METHOD AND APPARATUS FOR DETECTING INNOVATIONS IN A SCENE

Abstract: System for detecting innovations in an image. A method includes gnerating plural parallel signals representing light incident on adjacent pixels (masks) which form an n by one vector, (Z), n being the number of mask pixels. L groups of adjacent pixels geometrically cover the image in parallel. The system replicates the generating step to generate plural (Z) vectors by taking multiple frames of observations of the image. These (Z) vectors are denoted (Zk), where k equals 1, 2, 3,..., m, m being the number of replicates. Each (Zk) vector relates to a vector ( beta k) having three parameters by the equation Zk = D beta k + ek, where (ek) is noise. A solution thereof yields estimates of the parameters beta k = D<T>Zk, where D<T> is a three by four matrix, beta k is a three by one vector and (Zk) is a four by one vector. beta k includes components (uk), (Ak) and ( beta k) whose values are monitored, and a signal is generated whenever one of these variables exceeds a threshold.

Description: METHOD AND APPARATUS FOR DETECTING INNOVATIONS IN A SCENE This invention generally relates to methods and apparatus for detecting innovations, such as changes or movement, in a scene or view, and more particularly, to using associative memory formalisms to detect such innovations. In many situations, an observer is only interested in detecting or tracking changes in a scene, without having any special interest, at least initially, in learning exactly what that change is. For example, there may be an area in which under certain circumstances, no one should be, and an 10 observer may monitor that area to detect any movement in or across that area. At least initially, that observer is not interested in learning what is moving across that area, but only in the fact that there is such movement in an area where there should be none. - * -5 Various automatic or semiautomatic techniques or procedures may be employed to perform this monitoring. For instance, pictures of the area may be taken continuously and compared to a "standard picture," and any differences between the taken pictures and that standard picture indicate f) a change of some sort in the area. Alternatively, one could subtract adjacent frames of a time sequence of pictures taken of the same scene in order to observe gray level changes. It is assumed herein that the sampling rate, i.e. the frame rate, is selected fast enough to capture any sudden change or ^- motion (i.e. "innovations" or "novelty") . This mechanization would not require knowledge of a "standard picture". More particularly, each picture may be divided into a very large number of very small areas (picture elements) referred to as pixels, and each pixel of each taken picture may be compared 30 to the corresponding pixel of the standard or adjacent frame picture. The division of a picture containing the scene into 5 a larger number of pixels can be accomplished by a flying spot scanner or by an array of photodetectors/photosensors as well known to those versed in the art. The resultant light intensity of the ' discretized picture or image of the scene can be left as analog currents or voltages or can be J digitized into a number of intensity levels if desired. We will refer to the photodetector/photosensor output current or voltage signal as the input signal to the apparatus described herein. Whether the input signal is a current or voltage depends on the source impedance of the photodetector/ photosensor as well known to those versed in the art. This may be done, for example, by using photosensors to generate currents (or voltages) proportional to the amount of light incident on the pixels, and comparing these currents to currents generated in a similar fashion from the amount of - ? light incident on the pixels of the standard scene. These comparisons may be done electronically, allowing a relatively rapid comparison. Even so, the very large number of required comparisons is quite large, even for a relatively small scene. Because of this, these standard techniques require a 0 very large amount of memory and are still comparatively slow. Furthermore, changes in the scene can be caused not only by gray level differences but also by innovations or novelty (changes) in the texture of the scene. In such cases the method of reference comparisons or subtracting adjacent -' frames would not work. Hence, these prior art arrangements do not effectively detect changes in the texture of a scene. 0 5 The present invention is a method for detecting innovation in a scene comprising an array of pixels, and the method comprises the steps of generating signals representing the amount of light incident on a group of adjacent pixels, said signals forming a n by one vector, Z, where n equals the number of pixels in the group; and replicating the generating step a multitude of times to generate a multitude of said Z vectors, the replicated Z vectors being represented in the form Z, , where k = l,2,3,...,m, where m equals the number of said replicates. The method further comprises the steps of relating each of said Z, vectors to a vector^, , having at least one parameter, in the presence of noise e, , by means of the relationship Z, = ϋβ, + e, , where D is an at least four by at least three matrix; obtaining a solution for^, in terms of Z, to derive values for said one parameter; monitoring the value of said one parameter over time; and generating a signal whenever said one parameter rises above a preset level. The present invention also is an apparatus for detecting innovation in a scene including an array of pixels, and the apparatus comprises means to generate at each, of a. multitude of times, a set of signals representing the amount of light incident on a set of adjacent pixels, each set of signals forming a n by one vector, Z, where n equals the number of signals in the set, the sets of signals being represented in the form Z, , where k = l,2,3,...,m, where m equals the number of said sets. The apparatus further comprises means to relate each of said Z, vectors to a vector jβ , , having at least one parameter, in the presence of noise e, , by means of the relationship Z, = D , + e, , where D is an at least four by at least three matrix; means to derive values for said one parameter in terms of Z-_; means to monitor the values of said one parameter over time; and means to generate a signal v/henever said one parameter rises above a respective preset level. Figure 1 illustrates a general MxN pixel image or detector array of observations of frames of a scene, taken over a period of time and generally outlining how that scene may change. Figure 2 shows a two by two group of pixels (a two 10 by two elementary mask) of one of the observation frames. Figure 3 shows a series of two-by-two pixels . groups (masks) taken from a series of the observation frames. Figure 4 schematically depicts one network in the form of a three-neuron neural network with constant weights - *■ "--' for processing the signals from the group of pixels shown in Figure 3. Figure 5 schematically depicts another network to process the signals from the group of pixels shown in Figure 3. 20 25 30 35 Figure 6 schematically depicts a procedure to 1 calculate a robustizing factor that may be used in the present invention. Figure * 7 schematically depicts a network similar to the array represented in Figure 5 , but also including a noise -^ attenuating robustizing factor. Figure 8 comprises three graphs showing how three variables obtained by processing signals from a (2x2) mask change as an object moves diagonally from one pixel to another pixel. Figure 9 comprises three graphs showing how the three variables obtained by processing signals from a (2x2) mask change as an object moves either vertically or horizontally from one pixel to another adjacent pixel within the 2x2 mask. -^ • 5 Figure 10 shows an array of 2x2 masks at one observation frame. Figure 11 shows an array of overlapping 2x2 masks of an observation frame. I have discovered that the output signals from an - • image pixel array detector elements representing a scene under consideration can be expressed in terms of a selected group of variables in a mathematical equation having a form identical to the form of an equation used in a branch of mathematics referred to as associative mapping. I have ^5 further discovered that techniques used to solve the latter equation can also be used to solve the former equation for those selected variables, and that changes in these variables over time identify innovations in the scene. Figure 1 illustrates a series of observation frames 30 F..-F taken over a period of time. Each frame comprises an array of pixels, and Figure 2 shows a two-by-two mask neighborhood from frame F... Generally, a pixel is identified by the symbol z .. , where i identifies the row of the pixel in the frame, and j identifies the columns of the pixel in the frame. Thus, for " example, the four pixels shown in Figure 2 are identified as z. . , - - - t - ~ - < -~ n <-~ z ?2" Photosensors (not shown) may be used to generate currents proportional to the intensity of light incident on each pixel, and these currents (i.e. input signals described previously) may be represented, respectively, by the symbols ... . , Z,-, Z_- and 22 - These current measurements can be used to form a four by one vector, The measurement vector Z can also be expressed in the form of a linear model in the following manner: Z = fi> + e (2) Where t is a three by one parameter vector representing the current due to the light ' from the pixels from objects of interest, D is a four by three matrix, discussed below, and e is a four by one vector representing the current due to random fluctuations. Over time, a sequence of frames of a scene may be taken or developed, and Figure 3 shows a series of 2x2 masks from frames F, , F_ and F_ . The symbol for each pixel within the mask is provided with a superscript, k, identifying the frame of the pixel; and thus the pixels from frame F.. are identified in Figure 3 as z,-,, z i2' z 21 and * z 2 2 ' and * tιe pixels from frame F 2 are identified in Figure 3 as __ . , - - , , z 2 2 1 and z„1„. Photosensors may be used to generate currents representing the intensity of light incident on corresponding pixels of each frame as described previously; and if m frames are taken, the current measurements from the pixels z.., z i 2' z ?1 and z„„ can be generally represented by 1 - . , - -T- i - ~ an d Z ^ , where k = 1,2,3,..., . Equations (1) and (2) can be generalized respectively, as follows: (4) z = D A e, where k - 1,2,3, ,m. It is known that, while equation (4) does not always possess a unique solution fo S . , an approximation to β , , identified by the symbol , can be determined by the method of least squares, given by the equation: T -1 T ^ (D D) D Z, : 5 ) Where D T i.s the transpose of D. This nonrecursive method is based on the direct solution of the normal equations of an equivalent linear experimental design model. If D can be constructed as an orthogonal matrix, than D TD = 1 , and equation (5) becomes A = DTZ r . (6; k Equation (6) has the same form as the equation: i y, = Mx, for all k in the set (k=l,2,3, ... ,m) (7) which is used in ' linear associative mapping to represent the fact that M is the matrix operator by which pattern y, is obtained from pattern x, . If M is a novelty mapping then M is always a balanced matrix, which means that all of the elements of M are either 1 or -1. If equation (6) is to correspond to equation (7) , then D must also be balanced. Thus, D must have the following properties: 0 i) it must be orthogonal, which means that D TD = c[I] , where c is a scalar and I is the identity matrix. ii) every element of D must be 1, or -1, and iii) it must have four rows and three columns in this example case. 5 The design matrix of certain classes of reparametrized linear models are found to satisfy the above criteria for novelty mappings by providing the required balanced properties of the matrix operator. For a class of randomized block fixed-effect two-way layout with n 0 observations per cell experimental design, the corresponding reparametrized design matrix is both full rank and orthogonal. In this case, the association matrix can be prespecified by the model and becomes the transpose of the design matrix whose elements are +1 and -1. 5 I have found that one solution for D is: 1 1 1 D = 1 1 -1 (8) 1 -1 1 1 -1 -1 then equation ( 4 ) becomes Substituting the right-hand side of equation for D in equation (11) yields Equation (12) can be solved for u. A, and B, as follows u,_ = Z ll + '12 + Z 21 + Z 22 (13) A,. = '11 '12 '21 '22 (14) B k " Z ll " Z 12 + Z 21 ~ Z 22 (15) t o -- Figure 4 schematically depicts a logic array or network (which is in the form of a three-neuron neural network with constant weights) to process input signals according to equations (13) , (14) and (15) , and in particular, to produce output signals u, , A, and B, from input signals '^r, Z-.^, Z.&: and -'y. As previously mentioned the input or output signals can represent either voltages or currents as appropriate. Input signals ,^τ, - - ^ r - - -T an -- z re conducted to multiply operators OP.., 0P 2 , OP., and 0P d , respectively, and each of these operators is a unity operator. The output currents of these operators have values that are the same as the respective input signals Z , . , Z_-^- and Z *—, and these operators are shown in Figure 4 to illustrate the fact that they apply a weighted value of +1 to input signals - r r ~ - \ t *•> h ll i Z r and - ■ - > . The output of operators OP 2 , OP, and OP. are applied, respectively, to operators 0P,_, OP, and OP_ , which are signal inverters . Each of these latter three operators generates an output signal that is equal in magnitude, but opposite in polarity, to the input signal applied to the operator. Thus, the output of operator 0P_ has a magnitude equal to and a polarity opposite to the signal Z, 2 , the output of operator OP, has a magnitude equal to and a polarity opposite to the signal Z_^-, and the output of operator 0P_ has a magnitude equal to and a polarity opposite o the signal z ** . The output of operator OP. is applied to an "a" input of each of a group of summing devices S.. , S„ and S_, the output of operator 0P» is applied to a "d" input of summing device S.. and t . o a "c" input of summing device S ? , the output of operator OP, is applied to a "b" input of each of the summing devices S. and S,, and the output of operator OP. is applied to a "c" input of summing device S- . The output of operator 0P,_ is applied to a "c" input of summing device S_, the output of operator 0P fi is applied to a "d" - * - input of summing device S„, and the output of operator 0P 7 is applied to a "b" input of summing device S 2 and to a "d" input of summing device S_. For the sake of clarity, the "a", "b", "c" and "d" inputs of summing devices S- , S 2 and S, 5 are not expressly referenced in Figure 4. Each summing device S.., S 2 and S, generates an output signal equal to the sum of the signals applied to the inputs of the summing device. Thus: output of S 1 = Z^ + Z ^ + Z ^ + Z^ (16) output of S z = z - Zf 2 + Zf 2 - z (17) outpu t of S 3 = z + z - z -Z 2 ^ (18) As can be seen by comparing equations (13) -(15) with equations (16) -(18) , the outputs of summing devices S, , S„ and S, respectively represent u, , A, and B, . Another solution (recursive) for equation (4) can be derived by a technique called stochastic approximation minimum variance least squares (referred to as SAMVLS) , and this technique provides the iterative equation: β k+l β A D [Z k - D O k ] (19) k Where: an arbitrary value is chosen for ftά __. , and A is a selected matrix, referred to as the gain matrix. The gain matrix, A, controls the rate of convergence of the procedure along with the step size k. The gain matrix can also be made adaptive (a function of the input data sequence) by those versed in the art to keep the recursive estimation procedure convergence rate "near" optimum. This iterative/corrective procedure realization is based on temporal data sequence novelty parameter estimation from the measurement equation of the linear model using robustized stochastic approximation algorithms requiring little storage. -> • Equation (19) is a recursive equation in that each J2 is expressed in terms of the prior calculated _ J , value. Any arbitrary value is chosen for β_> , and so there will likely be an error for the first few calculated jP) . values. Any error, though, will decrease over time. Also, 0 under most conditions, there is a known range for the value of J*> . , and picking a β . within this range limits any error for the first few β \ , values calculated by means of equation (19) . Indeed, a skilled individual will normally be able to provide a good approximation of β> . , so that any error in the 5 values calculated by equation (19) may often be negligible. Figure 5 schematically depicts a logic array or network to process input signals according to equation (19) , and in particular, .to produce the output vector^fl , _, , from 0 the input vectors Z, and A , . For the sake of simplicity. Figure 5 does not show the individual components of Z, , fl , or β , . , nor does Figure 5 show the individual operators representing the elements of matrix D or A. These components and operators could easily be added by those of 5 ordinary skill in the art to expand Figure 5 to the level of detail shown in Figure 4. With the circuit shown in Figure 5, a , value is A conducted to operator OP, which multiplies - ->Y ---•- matrix D . At the same time, the measured signal values comprising 0 Z, are conducted to operator 0P_, which multiplies Z, by the matrix D . The outputs of operators 0Po o and 0P n y are conducted to operator 0P 1f) , which subtracts the former output 5 from the latter output, and the difference between the outputs of operators 0P o and 0P Q is conducted to operator OP-,-,, which multiplies that difference by the matrix A divided by k. The product produced at operator OP.., is conducted to operator OP,,, where β> , is added to that product to produce , . The value of fø , , is conducted both to an output of the network, and to delay means D__. , which simply holds that vector for a unit of time, corresponding to the iteration step, k. The Z) . values calculated by using equation (19) are sensitive to all signal changes in the elementary mask unit, including changes that are of interest and changes that are not of interest, referred to as noise. To decrease the sensitivity of W. to noise, and ideally to make j23 Λ . insensitive to noise, recursive estimation procedures based on robustized stochastic approximation may be incorporated into equation (19) . By using a nonlinear regression function, the recursive estimator can be made robust, i.e. the output parameter estimates made insensitive to unwanted disturbances/changes in the measurement equation of the model. In particular, W , a symmetric form of the Mann-Whitney-Wilcoxon nonparametric statistic based b-batch, nonlinear robustizing transformation may be added to equation (19) . More specifically , b b where r and s each is a set consisting of b sample measurements; and sgn is an operator which is equal to +1 if r.-s J. is g -reater than 0,' equal to 0 if r1.-s"J. eq^uals zero,' and equal to -1 if r.-s. is less than 0. U For example, assume that a total of eight sample measurements are taken, producing values 4, 2, 6, 1, 5 , 4, 3 and 7. These sample measurements may be grouped into the r and s sets as follows = ^4, 2, 6, 1^ (21) * can be calculated as follows: w = 1 sgn (2-s. 1 = 1- [ sgn (4-5) + sgn (4-4) + sng (4-3) - " - sgn (4-7) 16 + sgn (2-5) + sgn (2-4) + sgn (2-3) + sgn (2-7) (24) + sgn (6-5) + sgn (6-4) + sgn (6-3) • 4- sgn (6-7) + sgn (1-5) + sgn (1-4) + sgn (1-3) + sgn (1-7)] w u = 1_ [-1+0-1+1-1-1-1-1+1+1-1 16 -1-1-1-1 (25) w = 1 [-8] = -1 16 We note that in general, max w = +1 thus w has been normalized to ±1. Figure 6 schematically illustrates this procedure to calculate VT . A set of b sample values is stored in memory M.. a different set of b sample values is stored in memory M, , and then - is calculated by means of equation (20) . Various other procedures are known for calculating the robustizing factor w , and any suitable techniques may be used in the practice of this embodiment of the invention. The w factor is introduced into equation (19) as follows : A β x+1 = β - - A_ w b (q ± ) (27) k Where Where i equals 1, 2, 3,...,b, and k =b (k-1) . A is the gain matrix and selected to achieve a near optimum convergence rate for the procedure. One value for A which I have determined is given by the equation A time dependent adaptive gain matrix A, ( . ) could also be used in equation (27) to provide a faster approximation to Λ , . , although for most purposes, a fixed A value provides sufficient convergence rate. Numerous techniques are known by those of ordinary skill in the art to determine a time dependent adaptive gain matrix, and any suitable such technique may be used in the practice of this embodiment of the invention. Figure 7 schematically illustrates a network or array to process input signals according to equation (27) . As can be seen by comparing Figures 7 and 5, the robustizing of equation (19) requires the addition to the circuit of Figure 5 of two buffer units . and B_, and the matrix operator ^. The first m values of Z, are stored in buffers />- κ B π and B,, an arbitrary J .. is provided to operator 0P o , and the / β& . vector is operated on by matrix DT. At the same time, the vector Z, is operated on by the matrix D T at operator OP,. The output of operators OP, and OP, are conducted to operator OP..,, where the former is subtracted from the latter. This difference is then multiplied by , and this result is operated on by the gain matrix A at operator OP.,., . The output matrix from operator OP^ is added to JP> , at operator OP.. , to derive β , __. This value is conducted both to the output of the network, and to unit delay means D. , which holds that value of " , , for a time unit, until the network is used to calculate the next y" , value. In effect, ^ is a data dependent adaptive nonlinear attentuation factor, formed by summing and limiting selected measured values, and the introduction of this factor is designed to eliminate false alarms caused by increases in noise-like disturbances. The values taken to form are selected, not on the basis of their absolute magnitude, but rather on the basis of their value relative to the immediately preceding and immediately following measured values. Figure 8 shows the output values for u, , A, and B, for the situation where an object moves from one pixel, such as pixel Z-,-,, to a diagonal pixel, such as pixel Z,,. As can be seen, such movement is clearly indicated by a spike in u, and the parameters A and B do not show any significant change. Figure 9 shows the output signals u, , A, and B, during movement of an object from one pixel to an adjacent pixel, such as from pixel Z.,.. to pixel Z,-,. As can be seen, this movement results in spikes in the value of all three parameters, and in fact this change produces a double spike in the value of u. Thus, movement of an object across pixels z. __. , z - ~ r z_ 1 and z,, can be automatically detected by, for example, providing first, second and third threshold detectors to sense the output of summing devices S _, , S, and S,, respectively, of Figure 4 and to generate respective signals whenever the level of the output of any one of the summing devices rises above a respective preset level. As will be understood by those of ordinary skill in the art, these movement indication signals may be, and preferably are, in the form of electric current or voltage pulses, forms that are very well suited for use with electronic data processing equipment such as computers and microprocessors. Moreover, the present invention is effective to detect changes in the texture of a scene— hich is the result of changes in the light intensity of individual pixel groups—even if there is no actual movement of an object across the scene. A scene, of course, normally includes many more than just four pixels, and movement across a scene as a whole can be tracked by covering the scene by a multitude of elementary mask operators, and automatically monitoring the movement indication signals of the individual mask operators, a technique referred to as massive parallelism. For example, with reference to Figure 10, a movement indication signal from pixel group pg. followed by movement indication signals from pixel groups pg, and pg, indicate horizontal movement across the scene. Analogously, a movement indication signal from pixel group pg, followed by movement indication signals from pixel groups pg. and pg ^ indicate vertical movement across the scene. A more precise tracking of an object across a scene * can be obtained by overlapping the pixel groups . For instance, with reference to Figure 11, pixel group pg, can be formed from pixels ~ _. . , z,,, z,.. and z--,-,; pixel group pg, can be formed from pixels z_.,, - - ~ r -- - a -t- z '" -■•--■ pixel group pg can be formed from pixels ~ - , z - > ' - ~ - an< 3- z - > ~ - Movement indication signals from pixel groups pg, and pg,, coupled with no movement indication signals from pixel group pg ? , indicate movement of an object between pixels __ . . and z,, . Analogously, movement indication signals from pixel groups pg. and pg,, in combination with no movement indication signal from pixel group pg,, indicate movement between pixels z . . and z-,-■,. In addition to detecting the presence of innovations and direction of movement, one can also determine 5 the speed (and velocity given the direction of motion) of an object. This can be accomplished by computing the dwell time of an object"within a mask. The dwell time depends on the object speed, S, the frame rate £l-=l/T, where T is the frame time, the pixel size and the mask size. If each pixel within an elementary 2x2 mask is a by a units wide, then the speed of an object moving diagonally is given by S = (30) T 25 where L is the number of masks in the frame. The networks illustrated in Figures 4 , 5 and 7 are similar in many respects to neural networks as mentioned before. A multitude of data values are sensed or otherwise obtained, each of these values is given a weight, and the 0 weighted data values are summed according to a previously determined formula to produce a decision. 35

Claims: WHAT IS CLAIMED IS: 1. A method for detecting innovation in a scene comprising an array of pixels, the method comprising the steps of: generating signals representing the amount: of light incident on a group of adjacent pixels, said signals forming a n by one vector, Z, where n equals the number of pixels in the group; replicating the generating step a multitude of times to generate a multitude of said Z vectors, the replicated Z vectors being represented in the form Z, , where k = 1,2,3, . ,.,m, where m equals the number of said replicates; relating each of said Z, vectors to a vector β, , having at least one parameter, in the presence of noise e, , by means of the relationship Z, = DA + e, , where D is an at least four by at least three matrix; obtaining a solution for A, in terms of Z, to derive values for said one parameter; monitoring the value of said one parameter over time; and generating a signal whenever said one parameter rises above a preset level. 2. A method according to claim 1, wherein the group of pixels form a rectangle in the scene. 3. A method according to claim 2, wherein: the group of adjacent pixels includes four pixels; and 1 1 1 1 1 -1 D= 1 -1 1 1 -1 -1 4. A method according to any of the above claims, wherein: the signals representing the amount of light on the pixels are electric voltage signals; the obtaining step includes the steps of i) applying the electric voltage signals forming a given Z, vector to a network, ii) processing the electric voltage signals through the network, and iii) generating from the network at least one output signal representing the component of the A , vector associated with the given Z, vector, 5. A method according to claim 4, wherein: the step of generating the signals representing the amount of light incident on the group of pixels includes the step of generating at least first, second, third and fourth electric voltage signals respectively representing the amount of light incident on at least first, second, third and fourth of the group of pixels the processing step includes the steps of i) summing the first, second, third and fourth voltage signals, ii) summing the first and second voltage signals and the negatives of the third and fourth voltage signals, and iii) summing the first and third voltages and the negatives of the second and fourth voltage signals; and • the step of generating at least one output signal includes the steps of i) generating a first output signal proportional to the sum of said first, second, third and fourth voltage signals, ii) generating a second output signal proportional to the sum of said first and second voltage signals and the negatives of the third and fourth voltage signals, and iii) generating a third output signal proportional to the sum of the first and third voltage signals and the negatives of the second and fourth voltage signals. 6. A method according to claim 5, wherein the network includes first, second, third and fourth input means; first, second and third voltage inverters, and first, second and third summing devices, and wherein: the applying step includes the steps of applying the first, second, third and fourth voltage signals respectively to the first, second, third and fourth input means of the network; the processing step further includes the steps of i) applying the voltage of the second input means to the first inverter to generate a first internal voltage signal having a polarity opposite to the polarity of the second input means, ii) applying the voltage of the third input means to the second inverter to generate a second internal voltage signal having a polarity opposite to the polarity of the third input means, and iii) applying the voltage of the fourth input means to the third inverter to generate a third internal voltage signal having a polarity opposite to the polarity of the fourth input means; the step of summing the first, second, third and fourth voltage signals includes the step of applying to the first summing device, the voltages of the first, second, third and fourth input means; the step of summing the first and second voltage signals and the negatives of the third and fourth voltage signals includes the step of applying to the second summing device, the voltages of the first and second input means and the voltages of the second and third internal voltage signals; and the step of summing the first and third voltage signals and the negatives of the second and fourth voltage signals includes the step of applying to the third summing device the voltages of the first and third input means and the voltages of the second and third internal voltage signals. 7. A method according to any of the above claims. wherein the obtaining step includes the step of obtaining an approximation of p . , given by the symbol ,β, , by means of the equation: • ^ where D is the transpose of D. • 8. ' A method according to any of the above claims, wherein the obtaining step includes the step of obtaining an approximation of A , , given by the symbol A _. , by means of the equation: where A is an at least three by at least three matrix, and D T is the transpose of D. 9. Apparatus for detecting innovation in a scene including an array of pixels, the apparatus comprising: means to generate at each of multitude of times, a set of signals representing the amount of light incident on a set of adjacent pixels, each set of signals forming a n by one vector, Z, where n equals the number of signals in the set, the sets of signals being represented in the form Z, , where k = 1,2,3,... ,m, where m equals the number of said sets; means to relate each of said Z,k vectors to a vector & , , having at least one parameter, in the presence of noise e, , by means of the relationship Z, = D A, + e, , where D is an at least four by at least three matrix; means to derive values for said one parameter in terms of Zk, ; means to monitor the values of said one parameter over time; and means to generate a signal whenever said one parameter rises above a respective preset level. 10. Apparatus according to claim 9, wherein the group of pixels form a rectangle in the scene. 11. Apparatus according to claim 10, wherein: the group of adjacent pixels includes four pixels; and 12. Apparatus according to claim 9, wherein: the means to generate the signals representing the amount of light incident on the pixels includes voltage generating means "to generate voltage potentials representing the amount of light incident on the pixels; and " the deriving means includes a network connected to the voltage generating means to receive the voltage potentials therefrom, and to generate from each group of voltage potentials, Z, , at least one output signal representing the β , vector associated with said Z, vector. 13. Apparatus according to claim 12, wherein: the network includes first, second, third and fourth input means; first, second and third voltage inverters; and first, second and third summing devices; the voltage generating means generates first, second, third and fourth voltage signals representing the amount of light incident on first, second, third and fourth of the pixels respectively; the first, second, third and fourth input means of the network are connected to the voltage generating means, respectively, to receive the first, second, third and fourth electric voltage potentials from the voltage generating means; the first inverter is connected to the second input means to generate a first internal voltage signal having a polarity opposite to the polarity of the second input means; the second inverter is connected to the third input means to generate a second internal voltage signal having a polarity opposite to the polarity of the third input means; the third inverter is connected to the fourth input means to generate a third internal voltage signal having a polarity opposite to the polarity of the fourth input means; the first summing means is connected to the first, second, third and fourth input means and generates an output signal having a voltage equal to the sum of the voltages of the first, second, third and fourth input means; the second summing means is connected to the first and second input means and to the second and third inverters to generate an output signal having a voltage equal to the sum of the voltages of the first and second input means and the second and third inverters; and the third summing means is connected to the first and third input means and the first and third inverters to generate an output signal having a voltage equal to the sum of the voltages of the first and third input means and the first and third inverters.

Inventors: KADAR IVAN, KADAR, IVAN
Applicants: GRUMMAN AEROSPACE CORP, GRUMMAN AEROSPACE CORPORATION
Application year: 1989
Main classification: H04N7
IPC codes: G06E3, G06T7, G08B13, H04N7, H04N9, H04N11