Pyramid processor for building large-area, high-resolution image by parts

Claims

What is claimed is:

1. A pyramid-processing method for deriving, substantially without the introduction of spurious spatial-frequency image artifacts, the spatial-frequency spectrum analysis of a single high-resolution image extending over the entirety of a relatively-large-area two dimensional field of view and comprised of pixels having a given pixel density; said deriving being done from an array of relatively-small-area, high resolution sub-images each of which also has said given pixel density and extends over a mutually exclusive two dimensional portion of said two dimensional field of view; said method comprising the steps of:

(a) pyramid-processing said array to derive a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given positive integer, for each individual one of said sub-images, wherein Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level;

(b) pyramid-processing said array to derive a single Gaussian-output pyramid level G.sub.K for the entirety of said relatively large area, wherein said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of said Laplacian-output pyramid level L'.sub.K-1 ; and

(c) separately storing respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and respective pixels of said single Gaussian-output pyramid level G.sub.K.

2. The method defined in claim 1, wherein said sub-images are similar in size and are contiguous, and wherein each of said steps (a) and (b) of pyramid processing said array comprises the step of pyramid-processing in parallel all of said respective ones of said sub-images of said array.

3. A pyramid-processing method for deriving, substantially without the introduction of spurious spatial-frequency image artifacts, the spatial-frequency spectrum analysis of a single high-resolution image extending over the entirety of a relatively-large-area field of view and comprised of pixels having a given pixel density; said deriving being done from an array of relatively-small-area, high resolution sub-images disposed in abutting relationship with one another, each of which also has said given pixel density, said method comprising the steps of:

(a) pyramid-processing said array to derive a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given positive integer, for each individual one of said sub-images, wherein Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level, including separately pyramid-processing a given set of pixels associated with each individual one of said abutting sub-images of said array with multi-tap spatial filters, all of which filters exhibit a characteristic in each spatial dimension defined by a given kernel weighting function comprised of a certain odd plural number of coefficients symmetrically disposed about the centrally-located coefficient of said kernel weighting function, said given set of pixels associated with any individual one of said abutting sub-images including all the pixles situated within the boundary of that abutting sub-image plus a certain number of additional bordering pixels situated outside the boundary of that abutting sub-image which extend the integer portion of one-half said odd plural number of pixels beyond the boundaries of that abutting sub-image in each dimension thereof, said separate pyramid-processing resulting in the derivation of a separate set of Laplacian-output pyramid levels L.sub.0 . . . L.sub.K-1 for each individual one of said abutting sub-images, each of said separate sets having the same pixel density as its corresponding one of said L'.sub.0 . . . L'.sub.K-1 pyramid levels and including additional bordering Laplacian-output pyramid level pixels situated outside the boundary of that abutting sub-image which corresponds thereto; and

separating trimming said additional bordering pixels of each of said L.sub.0 . . . L.sub.K-1 Laplacian-output pyramid levels of the separate set for each individual one of said abutting sub-images to derive thereby said corresponding L'.sub.0 . . . L'.sub.K-1 Laplacian output pyramid levels for that abutting sub-image which corresponds thereto;

(b) pyramid-processing said array to derive a single Gaussian-output pyramid level G.sub.K for the entirety of said relatively large area, wherein said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of said Laplacian-output pyramid level L'.sub.K-1 ; and

(c) separately storing respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and respective pixels of said single Gaussian-output pyramid level G.sub.K.

4. The method defined in claim 3, wherein K is equal to two.

5. The method defined in claim 4, wherein said certain odd plural number is five.

6. A pyramid-processing method for deriving, substantially without the introduction of spurious spatial-frequency image artifacts, the spatial-frequency spectrum analysis of a single high-resolution image extending over the entirety of a relatively-large-area field of view and comprised of pixels having a given pixel density; said deriving being done from an array of relatively-small-area, high-resolution sub-images having respective boundaries and disposed in overlapping relationship each of which also has said given pixel density said method comprising the steps of:

(a) pyramid-processing said array to derive a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given positive integer, for each individual one of said sub-images, wherein Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level, including separately pyramid-processing a given set of pixels associated with each individual one of said overlapping sub-images of said array with multi-tap spatial filters, all of which filters exhibit a characteristic in each spatial dimension defined by a given kernel weighting function comprised of a certain odd plural number of coefficients symmetrically disposed about the centrally-located coefficient of said kernel weighting function, said given set of pixels associated with any individual one of said overlapping sub-images including all the pixels situated within the boundary of that overlapping sub-image plus a certain number of additional bordering pixels situated outside the boundary of that overlapping sub-image which extend the integer portion of one-half said odd plural number of pixels beyond the boundaries of that overlapping sub-image in each dimension thereof, said separate pyramid-processing resulting in the derivation of a separate set of Laplacian-output pyramid levels L.sub.0 . . . L.sub.K-1 for each individual one of said overlapping sub-images, each of said separate sets having the same pixel density as its corresponding one of said L'.sub.0 . . . L'.sub.K-1 pyramid levels and including additional bordering Laplacian-output pyramid level pixels situated outside the boundary of that overlapping sub-image which corresonds thereto; and

separately trimming said additional bordering pixels of each of said L.sub.0 . . . L.sub.K-1 Laplacian-output pyramid levels of the separate set for each individual one of said overlaping sub-images to derive thereby said corresponding L'.sub.0 . . . L'.sub.K-1 Laplacian output pyramid levels for that overlapping sub-image which corresponds thereto;

(b) pyramid-processing said array to derive a single Gaussian-output pyramid level G.sub.K for the entirety of said relatively large area, wherein said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of said Laplacian-output pyramid level L'.sub.K-1 ; and

(c) separately storing respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and respective pixels of said single Gaussian-output pyramid level G.sub.K.

7. The method defined in claim 6, wherein K is equal to two.

8. The method defined in claim 7, wherein said certain odd plural number is five.

9. A pyramid-processing method for deriving, substantially without the introduction of spurious spatial-frequency image artifacts, the spatial-frequency spectrum analysis of a single high-resolution image extending over the entirety of a relatively-large-area field of view and comprised of pixels having a given pixel density; said deriving being done from an array of relatively-small-area, high-resolution sub-images disposed in abutting relationship with one another each of which also has said given pixel density; said method comprising the steps of:

(a) storing the pixels of each of said high-resolution sub-images of said array in a first memory, whereby the pixels stored in said first memory include a limited first set of significant level-value pixels associated with each individual one of said high-resolution sub-images that are wholly within and define a sub-image domain of that individual one of said sub-images;

(b) pyramid-processing said array to derive a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given positive integer, for each individual one of said sub-images, wherein Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level;

(c) pyramid-processing said array to derive a single Gaussian-output pyramid level G.sub.K for the entirety of said relatively large area, wherein said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of said Laplacian-output pyramid level L'.sub.K-1, including reading out from said first memory and separately pyramid processing a given second set of stored significant level-value pixels associated with each individual one of said high-resolution sub-images with multi-tap spatial filters, all of which filters exhibit a characteristic in each spatial dimension defined by a given kernel weighting function comprised of a certain odd plural number of coefficients symmetrically disposed about the centrally-located coefficient of said kernel weighting function, said given second set of stored pixels associated with any individual one of said sub-images including all the stored pixels of said limited first set plus a certain number of bordering pixels which extend the integer portion of one-half said odd plural number of pixels beyond the boundaries of the sub-image domain of that individual one of said sub-images in each spatial dimension thereof, said separate pyramid processing resulting in the derivation of G.sub.K pixels for each individual one of said sub-images that includes certain significant level-value G.sub.K pixels situated wholly within the sub-image domain defined by that individual one of said sub-images; and

(d) separately storing respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and for each individual one of said sub-images, storing in a second memory only its derived certain significant level-value G.sub.K pixels.

10. The method defined in claim 9, wherein K is equal to two.

11. A pyramid-processing method for deriving, substantially without the introduction of spurious spatial-frequency image artifacts, the spatial-frequency spectrum analysis of a single high-resolution image extending over the entirety of a relatively-large-area field of view and comprised of pixels having a given pixel density; said deriving being done from an array of relatively-small-area, high-resolution sub-images each of which also has said given pixel density and extends over only a portion of said field of view; the portions of said field of view over which said sub-images respectively extend together comprising the entirety of said relatively large-area field of view; said method comprising the steps of:

(a) storing the pixels of each of said high-resolution sub-images of said array in a first memory, whereby the pixels stored in said first memory include a limited first set of significant level-value pixels associated with each individual one of said high-resolution sub-images that are wholly within and define a sub-image domain of that individual one of said sub-images, the sub-image domain of each individual one of said sub-images abutting the respective sub-image domains of those sub-images of said array that are situated next to that individual one of said sub-image;

(b) pyramid-processing said array to derive a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given positive integer, for each individual one of said sub-images, wherein Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level;

(c) pyramid-processing said array to derive a single Gaussian-output pyramid level G.sub.K for the entirety of said relatively large area, wherein said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of said Laplacian-output pyramid level L'.sub.K-1, including reading out from said first memory and separately pyramid processing all of the limited set of stored pixels defining the sub-image domain associated with each individual one of said high-resolution sub-images with multi-tap spatial filters, all of which filters exhibit a characteristic in each spatial dimension defined by a given kernel weighting function comprised of a certain odd plural number of coefficients symmetrically disposed about the centrally-located coefficient of said kernel weighting function, said separate pyramid processing resulting in the derivation of G.sub.K pixels for each individual one of said sub-images that includes both certain significant level-value G.sub.K pixels situated wholly within the particular sub-image domain of that individual one of said sub-images and additional significant level-value G.sub.K pixels extending into a sub-image domain that abuts that particular sub-image domain; and

(d) separately storing respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and for each individual one of said sub-images, the steps of (1) adding the level value of each additional G.sub.K pixel that extends into the particular sub-image domain thereof from a sub-image domain that abuts that particular sub-image domain to the level value of the corresponding certain G.sub.K pixel of that particular sub-image domain, thereby altering the respective level values of at least some of said certain G.sub.K pixels of that particular sub-image domain, and (2), following said alteration, separately storing in a second memory the respective level-values of all of the certain G.sub.K pixels of that particular sub-image domain.

12. The method defined in claim 1, further including the preliminary step of deriving from an array of television cameras having overlapping fields of view said array of small-areas, high-resolution sub-images, whereby the respective small-areas of adjacent ones of said television-derived high-resolution sub-images of said array overlap one another.

13. The method defined in claim 1, further including the preliminary step of separately storing the pixels of said respective high-resolution sub-images in a first memory, and performing each of steps a) and b) on pixels of each individual one of said high-resolution sub-images separately read out of said first memory; and wherein

step (c) comprises the step of storing the respective pixels of said single Gaussian output level G.sub.K in a second memory as a single G.sub.K image; and

said method comprises the further steps of (1) dividing the single G.sub.K image stored in said second memory into a second array of separate sub-images of G.sub.K pixels; (2) substituting the G.sub.K pixel sub-images of said second array for the high-resolution images of said first-mentioned array and performing the pyramid processing of each of steps (a) and (b) on said G.sub.K pixel sub-images thereby to derive a separate set of Laplacian-output pyramid levels L'.sub.K . . . L'.sub.K'-1 lower than a certain pyramid level K', where K' is a given positive integer greater than K, for each individual one of said sub-images of said second array and to derive a single Gaussian output pyramid level G.sub.K, for said entire single G.sub.K image stored in said second memory; and (3) separately storing in a third memory the respective pixels of each separate set of Laplacian-output pyramid levels L'.sub.K . . . L'.sub.K'-1 and the respective pixels of said single Gaussian output pyramid level G.sub. K'.

14. In a machine vision system comprised of utilization means responsive to the image content of a high-resolution television image of a given item of interest, said given item of interest having a two dimensional area that is too large to be viewed with high resolution by a single television camera; a combination for deriving the image content of a high-resolution television image of said given item of interest, substantially without the introduction of spurious spatial-frequency image artifacts, for use by said utilization means; said combination comprising;

a given plural number of separate television cameras arranged in an array, such that the field of view of each individual television camera in said television camera array thus formed partially overlaps the fields of view of those television cameras situated next to that individual television camera and such that the field of view of said television camera array as a whole views said two dimensional area of said given item of interest in its entirety and with high resolution, whereby said television camera array derives a corresponding array of overlapping high-resolution sub-images, each of said high-resolution sub-images including a mutually exclusive two dimensional portion of the image content of said two dimensional area of said given item, but all of said high-resolution sub-images together including all of the image content of said given item; and

pyramid-processing means responsive to said corresponding array of overlapping high-resolution sub-images for converting said array of sub-images to a spatial-frequency spectrum analysis of a single image defining the content of said given item with high resolution and substantially without the introduction of spurious spatial-frequency image artifacts, said spatial-frequency spectrum analysis of said single image being for use by said utilization means.

15. In a machine vision system comprised of utilization means responsive to the image content of a high-resolution television image of a given item of interest, said given item of interest having an area that is too large to be viewed with high resolution by a single television camera; a combination for deriving the image content of a high-resolution television image of said given item of interest, substantially without the introduction of spurious spatial-frequency image artifacts, for use by said utilization means; said combination comprising:

a given plural number of separate television cameras arranged in an array, such that the field of view of each individual television camera in said television camera array thus formed partially overlaps the fields of view of those television cameras situated next to that individual television camera and such that the field of view of said television camera array as a whole views said area of said given item of interest in its entirety and with high resolution, whereby said television camera array derives a corresponding array of overlapping high-resolution sub-images, each of said high-resolution sub-images including only a portion of the image content of said given item, but all of said high-resolution sub-images together including all of the image content of said given item; and

pyramid-processing means responsive to said corresponding array of overlapping high-resolution sub-images for converting said array of sub-images to a spatial-frequency spectrum analysis of a single image defining the content of said given item with high resolution and substantially without the introduction of spurious spatial-frequency image artifacts, said spatial-frequency spectrum analysis of said single image being for use by said utilization means; said pyramid-processing means including:

separate pyramid-analyzer modules equal in number to said given plural number, each of said modules being capable of pyramid processing pixels corresponding to a different one of said high-resolution sub-images into (K+1) pyramid levels comprised of Laplacian pyramid levels L'.sub.0 . . . L'.sub.K-1 pixels and Gaussian pyramid-level G.sub.K pixels for each of said different sub-images;

a camera-image pixel memory and camera memory output control coupled to said pyramid analyzer modules for separately storing the pixels of each individual one of said high-resolution sub-images in said camera-image pixel memory, under the control of said camera output control, and reading our certain stored high-resolution pixels belonging to an individual sub-image and applying said read out stored pixels as an input to that separate pyramid analyzer module that corresponds to that individual sub-image; and

a large-area (K+1) level pyramid-output pixel memory buffer coupled to said pyramid-analyzer modules separately storing separate sets of Laplacian pyramid-level L'.sub.0 . . . L'.sub.K-1 pixels derived, respectively, from each different one of said pyramid analyzer modules and separately storing a single set of G.sub.K pixels derived from all of said different ones of said modules.

16. The combination defined in claim 15, wherein said pixel memory and camera memory output control reads out in parallel the stored pixels of the different sub-images respectively applied as inputs to respective ones of said pyramid-analyzer modules.

17. The combination defined in claim 15, wherein K is equal to two.

18. The combination defined in claim 15, wherein each of said separate pyramid-analyzer modules comprises:

a K-stage pyramid having K stages and having certain stored readout pixels of the high-resolution sub-image corresponding to that separate pyramid-analyzer module applied as an input thereto, said K-stage pyramid deriving Laplacian pyramid-level L.sub.0 . . . L.sub.K-1 pixels and G.sub.K pixels;

an individual sub-image domain trimmer associated with each of said stages of said K-stage pyramid for trimming away some of the pixels from that one of the Laplacian pyramid level L.sub.0 . . . L.sub.K-1 pixels derived by that stage and thereby obtaining said L'.sub.0 . . . L'.sub.K-1 Laplacian pyramid level pixels as an output from each of said respective domain trimmers; and

a module-output distributor for selectively steering different groups of G.sub.K pixels derived from said K-stage pyramid to separate sub-image domain storage locations of said pyramid-output pixel memory buffer, one of said different groups of G.sub.K pixels corresponding to a sub-image domain associated with the high-resolution sub-image with which that pyramid-analyzer module corresponds, and each other of said different groups of G.sub.K pixels corresponding to a sub-image domain associated with a high-resolution sub-image that overlaps the high-resolution sub-image with which that pyramid-analyzer module corresponds.

19. A method for pyramid processing a single two dimensional large-area, high-resolution image having a given pixel density; said method comprising the steps of:

(a) dividing said single two dimensional large-area, high-resolution image into a plurality of contiguous two dimensional sub-images each of which has said given pixel density, and each sub image including a mutually exclusive portion of said single two dimensional large area high resolution image;

(b) pyramid-processing in parallel each of said contiguous sub-images to derive a respective set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K, where K is a given integer, and a respective Gaussian-output pyramid level G.sub.K wherein each Laplacian-output pyramid level L'.sub.0 has said given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level and each said Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of each said Laplacian-output pyramid level L'.sub.K-1 ; and;

(c) merging at least the Gaussian-output pyramid level G.sub.K of each of said respective sub-images into a single large area image.

BACKGROUND OF THE INVENTION

I. Field of the Invention

This invention relates to an image processing technique for seaming together a plurality of separate small-area high-resolution sub-images to provide a spatial frequency spectrum of a single large-area high-resolution image substantially without introducing image artifacts (e.g., defects at the sub-image seams of the single image). More particularly, the present invention is directed to such a technique making use of pyramid spatial-frequency spectrum analyzers.

II. Description of the Prior Art

Pyramid spectrum analyzers and synthesizers suitable for use in image processing are known in the art, and are described, by way of examples, in U.S. Pat. No. 4,447,886, issued May 8, 1984 to Meeker; U.S. Pat. No. 4,523,230, issued June 11, 1985 to Carlson, et al; and co-pending allowed U.S. Pat. No. 4,692,806, filed Apr. 4, 1984 by Carlson, et al, and assigned to the same assignee as the present application. This co-pending Carlson, et al. patent application, entitled "Real-Time Hierarchal Pyramid Signal Processing Apparatus," discloses pipeline architecture for performing the Burt Pyramid analyzer and/or synthesizer algorithm on a video signal defining successive television image frames. The Burt Pyramid is described in the article "The Laplacian Pyramid as a Compact Image Code," by Peter J. Burt, et al., IEEE Transactions on Communications, Vol. COM-31, No. 4, 532-540, April 1983.

One use of pyramid processing is in so-called machine vision, which is employed for such purposes as surveillance cameras, robotics, and automated "visual inspection" of manufactured items for defects. In this regard, reference is made to co-pending U.S. Pat. No. 4,692,806, filed Apr. 8, 1986 by Anderson, et al., and assigned to the same assignee as the present application. A programmed implementation of the image-data reduction technique disclosed in the co-pending Anderson, et al. application may be carried out by pyramid processor apparatus disclosed in co-pending U.S. Pat. No. 4,703,514, filed Sept. 16, 1985 by van der Wal and assigned to the same assignee as the present application.

In the past, the images processed by pyramid processors were usually derived from a single television camera. Any television camera is capable of viewing only a given limited field of view with a predetermined maximum resolution. If it is desired to view a larger field with this predetermined maximum resolution, it becomes necessary to employ an array comprised of a plurality of television cameras having overlapping fields of view and then "seam" together separate images, derived respectively from each of these television cameras of the array. Such seams, if noticeable, are image artifacts that degrade a large-area, high-resolution image comprised of a plurality of seamed small-area sub-images derived respectively from the separate television cameras of the array. In the case of machine vision, seam artifacts are a particular problem, since they are capable of producing analysis errors that cannot be tolerated. The pyramid processing technique of the present invention overcomes this problem.

SUMMARY OF THE INVENTION

The present invention is directed to a pyramid-processing technique for deriving, substantially without the introduction of spurious spatial-frequency image artifacts the spatial-frequency spectrum analysis of a single large-area, high-resolution image having a given pixel density from an array of small-area, high-resolution sub-images each of which also has said given pixel density. The pyramid processing technique of the present invention derives a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 lower than a certain pyramid level K (where K is a given positive integer) for each individual one of the sub-images. The Laplacian-output pyramid level L'.sub.0 has the given pixel density and each Laplacian output pyramid level above L'.sub.0 has a pixel density smaller than its immediately preceding Laplacian-output pyramid level. In addition to deriving a separate set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1, the pyramid-processing technique of the present invention also derives a single Gaussian-output pyramid level G.sub.K for the entire large area. The Gaussian-output pyramid level G.sub.K has a pixel density which is smaller than the pixel density of the Laplacian-output pyramid level L'.sub.K-1. The respective pixels of each individual set of Laplacian-output pyramid levels L'.sub.0 . . . L'.sub.K-1 and the respective pixels of the single Gaussian-output pyramid level G.sub.K are separately stored.

In addition, when desirable, stored pixels may be employed for either or both of two different purposes. The first purpose comprises deriving a Laplacian-output for the K pyramid level or higher, and/or a Gaussian-output for the (K+1) pyramid level or higher, from Gaussian-output pyramid level G.sub.K. This is accomplished either by repeating the pyramid processing technique of the present invention one or more times and/or by employing conventional pyramid analyzing techniques. The second purpose comprises deriving one or more Gaussian-output pyramid levels G.sub.K-1 . . . G.sub.0 from the stored pixels by employing conventional pyramid synthesizing techniques.

A desirable feature of the pyramid processing technique of the present invention is that it permits parallel processing to be employed in its implementation.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 diagrammatically illustrates a two-dimensional array of television cameras viewing a large-area item with high resolution;

FIG. 2 is a block diagram of a converter for converting multiple small-area sub-images from a television camera array to a single large-area image for use by utilization means;

FIG. 3 is an illustrative example of how contiguous small-area sub-image domains, which correspond respectively to television-camera overlap images of the array, comprise a single large-area image domain;

FIG. 3a diagramatically illustrates the effect of small positional errors in the placement of adjacent cameras of the array;

FIG. 3b diagramatically illustrates the basic premise taken by the present invention in combining the small-area sub-image domains of FIG. 3 to derive therefrom a single large-area image domain;

FIG. 4 is a block diagram of a first preferred embodiment of the present invention for performing a so-called partitioned-area approach to combining small-area sub-images;

FIG. 4a diagramatically illustrates a modification of FIG. 4 that provides a second preferred embodiment of the present invention for performing a so-called overlapped-area approach to combining small-area sub-images,

FIG. 5 illustrates a preferred embodiment of a typical camera module that may be employed in FIG. 4 or 4a, together with a block diagram of a camera memory output programmed control therefor;

FIGS. 6 and 6a respectively illustrate examples of the overlapped-area approach and the partitioned-area approach of the present invention;

FIG. 7 diagramatically illustrates sub-image trimming for the case of abutting sub-image domains that is usually employed in Laplacian-output derivation;

FIG. 7a diagramatically illustrates alternative sub-image trimming for the case of overlapping sub-image domains that is employed in Laplacian-output derivation in certain machine-vision applications; and

FIG. 8 is a block diagram illustrating additional pyramid processing that may be employed in the implementation of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the manufacture of such large-area items as printed-circuit boards, face plates and shadow masks for color-television cathode ray tubes, etc., it is necessary to visually inspect such items with high resolution for defects. Also, it is also desirable to analyze large-area images obtained from satellite or high-flying aircraft with high resolution. From the points of view of both speed and cost-saving, it would be desirable to utilize machine vision (such as that disclosed in the aforesaid Anderson et al. application) to automate such visual inspection. However, the limited field-of-view of a single television camera is insufficient to image simultaneously with high resolution (i.e. high pixel density) the entire area of such a large-area item. Therefore, as shown in FIG. 1 for illustrative purposes, it takes an array 100 of K.multidot.Y television cameras (where at least one of X and Y is a plural integer) to view simultaneously large-area item 102 with high resolution.

The respective television cameras of array 100 utilized in machine vision may be digital cameras or alternatively, may be analog cameras each of which has its output converted to digital form by an analog-to-digital (A/D) converter associated therewith. In any case, as shown in FIG. 2, television camera array 100 derives X.multidot.Y separate digital video outputs that are applied as respective inputs to multiple small-area sub-images to single large-area image converter 200. The converted single large-area image information is applied to utilization means 202, which may include means for comparing the large-area image information derived from converter 200 (which corresponds with the actual item then being viewed by the television camera array with high resolution) with stored image information pertaining to a corresponding item known to have no defects. Based on this comparison, utilization means 202 is programmed to make the decision as to whether or not the particular item then being viewed by the array of television cameras passes inspection.

For illustrative purposes, it is assumed in FIG. 3 that the respective values of each of X and Y is 3 (although, in practice, each of these values may be either smaller or larger than 3). As indicated in FIG. 3, a 3.multidot.3 array of television cameras provides 9 separate overlapped (OV) television-camera images 300-1,1 to 300-3,3. The field-of-view of the whole array of 9 television cameras includes rectangular (e.g. square) shaped, large-area image domain 302 (delineated by perimeter 306). The respective portions of large-area image domain 302 that are within the field-of-view of each individual one of the 9 television cameras are comprised of abutting, rectangular (e.g., square) shaped, small-area sub-image domains 304-1,1 to 304-3,3 (delineated by perimeter 306 and dashed seam lines 308).

As indicated in FIG. 3, each of the television-camera images 300-1,1 to 300-3,3 extends into those sub-image domains 304-1,1 to 304-3,3 that are adjacent to the particular sub-image domain with which that television camera image is individually associated. Further, those television-camera images associated with edge or corner sub-image domains, include portions exterior (EXT) to the perimeter 306 of large-area image domain 302. The problem is to combine the separate image information from each of the 9 overlapped images 300-1,1 to 300-3,3 in such a manner as to derive a single image comprised of all the image information within perimeter 306 of large-area image domain 302, and to accomplish this with negligible introduction of error. One type of error to be avoided (or at least minimized) is the introduction of artifacts at seam lines 308 of the small-area sub-image domains. Another type of error results from the fact that it is not