Three dimensional object recognition using invariants

The invariant used as an index has shown many advantages over the pose dependent methods in model-based object recognition. Although perspective and even weak perspective invariants do not exist for general three dimensional point sets from a single view invariants do exist for structured three dimensional point sets. However, such invariants are not easy to derive. The 3D invariant structure proposed by Rothwell (1993) requires seven points that lie on the vertices of a six-sided polyhedron and is applicable to position free objects. A new special structure for calculating invariants of three dimensional objects is developed by the authors (1995). In comparison, the proposed algorithm requires only six points on adjacent (virtual) planes that provides two sets of four coplanar points and does not require the position free condition. Hence it is applicable to a wider class of objects This paper is the extension of previous work to discuss how to use the projection to the base plane to obtain invariant conditions for the more general situation. The algorithm is demonstrated on images of real scenes.

Authors

• Yonggen Zhu
• Lakmal D. Seneviratne
• S. W. E. Earles

Keywords

• Object recognition
• Layout
• Objective Dimensions
• Object Classification
• Scene Images
• Basal Plane
• General Situation
• Set Of Dimensions
• Single View
• Adjacent Planes
• Imaging Data
• Proof Of Theorem
• Image Plane
• Intersection Point
• Image Point
• Object-oriented
• Visual Perspective
• Project Coordinator
• Projective Transformation
• Points In Plane
• Object Pose