METHOD AND APPARATUS FOR LOCATING AND IDENTIFYING AN OBJECT OF INTEREST IN A COMPLEX IMAGE

Abstract: A method of location and identification of an object of interest in a complex image frame involves defining the object of interest as a reference template. The ranks of grey shades in the complex image frame and reference template are computed (17). The rank of grey shades in the reference template are compared with the ranks of grey shades of the complex image (18), thereby determining closeness of fit. A threshold for detection is set by utilizing selected decision data (19).

Description: METHOD AND APPARATUS FOR LOCATING AND IDENTIFYING AN OBJECT OF INTEREST IN A COMPLEX IMAGE The present invention relates to a method and apparatus for locating and identifying an object of interest in a complex image. More particularly, the invention relates to the use of rank correlation to achieve such location and identification. Many techniques for detecting objects in complex imagery have been developed throughout the years. Such techniques are computationally complex, requiring multiplications at relatively high accuracy. This has forced digital processors to employ complex algorithms, resulting in vastly increased hardware implementation and, therefore, costs. Classically, registration techniques have been 5used for target detection, but these techniques have limitations. Moving target indication (MTI) and temporal differencing techniques are not useful for stationary, slow-moving, or radially-inbound targets, and can produce false alarms if the revisit time is long with respect to the scene stability. For targets of the aforementioned type, frame addition may be used, but there is a potential problem of background clutter components not being completely canceled, thus creating residues that act like real target detections, that is, 5 false alarms. None of the described techniques provides identification. Conventional correlation is a fundamental mathematical process for a wide variety of image processing functions, including 1) image Q registration, 2) background classification, 3) scene matching and 4) target detection. Although well 5 defined, the direct application of digital correlation approaches is thwarted by the enormous magnitude of mathematical operations required for implementation, especially multiplications. For example, the correlation of two one-dimensional input signals, x(n) and y(n), which are defined for the interval 0 < n < N-1 (and 0 outside the interval), is given as (L.R. Rabiner, "Correlation Method for Power Spectrum Estimation," IEEE Press, Programs for Digital Signal Processing, 1979) l" " ' " " R *y (m)a N Σ tx ( n ) -3?)ly(ιmn)-yj 0≤τn<L-l n«0 where x and y are the estimated means of x(n) and y(n), <"> and L equals the number of correlations lags of interest. Outside the interval 0 < m < L-l the functions are assumed to be zero. The present invention is directed to a method and apparatus for locating and identifying an object of interest in a complex image, which method and apparatus are more simple than those of the prior art and exhibit superior performance than the prior art. The present invention further provides a method and apparatus for locating and identifying an object of interest in a complex image, which method and apparatus are independent of contrast variations in the 1image. Still further, the present invention provides a method and apparatus for locating and identifying an object of interest in a complex image, which method and 5apparatus are highly immune to false detection and provide an improved constant false alarm rate (CFAR) due to their being distribution-independent. The present invention further provides a method and apparatus for locating and identifying an lOobject of interest in a complex image, which method and apparatus utilize simpler mathematical operations than those of the prior art and require no multiplications. Still further, the present invention provides a method and apparatus for locating and identifying an 1 object of interest in a complex image, which method and apparatus provide a correlation output having a well- defined normal distribution. The present invention is further directed to a method and apparatus for locating and identifying an 20object of interest in a complex image, which method and apparatus are independent of the level of grey scale encoding of the image. In addition, the present invention provides a method and apparatus for locating and identifying an 25 object of interest in a complex image, which method and apparatus are relatively insensitive to background clutter and, in fact, functions best in the presence of significant random background noise. Still further, the present invention provides 30 a method and apparatus for locating and identifying an object of interest in a complex image, which method and 35 apparatus are free from problems, such as, for example, lscene stability associated with image registration techniques, since said method and apparatus are performed on individual frozen frames. 5 Still further, the present invention provides a method and apparatus for locating and identifying an object of interest in a complex image, which method and apparatus do not depend on object movement. The present invention further provides a lOmethod and apparatus for locating and identifying an object of interest in a complex image, which method and apparatus involve a ranking process which reduces the image representation in amplitude by up to 50 to 1. Still further, the present invention provides 15a method and apparatus for location and identifying an object of interest in a complex image, which method and apparatus have great utility and may be used on any image such as, for example, radar, visible, infra-red, X-ray or ultraviolet, regardless of its source or 20 spectral band. The present invention also provides a method and apparatus for locating and identifying an object of interest in a complex image, which method and apparatus are completely compatible with model-based processing 5 and are thus usable in an extremely wide range of image applications. The present inventor still further provides a method and apparatus for locating and identifying an object of interest in a complex image, which method and 30 apparatus require minimized hardware implementation and 35 function in compatibility with vector, parallel and Ineuronal network processors. In accordance with the invention, a method of location and identification of an object of interest in a complex image comprises the steps of defining the 5object of interest as a reference template and computing the ranks of grey shades in both the complex image and the reference template. The ranks of grey shades in the reference template are compared with the ranks of grey shades of the complex image, thereby determining lOcloseness of fit. A threshold for detection is set by utilizing selected decision data. The reference template is defined by computation of the ranks of grey shades in the object of interest. The stages of the method are iterated over 15scene data. In accordance with the invention, the apparatus for locating and identifying an object of interest in a complex image frame comprises an object model data base for defining the object of interest as a 20 reference template. A sampler provides a sampled digitized image frame having an address. An incrementer controls the center of the digitized image. A rank generator replaces grey shade information in the complex image frame and the reference template with ranks, 25 thereby providing a ranked template and a ranked scene sample of the complex image frame. A comparator/summer correlates the ranked template with the ranked scene sample of the complex image frame. An adaptive threshold generator/imple enter utilizes data from the 30 sampled image frame to provide threshold number. A controller controls the object model data base and is controlled by the adaptive threshold lgenerator/implementer, whereby the controller is informed by signal from the adaptive threshold generator/implementer, if the correlation sum fails to exceed the threshold number. The controller causes the incrementer to increment the address of the sampled image frame. The incrementer informs the controller when a digitized image frame has been completely scanned, at which point the controller signals the object model data base to institute a new model as the 0 eference template. The object model data base has an input connected to the controller, a first output connected to the sampler and to the rank and report generators and a second output coupled to the controller. The sampler 5has a first input connected to the output of the object model data base, a second input connected to the incrementer and an output connected to the rank generator and to the adaptive threshold generator/implementer. The incrementer has a first 0 output connected to the controller and a second output connected to the sampler and report generator. The rank generator has a first input connected to the output of the object model data base, a second input connected to the output of the sampler, a first output connected to 5 the comparator/summer and a second output connected to the comparator/summer. The comparator/summer has a first input connected to the first output of the rank generator, a second input connected to the second output of the rank 0 generator and an output connected to the adaptive threshold generator/implementer. The adaptive 5 threshold generator/implementer has a first input lconnected to the output of the sampler, a second input connected to the output of the comparator/summer and an output coupled to the controller. The controller has a first input connected to the incrementer, a second input 5coupled to the output of the adaptive threshold generator/implementer, a third input coupled to the output of the adaptive threshold generator/implementer and coupled to the second output of the object model data base and an output connected to the object model 0data base. A report generator generates an object report containing identification data extracted from the reference template and location data from the incrementer. The report generator has a first input 5connected to the first output of the object model data base, a second input connected to the second output of the incrementer, a third input coupled to the output of the adaptive threshold generator/implementer and an output for providing the object report. 0 A first terminal inputs image frame update data to the object model data base and to the controller, a second terminal inputs digitized image frame data to the sampler. The report generator generates an object report containing identification 5 data extracted from the reference template and location data from the incrementer. A third terminal outputs the object report from the report generator. The object model data base outputs the reference template at its first output and outputs a data base exhausted signal at 0 its second output. The sampler outputs sampled image frame data at its output. The incrementer outputs an 5 image scan complete signal at its first output and location data at its second output. The rank generator outputs ranked template data at its first output and ranked scene sample data at its second output. The comparator/summer outputs correlation data at its output. The adaptive threshold generator/implementer outputs ID status data at its output and the controller outputs new object/perspective select data at its output. Other objects and features of the present invention will become apparent from the following detailed description considered in connection with the accompanying drawing, which discloses a single embodiment of the present invention. It should be understood, however, that the drawing is designed for the purpose of illustration only and not as a definition of the limits of the invention. In the drawing, wherein similar reference characters denote similar elements throughout the several views: The single figure is a block diagram of an embodiment of the apparatus of the invention for locating and identifying an object of interest in a complex image. Rank correlation is a method of replacing data with a simple listing of "ranks," which describes how many other samples in the data set the sample exceeds in absolute level (E.L. Lehmann, "Nonparametrics: Statistical Methods Based On Ranks", McGraw-Hill Book Publishing Company, 1975). The result is like a "poll" describing how many samples it "beats." Rank correlation is an old approach almost exclusively used in the social sciences to analyze the qualitative Iresults of opinion polls and political voting patterns, wherein data is generated by primarily subjective mechanisms. In such cases, rank correlation mat be the only technique that can provide numerical correlation. 5 Symbolically, the ranking operation is denoted by the script "τ\ . The process of ranking the data in an array s(i,j) of dimension n by is then written as follows: S'(i,j) = R [S(i,j)] 0<i<n, 0<j<m (1) lOwhere the resultant array S' (i,j) is S(i,j) with its elements transformed into ranks. Similarly, the operation of ranking the data in an array T{i,j) is written as follows: T'(i,j) = R[T(i,j)] 0<i<n,0<j<m (2) 15 The original arrays, S(i,j) and T(i,j), can be rank correlated by simply comparing the resultant rank arrays, S'(i,j) and T'(i,j) on an element-by-element basis, as follows: 20 If T' (i,j) = S'(i,j)] then C(i,j) = 1 (3) If T' (i,j) ≠ S'(i,j)] then C(i,j) = 0 (4) wherein C(i,j) is the correlation array. The final step is to sum all the resultant elements of C(i,j) and 25 normalize over the entire array as follows: m n Correlation Strength = — Y Y c (i , j ) (5) wherein N = n x m. If the resulting correlation 30 strength approaches unity, then the original S and T arrays are "highly ranked correlated" and the images are 35 inherently similar. If the resulting correlation strength approaches zero, then the original S and T arrrays are "highly rank decorrelated" and the images are inherently dissimilar. In the foregoing discussion, if S(i,j) describes an image or scene, its elements being individual pixels, and T(i,j) describes the image of an object of interest, then, in the case of the correlation strength approaching unity, an object detection could be declared. The present invention involves the innovative application of a modified rank correlation to the quantitative correlation of images. Rank correlation is a model based process, since it requires a-priori information about the object image. The object of interest can be obtained from a pre-calculated look-up- table (LUT), or can be calculated in real time from the faceted geometric model of the object by a suitable code. It may also be obtained from a previous image frame. Once the object of interest has been defined, the method of the invention involves the following steps: 1) Computation of the ranks of the grey shades contained in the object of interest, that is, creation of a reference template. 2) Computation of the ranks of the grey shades contained in the scene for the corresponding area in the template. 3) Comparison of the ranks of step 1 with the ranks of step 2 to determine closeness of fit. 4) Iteration of the procedure over the appropriate scene data. 5) Use of an appropriate, or a selected decision criteria, or data, to set the threshold for detection. As shown in the figure, the process of rank correlation is initiated by an external signal, image frame update, inputted via a terminal 11. The image frame update signal is supplied to controller 12, which transmits the appropriate timing signals to all the modules and initiates the rank correlation process. The image frame update signal is also supplied to object model data base 13 to reset said data base to its beginning. At such time, a digitized image frame is supplied from an external. source via a terminal 14 to sampler 15 of size n by . Each of the controller 12, the object model data base 13, and the n by m sampler 15 may comprise any suitable known hardware and software. Sampler 15 receives the values of n and m from the reference template n by m outputted by object model data base 13. Sampler 15 selects an n by m section of the digitized image frame, whose center (i,j) is determined by an incrementer 16 of any suitable known type. This section of the digitized image frarrε is outputted by sampler 15 as the sampled image frame n by m and inputted to a rank generator 17 of any suitable known type. Controller 12 selects a model from the object model data base 13 and said object model data base feeds the selected model to tank generator 17 as the reference template. Rank generator 17 replaces grey shade information contained in the sampled image frame and reference template inputted to it with ranks and sends the resultant outputs, which are the ranked template and the ranked scene sample, to a comparator/summer 18 of any suitable known type. Comparator/summer 18 correlates the ranked template with the sampled image frame, thereby producing a correlation sum [c(i,j)J. The correlation sum produced by comparator/summer 18 is supplied to an adaptive threshold generator/implementer 19 of any suitable known type. Adaptive threshold generator/implementer 19 uses data from the sampled image frame to provide a threshold number. If the correlation sum does not exceed the threshold, the ID status output of adaptive threshold generator/implementer 19 is determined as negative. In such cases, correlation process controller 12 is informed of this and causes incrementer 16 to increment the i,j address by 1 in i or j . The foregoing process continues, using the same reference template and an n by m sample of the digitized image of a new center i,j. As long as the ID status continues to be negative and i is equal to or less than n and j is equal to or less than m, the foregoing process continues. When correlation process controller 12 is informed that the digitized image frame has been co pletely scanned, which occurs via an image scan lcomplete from incrementer 16, said controller selects another object model from object model data base 13 and the process of correlation continues using the new model as the reference template. This process of selecting 5reference templates from object model data base 13 and correlating them with the sampled image frame continues until one of the following three things occurs. 1. All the models in the object model data base have been selected and processed, creating the data lObase exhausted signal, with negative ID results. 2. An image frame update occurs, which restarts the correlation process and initializes. 3. A positive ID occurs. 15 positive ID informs a report generator 20, of any suitable known type, to generate an object report containing identification data extracted from the reference template, and location data i,j from incrementer 16. The object report is provided to an 20external object processor via a terminal 21. The ID status output of adaptive threshold generator/implementer 19 is determined as negative or positive by a decision processor 22 of any suitable known type. The positive ID also is imputed to an OR 25 gate 23 of any suitable known type. A data base exhausted signal output of object model data base 13 is also inputted to OR gate 23. The OR gate 23 produces an end process signal when a positive ID signal or a data base exhausted signal is received by OR gate 23. The 30 end process signal is fed to controller 12 and causes 35 the controller to end the identification/location process. While a single embodiment of the present invention has been shown and described, it is to be understood that many changes and modifications may be made thereunto without departing from the spirit and scope of the invention as defined in the appended claims.

Claims: 1 CLAIMS WHAT IS CLAIMED: l. A method of location and identification of an object of interest in a complex image frame, said method comprising the steps of: defining the object of interest as a reference 0template; computing the ranks of grey shades in the complex image frame; computing the ranks of grey shades in the references template; 5 comparing the ranks of grey shades in said reference template with the ranks of grey shades of said complex image, thereby determining closeness of fit; and -tting a threshold for detection by utilizing selected decision data. 20 2. A method as claimed 1, wherein said reference template is defined by computation of the ranks of grey shades in said object of interest. 253. A method as claimed in claims 1 or 2, wherein said steps are iterated over scene data. 4. An apparatus for locating and identifying and object of interest in a complex image frame, said 30 apparatus comprising: 35 object model data base means for defining the object of interest as a reference template; sampler means for providing a sampled digitized image frame having an address; incrementer means for controlling the center of the digitized image; rank generator means for replacing grey shade information in the complex image frame and the reference template with ranks, thereby providing a ranked template and a ranked scene sample of said complex image frame; comparator/summer means for correlating the ranked template with the ranked sampled image frame; adaptive threshold generator/implementer means utilizing data from the sampled image frame to provide a threshold number; and controller means controlling said object model data base means and controlled by said adaptive threshold generator/implementer means, whereby said controller is informed by signal from said adaptive threshold generator/implementer means if the correlation sum fails to exceed the threshold number and said controller means causes said incrementer means to increment the address of said sampled image frame and whereby said incrementer means informs said controller means that a digitized image frame has been completely scanned and said controller controls said object model data base means to institute a new model as the reference template. 5. Apparatus as claimed in claim 4, further comprising report generator means for generating an object report containing identification data extracted from said reference template and location data from said incrementer means. 6. Apparatus as claimed in claim 4 or 5, wherein said object model data base means has an input connected to said controller means, a first output connected to said sampler means and to said rank generator and report generator means and a second output coupled to said controller means. 7. Apparatus as claimed in anyone of claims 4-6, wherein said sampler means has a first input connected to the output of said object model data base means, a second input connected to said incrementer means and an output connected to said rank generator means and to said adaptive threshold generator/implementer means. 8. Apparatus as claimed in anyone of claims 4-7, wherein said incrementer means has a first output connected to said controller means and a second output connected to said sampler means and report generator means. 9. Apparatus as claimed in anyone of claims 4-8, wherein said rank generator means has a first input connected to said output of said object model data base means, a second input connected to the output of said sampler means, a first output connected to said comparator/summer means and a second output connected to said comparator/summer means. 10. Apparatus as claimed in anyone of claims 4-9, wherein said comparator/summer means has a first input connected to the first output of said rank generator means, a second input connected to the second output of said rank generator means and an output connected to said adaptive threshold generator/implementer means. 11. Apparatus as claimed in anyone of claims 4-10, wherein said adaptive threshold generator/implementer means has a first input connected to said output of said sampler means, a second input connected to the output of said comparator/summer means an output coupled to said controller means. 12. Apparatus as claimed in anyone of claims 4-11, wherein said controller means has a first input connected to said incrementer means, a second input coupled to the output of said adaptive threshold generator/implementer means, a third input coupled to the output of said adaptive threshold generator/implementer means and coupled to the second output of said object model data base means and an output connected to said object model data base means. 13. Apparatus as claimed in anyone of claims 5-12, wherein said report generator means has a first input connected to said first output of said object model data base means, a second input connected to said second loutput of said incrementer means, a third input coupled to the output of said adaptive threshold generator/implementer means and an output for providing said object report. 5 14. Apparatus as claimed in anyone of claims 4-13, further comprising first terminal means for inputting image frame update data to said object model data base means and to said controller means, second terminal lOmeans for inputting digitized image frame data to said sampler means, report generator means for generating an object report containing identification data extracted from said reference template and location data from said incrementer means and third terminal means for 15outputting the object report from said report generator means. 15. Apparatus as claimed in anyone of claims 4-14, wherein said object model data base means outputs said 20reference template at its first output and outputs a data base exhausted signal at its second output, said sampler means outputs sampled image frame data at its output, said incrementer means outputs an image scan complete signal at its first output and location data at 25 its second output, said rank generator means outputs ranked template data at its first output and ranked scene sample data at its second output, said comparator/summer means outputs correlation sum data at its output, said adaptive threshold 30 generator/implementer means outputs ID status data at 35 its output and said controller means outputs new object/perspective selects data at its output.

Inventors: KLEEHAMMER ROBERT J, MANDELI STEVEN, KLEEHAMMER, ROBERT, J., MANDELI, STEVEN
Applicants: GRUMMAN AEROSPACE CORP, GRUMMAN AEROSPACE CORPORATION
Application year: 1995
Main classification: G06K9
IPC codes: G06K9, G06T1, G06T7