IEEE Robotics & Automation Magazine - September 2017 - 133
Captured Frame
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
Recognition
Result
(a)
Captured Frame
Figure 8. The qualitative experimental results when a real scenario is considered. (a) and (f) A red book is correctly identified. (b) and
(g) A toy car is identified even while its color scheme is greatly similar to its background. (c) and (h) A generalized cylinder is correctly
identified. (d) and (i) Two objects are simultaneously identified. (e) and (j) Focus is maintained on the target object, the generalized
cylinder, even when several objects are added to the scene.
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Recognition
Result
(a)
Figure 9. The qualitative experimental results when (a)-(f) a real scenario is considered. (g)-(l) In these binary images, the detected
objects are highlighted, especially when they are partially occluded or if the object has a color scheme similar to its background.
to compare their performance with other state-of-the-art
approaches. However, these repositories could be classified
based on the goal to be satisfied. That is, object recognition
has multiple levels of semantics (e.g., category recognition,
instance recognition, pose recognition, etc.), it can refer to
different application scenarios, or it could be based on certain input data. Consequently, the required evaluation data
set must correspond to the needs of a particular approach.
This is why the RGB-D Object Data Set [81], publicly available at http://www.cs.washington.edu/rgbd-dataset, has
been used for this validation. This data set is composed of
thousands of images of 300 objects commonly found in
home and office environments, taken from multiple views
by using an RGB-D camera. Objects are organized into a
hierarchy of 51 categories composed of a number of instances between three and 14, so that each object belongs to only
one category. In addition, ground truth images are provided
to adequately assess the segmentation process. Consequently, this image data set allows object recognition techniques
to be evaluated at at two levels: the category level and the
instance level.
Despite the fact that the ability to recognize objects at both
levels is a key point in the context of robotic tasks, in this
work only the instance recognition is considered because no
category abstraction was carried out. The task for the recognition algorithm was to detect the exact physical instance of an
object that was previously presented. In our case, the previous
instance (i.e., the first frame of each object sequence) based
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