional methods especially in
the field of robustness and recognition time. Even in compari-
son to PatMax® particularly the shape based matching in com-
bination with the least-squares adjustment shows very good re-
sults. In contrast, we exposed some inconsistencies when using
PatMax®: restricting the parameter space to translations causes
the recognition rate to drop down dramatically. The automatic
setting of the grain limits is problematical since significantly dif-
ferent results are obtained using the same object and, although a
high value for the grain limits leads to a fast computation it also
results in a high risk of returning false positives. In contrast, a
low value means higher robustness but slow computation.
In most cases the shape-based matching approach and the modi-
fied Hough transform show equivalent behavior. The shape-based
matching approach should be preferred when dealing with in-
tense illumination changes and situations where it is important to
know the exact orientation of the object. In contrast, the modified
Hough transform is better suited when either the dimensionality
or the extension of the parameter space increases and the compu-
tation time is a critical factor.
The breakdown points of the normalized cross correlation are its
low robustness against occlusions/clutter and non-linear illumina-
tion changes. The relatively slow computation is another factor
that limits its applicability. As a breakdown point of the Haus-
dorff distance its trend to return false positives should be men-
tioned, which often occur in the presence of clutter.
Aside from these conclusions, it should be pointed out that some
of the results might change if we chose, for example, other im-
plementations of the approaches, other parameter constellations
or other image sequences. Therefore, our comparison is more of
a qualitative nature rather than of a quantitative one. Neverthe-
less, our results are very objective and help potential users to find
the optimum approach for their specific application. To facilitate
an extended comparison including other recognition methods, in-
terested parties are requested to send an e-mail to the authors in
order to get the sequences that we used for the evaluation.
A - 374
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