Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Space and Time Bounds on Indexing 3D Models from 2D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
The non-existence of general-case view-invariants
Geometric invariance in computer vision
Matching 3-D Models to 2-D Images
International Journal of Computer Vision
Limitations of Non Model-Based Recognition Schemes
ECCV '92 Proceedings of the Second European Conference on Computer Vision
What can be seen in three dimensions with an uncalibrated stereo rig
ECCV '92 Proceedings of the Second European Conference on Computer Vision
On Geomatric and Algebraic Aspects of 3D Affine and Projective Structures from Perspective 2D Views
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Recognizing 3-D Objects Using 2-D Images
Recognizing 3-D Objects Using 2-D Images
Matching 3-D Models to 2-D Images
International Journal of Computer Vision
Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A system level implementation strategy and partitioning heuristic for LUT-based applications
Computers and Electrical Engineering
FormPad: a camera-assisted digital notepad
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Parametric manifold of an object under different viewing directions
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
| Hi-index | 0.14 |
Object recognition systems can be made more efficient through the use of table lookup to match features. The cost of this indexing process depends on the space required to represent groups of model features in such a lookup table. We determine the space required to perform indexing of arbitrary sets of 3-D model points for lookup from a single 2-D image formed under perspective projection. We show that in this case, one must use a 3-D surface to represent model groups, and we provide an analytic description of such a surface. This is in contrast to the cases of scaled-orthographic or affine projection, in which only a 2-D surface is required to represent a group of model features [3], [10]. This demonstrates a fundamental way in which the recognition of objects under perspective projection is more complex than is recognition under other projection models.