Geometric hashing method for model-based recognition of an object

• US 20060126943A1
• Filed: 02/03/2006
• Published: 06/15/2006
• Est. Priority Date: 06/05/2001
• Status: Active Grant

• Current Assignee: Matrox Electronic Systems Ltd.
• Original Assignee: Matrox Electronic Systems Ltd.
• Inventors: Simon, Christian, Meddah, Djamel
• Granted Patent: US 7,327,888 B2
• Time in Patent Office: Days
• US Class Current: 382/217
• CPC Class Codes: G06V 10/753 Transform-based matching, e.g. Hough transform
• CPC Class Codes: G06V 10/757 Matching configurations of points or features

Abstract

A method of pattern matching for recognition of objects within an image, uses a model defined by a set of one or more model primitives representative of large scale structures of an archetype. The image is analyzed to derive at least one target primitive representative of a large scale structure of the image. A basis is derived from at least one of the target primitives, and each of the target primitives represented as invariants in the derived basis. For each target primitive, any model primitives that at least partially match the target primitive are identified. Each identified model primitive is then processed to calculate a figure of merit indicative of a degree of correspondence between the target primitive and the model primitive. In some embodiments, the figure of merit is an Orthogonal projection between the target and model primitives, which is and accumulated for each model basis.

Claims

1. A method of pattern matching for recognition of objects within an image using a model defined by a set of one or more model primitives representative of large scale structures of an archetype, the method comprising steps of:

deriving at least one target primitive representative of a large scale structure of the image; and

for each target primitive;

identifying any model primitives that at least partially match the target primitive; and

for each identified model primitive, calculating a figure of merit indicative of a degree of correspondence between the target primitive and the model primitive.

2. A method as claimed in claim 1, wherein the step of deriving at least one target primitive comprises steps of:

detecting geometric features within the image;

analyzing the detected geometric features to find large scale structures of the image;

approximating each large scale structure with at least one respective primitive;

deriving a basis from at least one of the primitives; and

representing each of the primitives as invariants in the derived basis.

3. A method as claimed in claim 2, wherein the step of approximating each large scale structure comprises a step of mapping a respective primitive through at least a sub-set of the geometric features forming the large scale structure.

4. A method as claimed in claim 3, wherein each primitive comprises either one of:

a straight line-segment and a curve segment.

5. A method as claimed in claim 4, wherein each curve segment primitive is defined by any one of:

an elliptic function;

a quadratic function;

a cubic function; and

a spline function.

6. A method as claimed in claim 2, wherein the step of deriving a basis comprises the steps of:

calculating an origin of a respective local coordinate system and calculating an orientation of the respective local coordinate system.

7. A method as claimed in claim 6, wherein the step of deriving a basis comprises the steps of:

deriving a plurality of bases and selecting at least one of the plurality of derived bases.

8. A method as claimed in claim 7, wherein the step of selecting at least one of the plurality of derived bases comprises any one or more of:

selecting bases derived using relatively large primitives;

selecting at most two bases derived using any one primitive;

selecting bases having respective origins distributed substantially evenly across the image; and

selecting bases in respect of which the orientation of the respective coordinate system is parallel to at least one primitive used in the derivation of the respective basis.

9. A method as claimed in claim 6, wherein the step of calculating the orientation of the respective local coordinate system comprises steps of:

selecting one of the primitives used to calculate the origin; and

setting the orientation of the coordinate system based on an orientation of the selected primitive.

10. A method as claimed in claim 9, wherein the step of calculating the orientation of the respective local coordinate system comprises a step of calculating an average orientation of the primitives used to calculate the origin.

11. A method as claimed in claim 10, wherein the orientation of each primitive is weighted by a length of the respective primitive.

12. A method as claimed in claim 2, wherein the step of representing each of the primitives as invariants in the derived basis comprises a step of calculating parameters of each primitive relative to a respective local coordinate system of the derived basis.

13. A method of deriving a model for use in a pattern matching method in accordance with claim 1, the method comprising the steps of:

deriving at least one model primitive representative of large scale structures of an archetype;

sampling each model primitive at two or more respective sample locations;

mapping each sample location to a respective sample bin of a hash table;

and inserting a reference to the respective model primitive in the sample bin.

14. A method as claimed in claim 13, wherein each model primitive is sampled with a granularity selected on the basis of desired recognition accuracy and speed.