Grouping image features into loops for monocular recognition

Model-based monocular vision has been used to recognize and locate 3-D objects by matching image corners (or lines) to model corners (or lines). These algorithms typically have high computational complexities. Grouping of visual features has been used to reduce the computational complexity. In this work point image features are grouped into loops and object loops that have similar viewpoint-invariant characteristics. Examples of viewpoint-invariant characteristics of loops are the number of lines and vertices, the number of convex and concave curves, and the sequence in which the lines, curves, and vertices are linked together. Grouping into loops also facilitates model matching by ellipses, in addition to corners and lines. Experiments are performed to extract loops from images and segmenting them into lines and elliptical curves. Distinguishable loops are used to find 3-D locations of the objects in scenes