Method for recognizing a target component image within an image having multiple component images

• US 6,718,048 B1
• Filed: 08/12/2002
• Issued: 04/06/2004
• Est. Priority Date: 09/24/1999
• Status: Expired due to Fees

• Current Assignee: Cognex Technology & Investment Corporation (Cognex Corporation)
• Original Assignee: Cognex Technology & Investment Corporation (Cognex Corporation)
• Inventors: Kawata, Masayuki, Okuma, Kenji, Hasagawa, Hiroyuki
• Primary Examiner(s): Au, Amelia M.
• Assistant Examiner(s): LAROSE, COLIN M
• Application Number:
  US10/217,170
• Time in Patent Office: 603 Days
• Field of Search: 382/103, 382/170, 382/174, 382/181, 382/194, 382/216-219, 382/199, 382/209, 382/296, 382/203, 382/151, 348/169-172
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/30141 Printed circuit board [PCB]
• CPC Class Codes: G06T 7/564 from contours
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

A position detection tool in a machine vision system finds the position of target objects in a digital image using the length and width of the target object as parameters. The input length and width dimensions are measured by the developer as the length and width of a simple hypothetical rectangle that surrounds the object. For most shapes, the developer can easily obtain these two measurements. The position detection tool can easily be adapted to discern particular image patterns from multiple component images.

Claims

1. A method for recognizing a target component image from a larger image having a plurality of component images, the method comprising:

receiving a first linear dimension measurement and a second linear dimension measurement of a minimum enclosing rectangle of an object corresponding to the target component image;

segmenting the larger image into the plurality of component images;

calculating an error amount for each of the plurality of component images based on a calculation of two local maximum distances between edge pixels in each of the component images for each of a plurality of orientations of an orthogonal coordinate system defined by the first and second linear dimensions and comparing the calculated two local maximum distances to the first and second linear dimensions; and

recognizing the target component image as the component image with the smallest error amount.

2. The method of claim 1, further comprising performing edge detection of the component images to define an outlining area for each of the component images.

3. The method of claim 1, wherein recognizing the target component image as the component image with the smallest error amount additionally includes comparing the error amount with a predetermined error value and recognizing the target component image when the error amount is less than the predetermined value.

4. The method of claim 1, wherein calculating two local maximum distances between edge pixels further comprises determining distances between all pairs of edge pixels that lie along lines parallel to the direction of the linear dimensions for each of the plurality of orientations of the orthogonal coordinate system.

5. The method of claim 1, wherein the first and second linear dimensions define dimensions of a hypothetical minimum enclosing rectangle surrounding the object.