Method of and system for producing high-resolution 3-D images of 3-D object surfaces having arbitrary surface geometry

• US 6,962,289 B2
• Filed: 07/08/2002
• Issued: 11/08/2005
• Est. Priority Date: 06/07/1999
• Status: Expired due to Fees

• Current Assignee: Metrologic Instruments Incorporated (Honeywell International Inc.)
• Original Assignee: Metrologic Instruments Incorporated (Honeywell International Inc.)
• Inventors: Zhu, Xiaoxun, Knowles, C. Harry, Vatan, Pirooz, Tsikos, Constantine J.
• Primary Examiner(s): LE, THIEN MINH
• Application Number:
  US10/068,462
• Publication Number:
  US 20030053513A1
• Time in Patent Office: 1,219 Days
• Field of Search: 23546201-46245, 235/472.01, 235/472.02, 235/472.03, 235/454, 235/455, 235/470, 235/494
• US Class Current: 235/462.01
• CPC Class Codes: G02B 19/0014 at least one surface having optical power
• CPC Class Codes: G02B 19/0052 the light source comprising a laser diode coupling into light guides using intermediate optical elements G02B6/4204; semiconductor lasers having optical devices external to the laser cavity H01S5/005
• CPC Class Codes: G02B 19/0085 for use with both a detector and a source , e.g. in a transceiver, G02B19/009, G02B19/0095 take precedence
• CPC Class Codes: G02B 19/009 for use with infrared radiation
• CPC Class Codes: G02B 19/0095 for use with ultraviolet radiation
• CPC Class Codes: G02B 26/10 Scanning systems
• CPC Class Codes: G02B 27/095 Refractive optical elements
• CPC Class Codes: G02B 27/48 Laser speckle optics
• CPC Class Codes: G06K 7/10 by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
• CPC Class Codes: G06K 7/10594 Beam path
• CPC Class Codes: G06K 7/10722 Photodetector array or CCD scanning
• CPC Class Codes: G06K 7/10732 Light sources
• CPC Class Codes: G06K 7/146 the method including quality enhancement steps
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images
• CPC Class Codes: G06V 30/144 using a slot moved over the image; using discrete sensing elements at predetermined points; using automatic curve following means
• CPC Class Codes: H01S 5/005 Optical components external to the laser cavity, specially adapted therefor, e.g. for homogenisation or merging of the beams or for manipulating laser pulses, e.g. pulse shaping H01S5/026, H01S5/18388 take precedence
• CPC Class Codes: H01S 5/02325 Mechanically integrated components on mount members or optical micro-benches
• CPC Class Codes: H01S 5/4025 Array arrangements, e.g. constituted by discrete laser diodes or laser bar H01S5/42 takes precedence

Abstract

Methods of and systems for illuminating objects using planar laser illumination beams having substantially-planar spatial distribution characteristics that extend through the field of view (FOV) of image formation and detection modules employed in such systems. Each planar laser illumination beam is produced from a planar laser illumination beam array (PLIA) comprising an plurality of planar laser illumination modules (PLIMs). Each PLIM comprises a visible laser diode (VLD, a focusing lens, and a cylindrical optical element arranged therewith. The individual planar laser illumination beam components produced from each PLIM are optically combined to produce a composite substantially planar laser illumination beam having substantially uniform power density characteristics over the entire spatial extend thereof and thus the working range of the system. Preferably, each planar laser illumination beam component is focused so that the minimum beam width thereof occurs at a point or plane which is the farthest or maximum object distance at which the system is designed to acquire images, thereby compensating for decreases in the power density of the incident planar laser illumination beam due to the fact that the width of the planar laser illumination beam increases in length for increasing object distances away from the imaging optics. By virtue of the present invention, it is now possible to use both VLDs and high-speed CCD-type image detectors in conveyor, hand-held and hold-under type scanning applications alike, enjoying the advantages and benefits that each such technology has to offer, while avoiding the shortcomings and drawbacks hitherto associated therewith.

Claims

1. system for producing high-resolution 3-D images of 3-D object surfaces of arbitrary surface geometry moving relative to said system, said system comprises:

an object profiling subsystem for profiling a 3-D object surface of arbitrary surface geometry moving past said system, and producing a series of linear 3-D surface profile maps of said moving 3-D object surface as said 3-D object surface moves past said system,

wherein each said linear 3-D surface profile map comprises a set of 3-D coordinates specifying the location of sampled points along said moving 3-D object surface;

a linear imaging subsystem for producing a series of linear high-resolution 2-D images of said moving 3-D object surface as said 3-D object surface moves pest said system,

wherein each said linear high-resolution 2-D image comprises a set of pixel intensity values, and each said pixel intensity value being assigned set of two-dimensional coordinates specifying the location of the pixel in said linear high-resolution 2-D image; and

an image processing computer for constructing high-resolution 3-D images of said 3-D object surface using said linear 3-D surface profile maps and said high-resolution 2-D linear images of said moving object surface.

2. The system of claim 1, wherein said image processing computer further comprises:

(i) means for producing a 3-D surface geometry model of said moving 3-D object surface using said linear 3-D surface profile maps,

(ii) means for mathematically projecting pixel rays from each pixel in each said captured linear high-resolution 2-D image,

(iii) for computing means the x, y, z coordinates associated with the points of intersection between these pixel rays and said 3-D surface geometry model, and

(iv) means for generating a linear high-resolution 3-D image of said moving 3-D object surface based on said computed points of intersection,

whereby each pixel in said high-resolution linear 3-D image comprises an intensity value I(x, y, z) and a set of x,y,z coordinate values specifying the location of the sampled point of said moving 3-D object surface; and

(v) means for assembling, in an image buffer, a set of consecutively generated linear high-resolution 3-D images so as to construct an area-type high-resolution 3-D image of said moving 3-D object surface.

3. The system of claim 2, wherein said image processing computer further comprises:

(vi) at said image processing computer, mapping the intensity value I(x′

, y′

, z′

) of each C, pixel in said generated area-type 3-D image onto the x′

,y′

,z′

coordinates of points on a uniformly-spaced grid surface positioned along the optical axis of said linear imaging subsystem so as to model a 2-D planar substrate on which graphical forms of intelligence on said 3-D object surface might have been originally rendered; and

(vii) using an intensity weighing function based on the x′

, y′

, z′

coordinate values of each pixel in said area-type high-resolution 3-D image, so as to produce an area-type high-resolution 2-D image of said 2-D planar substrate surface bearing said forms of graphical intelligence.

4. The system of claim 3, which further comprises:

(viii) at said image processing computer, using an OCR algorithm to perform automated recognition of graphical forms of intelligence that might be possibly contained in said area-type high-resolution 2-D image of said 2-D planar substrate surface so as to recognize said graphical forms of intelligence and generate symbolic knowledge structures representative thereof.

5. A method of producing high-resolution 3-D images of moving 3-D object surfaces of arbitrary surface geometry, said method comprising the steps of:

(a) profiling a 3-D object surface of arbitrary surface geometry moving past an object profiling subsystem, and producing a series of linear 3-D surface profile maps of said moving 3-D object surface as said 3-D object surface moves past said subsystem,

wherein each said linear 3-D surface profile map comprises a set of 3-D coordinates specifying the location of sampled points along said moving 3-D object surface;

(b) producing a series of linear high-resolution 2-D images of said moving 3-D object surface as said 3-D object surface moves past a linear imaging subsystem,

wherein each said linear high-resolution 2-D image comprises a set of pixel intensity values, and each said pixel intensity value being assigned a set of two-dimensional coordinates specifying the location of the pixel in said linear high-resolution 2-D image; and

(c) constructing high-resolution 3-D image of said 3-D object surface using said linear 3-D surface profile maps and said high-resolution 2-D linear images of said moving object surface.

6. The method of claim 5, wherein step (c) further comprises:

(c1) producing a 3-D surface geometry model of said moving 3-D object surface using said linear 3-D surface profile maps,

(c2) means for mathematically projecting pixel rays from each pixel in each said captured linear high-resolution 2-D image,

(c3) means for computing the x, y, z coordinates associated with the points of intersection between these pixel rays and said 3-D surface geometry model, and

(c4) means for generating a linear high-resolution 3-D image of said moving 3-D object surface based on said computed points of intersection,

whereby each pixel in said high-resolution linear 3-D image comprises an intensity value I(x, y, z) and a set of x,y,z coordinate values specifying the location of the sampled point of said moving 3-D object surface; and

(c5) means for assembling, in an image buffer, a set of consecutively computed linear high-resolution 3-D images so as to construct an area-type high-resolution 3-D image of said moving 3-D object surface.

7. The method of claim 6, which further comprises:

(d) at said image processing computer, mapping the intensity value I(x′

, y′

, z′

) of each pixel in said computed area-type 3-D image onto the x′

,y′

,z′

coordinates of points on a uniformity-spaced grid surface positioned along to the optical axis of said linear imaging subsystem so as to model a 2-D planar substrate on which graphical forms of intelligence on said 3-D object surface might have been originally rendered; and

(e) using an intensity weighing function based on the x′

, y′

, z′

coordinate values of each pixel in said area-type high-resolution 3-D image, so as to produce an area-type high-resolution 2-D image of said 2-D planar substrate surface bearing said forms of graphical intelligence.

8. The method of claim 7, which further comprises:

(f) at said image processing computer, using said OCR algorithm to perform automated recognition of graphical forms of intelligence that might be possibly contained in said area-type surface so as to recognize said graphical high-resolution 2-D image of said 2-D planar substrate forms of intelligence and generate symbolic knowledge structures representative thereof.

9. A method of recognizing graphical intelligence recorded on planar substrates that have been physically distorted as a result of either (i) application of the graphical intelligence to an arbitrary 3-D object surface, or (ii) deformation of a 3-D object on which the graphical intelligence has been rendered.

10. The method of claim 9, which is capable of undisorting any distortions imparted to the graphical intelligence while being carried by the arbitrary 3-D object surface due to non-planar surface characteristics.

11. A method of recognizing graphical intelligence, originally formatted for application onto planar surfaces, but applied to non-planar surfaces or otherwise to substrates having surface characteristics which differ from the surface characteristics (or which the graphical intelligence was originally designed without spatial distortion.