19-11-2012, 05:40 PM
Active Recognition through Next View Planning: A Survey
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Abstract
3-D object recognition involves using image-computable features to identify 3-D object. A
single view of a 3-D object may not contain sufficient features to recognize it unambiguously.
One needs to plan different views around the given object in order to recognize it.
Such a task involves an active sensor – one whose parameters (external and/or internal)
can be changed in a purposive manner. In this paper, we review two important applications
of an active sensor. We first survey important approaches to active 3-D object recognition.
Next, we review existing approaches towards another important application of an active
sensor namely, that of scene analysis and interpretation.
Introduction
3-D object recognition is the process of identifying 3-D objects from their images
by comparing image-based features, or image-computable representations with a
stored representation of the object. (For detailed surveys of 3-D object recognition
and related issues, see [1], [2]) Various factors affect the strategy used for recognition,
such as the type of the sensor, the viewing transformations, the type of object,
and the object representation scheme. Sensor output could be 3-D range images,
The Need for Multiple Views
Most model-based 3-D object recognition systems consider the problem of recognizing
objects from the image of a single view of an object ([1], [2], [3], [4]). Due
to the inherent loss of information in the 3-D to 2-D imaging process, one needs an
effective representation of properties (geometric, photometric, etc.) of objects from
images which are invariant to the view point, and should be computable from image
information. Invariants may be colour-based (e.g., [5]), photometric (e.g., [6])
or geometric (e.g., [3]).
Burns, Weiss and Riseman prove a theorem in [7] that geometric invariants cannot
be computed for a set of 3-D points in general position, from a single image. Invariants
can only be computed for a constrained set of 3-D points. One can impose
constraints on the nature of objects to compute invariants for recognition [8] – this
severely restricts the applicability of the recognition system to only specific classes
of objects e.g., canal surfaces [9], [10], rotational symmetry [8], [11], [3], repeated
structures (bilateral symmetry, translational repetition) [3], [12], [13]. While invariants
may be important for recognizing some views of an object, they cannot
characterize all its views – except in a few specific cases, as mentioned above. We
often need to recognize 3-D objects which because of their inherent asymmetry,
cannot be completely characterized by an invariant computed from a single view.
For example, certain self-occluded features of an object can become visible if we
change the viewpoint. In order to use multiple views for an object recognition task,
one needs to maintain a relationship between different views of an object.
Object Feature Detection
Object feature detection seeks to automatically determine vision sensor parameter
values for which particular features satisfy particular constraints when imaged.
These features belong to a known object in a known pose [27]. In addition to the
general survey on sensor planning, the authors lay specific emphasis on systems for
object feature detection systems. (A separate paper [28] presents the authors’ own
MVP system in detail.) A related topic is planning for complete sensor coverage of
3-D objects. A recent work in the area is that of Roberts and Marshall [29], who
present a viewpoint selection scheme for complete surface coverage of 3-D objects.
Some important earlier work in the area include those of Cowan and Kovesi [30],
Tarbox and Gottschlich [31] and Mason and Grun [32].
Active Object Recognition Systems
An active object recognition system uses multiple views of 3-D objects for recognition
in a purposive fashion. Based upon specialized representation scheme linking
multiple views of 3-D objects, different recognition schemes have been formulated
for active object recognition.