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Anthropological methods of optical image processing
Volume 40, number 1
ANTHROPOLOGICAL
OPTICS COMMUNICATIONS
1 December 1981
METHODS OF OPTICAL IMAGE PROCESSING
V.M. GINZBURG USSR State Committee for Standards, II 7049 Moscow, USSR
Received 17 August 1981
Some applications of the new method for optical Image processing, based on a prior separation of informative elements (IE) with the help of a defocusing equal to the average eye defocusing, considered in a previous paper, are described. A diagram of a “drawing” robot with the use of defocusing and other mechanisms of the human visual system (VS) is given. Methods of narrowing the .TV channel bandwidth and elimination of noises in computer image processing by prior image defocusing are described.
In a previous paper [l] it was shown that some facts known about the human visual system (VS) could be explained by the phenomenon of defocusing. This allowed the author to put forward a hypothesis on using the crystalline lens defocusing for the preliminary processing of the image in the VS. This hypothesis provided explanation of some functional relationships in the VS and was used as a basis for a new method of optical image processing [2,3] , In this paper some new method applications mentioned in [ 1] are considered, namely: the diagram of a “drawing” robot, a TV channel with a narrowed bandwidth and a method of eliminating noises in computer image processing [9]. To confirm the possibility of automatic system construction by the method described in [-1] a threedimensional T(x, y) function of the transparent transmission with the image of an angle defocused by 0.2 D (diopfrics) (fig. 2a in [l] ) was analysed in ref. [4] . Fig. la shows the function T(I), where I is a reverse line (the valley in T(x, JJ)). The coordinates (x, y) along 1 (in mm) counted from the point (0,O) at T,,(O, 0) = 1 are marked. Letters B and A denote the external boundary and inner edge of the angle, accordingly. One can see that function T(x, y) falls on the valleys to the value of T(I) < 0.5. Functions T(x) and dZ(x)/d.x for y = 0.4 mm (fig. lb) also change considerable at the reverse line. Hence, it is possible to use the reverse line for automatic separa0 030-4018/81/0000-0000/$02.75
0 1981 North-Holland
tion of informative elements (IE) from the defocused image. The separation of IE is accompanied by an abrupt spectrum narrowing. This leads to the generalisation of the image due to the elimination of the image fine structure. This phenomenon can be used for reducing the number of spatial filters in an optical holographic processor. For further reducing this number it was proposed to represent an image as a structure of elementary geometrical figures described by the simplest analytic reference functions [2] . The image as a whole is represented as a reference functions predicate, equal to unity inside the image and zero outside it [S] . For basic figures the “genetic” figures such as a “round spot” and a “strip” for which VS has inborn detectors were used in [2]. The predicate equations of genetic functions (GF) describing these figures are of the form: Q(Y,,) = LO - Yu - a(x - x&Y -a(x-xo+So/~))>O]
- Yo - S/m = 1,
L?(y,) = [(r2 - (x - x0)2 - 0, - ro)2)
(1)
> O] = 1. (2)
Here a = tan CK,where CYis the strip slope angle, S is the strip width, r is the circle radius, x0, y. are the coordinates of a fixed point at the strip or circle centre. When S -+ 0 the “strip” degenerates into a straight line. With the help of simple logic operations, an example of a basic set of secondary GF is made from functions (1) and (2), corresponding to simple 15
Volume
40, number
1
OPTICS COMMUNICATIONS
1 December
a
b
Fig. 1. The functions:
Fig. 2. A set of elementary
16
figures described
by genetic
functions.
T(I)-(a);
T(x)-(b).
1981
Volume 40, number 1
OPTICS COMMUNICATIONS
1 December 1981
Fig. 3. The “drawing” robot diagram. geometric figures (fig. 2). With their aid a stylized image of an arbitrary real contour image applied to the processor input can be “contructed”. The total number of these functions is unlikely to exceed ten. During the defocusing of the input image IE are separated, which are the defocused images of elementary figures described by GF. Hence, to synthesise a stylized (generalized) image at the processor output, it is sufficient to use spatial filters for the defocused images of figures described by GF. Fig. 3 shows the principal diagram of a robot “drawing” a generalized image of the real object image of the real object image illuminated by white light applied to the input 1 (plane 5,~) [6] . Unit 2 forms a defocused noise-free image with separated IE. Signals of coordinates of the brightest points in IE are in turn transmitted through channel 4 into the scanner unit 3. Beam 5 of unit 3 is directed onto the appropriate IE point and scans the IE region limited by the reverse line. The IE image illuminated by the coherent light beam 18 from laser 15 through divider 16 is displayed to a given scale on the dynamical transparent of unit 3 (for instance, on Titus). This image is processed in a holographic correlator incorporating multiplicator 6, objectives 7, and a set of spatial filters 8 made for defocused images of figures described by GF. Filter recording is performed with the help of scanner 17 which shapes reference
beams 19 in succession. At the correlator output reader 9 (a dissector or charge-coupled device) is installed. At its output, depending on the coordinates Xi, yi of brightest correlation spot, the coded signal * of a given GF is produced, which is transmitted to the computer 10 for GF formation by an appropriate formula. Signals from the computer output 10 are applied to the display 11. Simultaneously, signals from units 2 and 3 are applied to the display 11 through channels 12 and 13. They contain information on the relative scale, angular IE orientation in the initial image, and on the coordinates of the maximum brightness points in IE used for “clamping” to it coordinates of the fixed point ~0 JO in the predicate equation. After “drawing” the sharp image of an elementary figure corresponding to the identified IE on the display (plane <‘, n’), a signal of the completion of operation with the given IE is transmitted from unit 11 to unit 3 through channel 14. By this signal the scanner “jumps” to the next IE similar to the saccastical jumps of the eye into the fixed points inside IE. After the imaging of all IE on the display * It is worthwhile using signals similar to analogous signals in living beings. Maybe then physiological experiments of the blind men brain excitation would be possible. Such experiments could confirm the supposition that the brain receives generalized images of real objects [7]. 17
Volume
40, number
1
Fig. 4. The diagram
OPTICS COMMUNICATIONS
of a narrow-band
TV system.
of unit 11 a noise-free stylized image of the initial image is formed (“blanks” if any, can be filled by straight lines). Another example of using the method is the TV channel bandwidth narrowing. Fig. 3 shows the principal diagram of a narrow-band TV system [8] The transmitted image 1 is simultaneously applied to two TV tubes: through divider 8 to tube 2 and, after reflection at mirror 9, through the defocusing system 5 to tube 3. The reading beams of both tubes are synchronously controlled by the sweep unit 4. The amplitude discriminator 7 discriminates time intervals when the reading beam passes through the IE of a defocused image on the target of tube 3. In these intervals, a unity level is produced in the discriminator and fed to the sweep unit 4 to slow down the speed of reading beams in both tubes. Outside IE the beam speed does not change. The signal from discriminator 7 is mixed with the video signal in amplifier 6 and passes through modulator 10 to the antenna. At the receiving end the scan speed is synchronously changed Unlike the known narrow-band TV systems with retarded transmission of the whole image, in our system only the transmission of IE is slowed down. This
18
1 December
1981
allows a considerable increase in the transmission speed of the image as a whole with simultaneous narrowing of the TV channel bandwidth. The method has also been used for computer processing of noisy images of the fuel hollow spray [9]. Two images were put into the computer: a sharp and a defocused. In the latter the noise was completely suppressed and bright blurred spots were separated at the regions of the fuel drops existence. To determine the drop coordinates and dimensions the sharp image data taken from the regions separated in the defocused image only have been used.
References [l] V.M. Ginzburg, Optics Comm. 36 (1981) 258. [2] V.M. Ginzburg, Transactions Acad. Sci. 244 (1979) 580 (in russian). [3] V.M. Ginzburg, preprints of the 14-th ICHSP, Moscow (1980). i elec]4 ] V.M. Ginzburg and E.B. Levitov, Radiotekhnica tronika (Radio engineering and electronics) 26 (1981) 592. ]5 ] V.L. Rvachev and A.P. Slesarenko, The algebra of logic and integral transformations in boundary-value problems (Naukova Dumka, Kiev, 1976) (in russian). [6] V.M. Ginzburg, Radiotekhnica i electronika 25 (1980) 1288. [7] K. Pribram, Languages of the brain (Prentice-Hall, New Jersey). [S] V.M. Ginzburg, F.Ja. Nikolaev and B.M. Stepanov, patent no. 598270 of 11 July 1975, invention bulletin no. 10, 15.03.78 (in russian). [9] V.M. Ginaburg, G.G. Levin, E.V. Moroz and G.N. Pavlygin, in: Holographic methods and equipments used in physical investigation and their metrological control (Moscow, 1977) (in russian) p. 25.
ANTHROPOLOGICAL
OPTICS COMMUNICATIONS
1 December 1981
METHODS OF OPTICAL IMAGE PROCESSING
V.M. GINZBURG USSR State Committee for Standards, II 7049 Moscow, USSR
Received 17 August 1981
Some applications of the new method for optical Image processing, based on a prior separation of informative elements (IE) with the help of a defocusing equal to the average eye defocusing, considered in a previous paper, are described. A diagram of a “drawing” robot with the use of defocusing and other mechanisms of the human visual system (VS) is given. Methods of narrowing the .TV channel bandwidth and elimination of noises in computer image processing by prior image defocusing are described.
In a previous paper [l] it was shown that some facts known about the human visual system (VS) could be explained by the phenomenon of defocusing. This allowed the author to put forward a hypothesis on using the crystalline lens defocusing for the preliminary processing of the image in the VS. This hypothesis provided explanation of some functional relationships in the VS and was used as a basis for a new method of optical image processing [2,3] , In this paper some new method applications mentioned in [ 1] are considered, namely: the diagram of a “drawing” robot, a TV channel with a narrowed bandwidth and a method of eliminating noises in computer image processing [9]. To confirm the possibility of automatic system construction by the method described in [-1] a threedimensional T(x, y) function of the transparent transmission with the image of an angle defocused by 0.2 D (diopfrics) (fig. 2a in [l] ) was analysed in ref. [4] . Fig. la shows the function T(I), where I is a reverse line (the valley in T(x, JJ)). The coordinates (x, y) along 1 (in mm) counted from the point (0,O) at T,,(O, 0) = 1 are marked. Letters B and A denote the external boundary and inner edge of the angle, accordingly. One can see that function T(x, y) falls on the valleys to the value of T(I) < 0.5. Functions T(x) and dZ(x)/d.x for y = 0.4 mm (fig. lb) also change considerable at the reverse line. Hence, it is possible to use the reverse line for automatic separa0 030-4018/81/0000-0000/$02.75
0 1981 North-Holland
tion of informative elements (IE) from the defocused image. The separation of IE is accompanied by an abrupt spectrum narrowing. This leads to the generalisation of the image due to the elimination of the image fine structure. This phenomenon can be used for reducing the number of spatial filters in an optical holographic processor. For further reducing this number it was proposed to represent an image as a structure of elementary geometrical figures described by the simplest analytic reference functions [2] . The image as a whole is represented as a reference functions predicate, equal to unity inside the image and zero outside it [S] . For basic figures the “genetic” figures such as a “round spot” and a “strip” for which VS has inborn detectors were used in [2]. The predicate equations of genetic functions (GF) describing these figures are of the form: Q(Y,,) = LO - Yu - a(x - x&Y -a(x-xo+So/~))>O]
- Yo - S/m = 1,
L?(y,) = [(r2 - (x - x0)2 - 0, - ro)2)
(1)
> O] = 1. (2)
Here a = tan CK,where CYis the strip slope angle, S is the strip width, r is the circle radius, x0, y. are the coordinates of a fixed point at the strip or circle centre. When S -+ 0 the “strip” degenerates into a straight line. With the help of simple logic operations, an example of a basic set of secondary GF is made from functions (1) and (2), corresponding to simple 15
Volume
40, number
1
OPTICS COMMUNICATIONS
1 December
a
b
Fig. 1. The functions:
Fig. 2. A set of elementary
16
figures described
by genetic
functions.
T(I)-(a);
T(x)-(b).
1981
Volume 40, number 1
OPTICS COMMUNICATIONS
1 December 1981
Fig. 3. The “drawing” robot diagram. geometric figures (fig. 2). With their aid a stylized image of an arbitrary real contour image applied to the processor input can be “contructed”. The total number of these functions is unlikely to exceed ten. During the defocusing of the input image IE are separated, which are the defocused images of elementary figures described by GF. Hence, to synthesise a stylized (generalized) image at the processor output, it is sufficient to use spatial filters for the defocused images of figures described by GF. Fig. 3 shows the principal diagram of a robot “drawing” a generalized image of the real object image of the real object image illuminated by white light applied to the input 1 (plane 5,~) [6] . Unit 2 forms a defocused noise-free image with separated IE. Signals of coordinates of the brightest points in IE are in turn transmitted through channel 4 into the scanner unit 3. Beam 5 of unit 3 is directed onto the appropriate IE point and scans the IE region limited by the reverse line. The IE image illuminated by the coherent light beam 18 from laser 15 through divider 16 is displayed to a given scale on the dynamical transparent of unit 3 (for instance, on Titus). This image is processed in a holographic correlator incorporating multiplicator 6, objectives 7, and a set of spatial filters 8 made for defocused images of figures described by GF. Filter recording is performed with the help of scanner 17 which shapes reference
beams 19 in succession. At the correlator output reader 9 (a dissector or charge-coupled device) is installed. At its output, depending on the coordinates Xi, yi of brightest correlation spot, the coded signal * of a given GF is produced, which is transmitted to the computer 10 for GF formation by an appropriate formula. Signals from the computer output 10 are applied to the display 11. Simultaneously, signals from units 2 and 3 are applied to the display 11 through channels 12 and 13. They contain information on the relative scale, angular IE orientation in the initial image, and on the coordinates of the maximum brightness points in IE used for “clamping” to it coordinates of the fixed point ~0 JO in the predicate equation. After “drawing” the sharp image of an elementary figure corresponding to the identified IE on the display (plane <‘, n’), a signal of the completion of operation with the given IE is transmitted from unit 11 to unit 3 through channel 14. By this signal the scanner “jumps” to the next IE similar to the saccastical jumps of the eye into the fixed points inside IE. After the imaging of all IE on the display * It is worthwhile using signals similar to analogous signals in living beings. Maybe then physiological experiments of the blind men brain excitation would be possible. Such experiments could confirm the supposition that the brain receives generalized images of real objects [7]. 17
Volume
40, number
1
Fig. 4. The diagram
OPTICS COMMUNICATIONS
of a narrow-band
TV system.
of unit 11 a noise-free stylized image of the initial image is formed (“blanks” if any, can be filled by straight lines). Another example of using the method is the TV channel bandwidth narrowing. Fig. 3 shows the principal diagram of a narrow-band TV system [8] The transmitted image 1 is simultaneously applied to two TV tubes: through divider 8 to tube 2 and, after reflection at mirror 9, through the defocusing system 5 to tube 3. The reading beams of both tubes are synchronously controlled by the sweep unit 4. The amplitude discriminator 7 discriminates time intervals when the reading beam passes through the IE of a defocused image on the target of tube 3. In these intervals, a unity level is produced in the discriminator and fed to the sweep unit 4 to slow down the speed of reading beams in both tubes. Outside IE the beam speed does not change. The signal from discriminator 7 is mixed with the video signal in amplifier 6 and passes through modulator 10 to the antenna. At the receiving end the scan speed is synchronously changed Unlike the known narrow-band TV systems with retarded transmission of the whole image, in our system only the transmission of IE is slowed down. This
18
1 December
1981
allows a considerable increase in the transmission speed of the image as a whole with simultaneous narrowing of the TV channel bandwidth. The method has also been used for computer processing of noisy images of the fuel hollow spray [9]. Two images were put into the computer: a sharp and a defocused. In the latter the noise was completely suppressed and bright blurred spots were separated at the regions of the fuel drops existence. To determine the drop coordinates and dimensions the sharp image data taken from the regions separated in the defocused image only have been used.
References [l] V.M. Ginzburg, Optics Comm. 36 (1981) 258. [2] V.M. Ginzburg, Transactions Acad. Sci. 244 (1979) 580 (in russian). [3] V.M. Ginzburg, preprints of the 14-th ICHSP, Moscow (1980). i elec]4 ] V.M. Ginzburg and E.B. Levitov, Radiotekhnica tronika (Radio engineering and electronics) 26 (1981) 592. ]5 ] V.L. Rvachev and A.P. Slesarenko, The algebra of logic and integral transformations in boundary-value problems (Naukova Dumka, Kiev, 1976) (in russian). [6] V.M. Ginzburg, Radiotekhnica i electronika 25 (1980) 1288. [7] K. Pribram, Languages of the brain (Prentice-Hall, New Jersey). [S] V.M. Ginzburg, F.Ja. Nikolaev and B.M. Stepanov, patent no. 598270 of 11 July 1975, invention bulletin no. 10, 15.03.78 (in russian). [9] V.M. Ginaburg, G.G. Levin, E.V. Moroz and G.N. Pavlygin, in: Holographic methods and equipments used in physical investigation and their metrological control (Moscow, 1977) (in russian) p. 25.