Invariant surface characteristics for 3D object recognition in range images
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Pose Determination of a Three-Dimensional Object Using Triangle Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
A Spherical Representation for Recognition of Free-Form Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential invariants as the base of triangulated surface registration
Computer Vision and Image Understanding - Registration and fusion of range images
Harmonic shape images: a three-dimensional free-form surface representation and its applications in surface matching
Exploration trees on highly complex scenes: A new approach for 3D segmentation
Pattern Recognition
The correspondence framework for 3D surface matching algorithms
Computer Vision and Image Understanding
Directional histogram model for three-dimensional shape similarity
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
This paper analyzes a new 3D recognition method for occluded objects in complex scenes. The technique uses the Depth Gradient Image Based on Silhouette representation (DGI-BS) and settles the problem of identification-pose under occlusion and noise requirements. DGI-BS synthesizes both surface and contour information avoiding restrictions concerning the layout and visibility of the objects in the scene. Firstly, the paper is devoted to show the main properties of this method compared with a set of known techniques as well as to explain briefly the key concepts of the DGI-BS representation. Secondly, the performance of this strategy in real scenes under occlusion and noise circumstances is presented in detail.