Method for identifying three-dimensional objects using two-dimensional images

A machine method for recognizing a three-dimensional object from a two-dimensional image of the object, using a Hough space representation of said image comprising clusters each repesented by its center point, comprising the steps of:

• (a) determining, in the Hough space representation of said object image, linear arrangements of cluster points, called cluster colinearities, each of said cluster colinearities representing a vertex in said object image;
• (b) preparing tables of such cluster colinearities indicating their interrelations, including common cluster points;
• (c) preparing tables representing specific cluster colinearity configurations identifying specific vertex point arrangements in said object image;
• (d) preparing a Hough space representation for each of a plurality of preselected object models;
• (e) determining cluster colinearities and preparing tables of cluster colinearities indicating their interrelations, for each of the preselected model Hough space representations, each of their cluster colinearities being related to a 3-D defined vertex point of the respective model;
• and
• (f) comparing the object colinearity tables prepared in step (c) to model colinearity tables prepared in step (e) for determining similar cluster colinearity configurations, thus relating vertex points of the object image to vertex points of at least one model.

A system for recognizing or identifying a three-dimensional object from a two-dimensional image of the object, using a Hough space representation of said image comprising clusters each represented by its center point, comprising:

• (a) means for determining in the Hough space representation of said object image, cluster configurations having specific properties;
• (b) means for storing predetermined cluster configurations of Hough space representations which correspond to known object models identifying vertex points in three-dimensional space;
• (c) means for comparing said cluster configurations determined by said determining means to said predetermined cluster configurations of Hough space representations stored in said storage means, in order to identify said object by comparison of the vertex points of said object image represented by said determined cluster configurations to corresponding vertex points of said object model representations;
• and
• (d) means, responsive to said comparing means, for identifying said object and, if further information is required for identification, for fitting vertex points of the object image, the locations of which are given by the slope and intercept of respective cluster point colinearities in Hough space representation, to vertex points of at least one object model, on the basis of the relations determined by said comparing means.

A system for recognizing a three-dimensional object from a two-dimensional image of the object, using a Hough space representation of said image comprising clusters each represented by its center point, comprising:

• (a) means for determining, in the Hough space representation of said object image, linear arrangements of cluster points, called cluster colinearities, each of said cluster colinearities representing a vertex in said object image;
• (b) first means for preparing tables of such clusters colinearities indicating their interrelations, including common cluster points;
• (c) second means for preparing tables representing specific cluster colinearity configurations identifying specific vertex point arrangements in said object image;
• (d) means for preparing a Hough space representation for each of a plurality of preselected object models;
• (e) third means for determining cluster colinearities and preparing tables of cluster colinearities indicating their interrelations, for each of the preselected model Hough space representations, each of their cluster colinearities being related to a 3-D defined vertex point of the respective model;
• and
• (f) means for comparing the object colinearity tables prepared by said second means to model colinearity tables prepared by said third means for determining similar cluster colinearity configurations, thus relating vertex points of the object image to vertex points of at least one model.