Automatic focusing system with response control using fuzzy interference

An automatic focusing system is arranged to use as a parameter a signal component which is extracted from an image signal and varies with the state of focus, to compute an evaluation value on the basis of a degree to which the signal component satisfies preset conditions or rules, and to adjust focus on the basis of the result of the computing operation.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a video camera and more particularly to the 
automatic focusing (hereinafter occasionally abbreviated to AF) system of 
a video camera. 
2. Description of the Related Art 
The video apparatuses such as video cameras have conspicuously advanced 
during recent years. Their sizes have been reduced. They have come to be 
arranged to more automatically operate and to have more versatile 
functions. As a result of the advancement, almost all the video cameras 
are normally provided with an automatic focus adjustment (or focusing) 
system these days. 
Automatic focus adjustment systems are provided in varied kinds. Unlike a 
still camera which is arranged to be used in taking a still picture, a 
video camera is required to be continuously focused on a moving object. It 
is, therefore, essential for the video camera to be capable of retaining 
its in-focus state for a moving object. 
The performance of the automatic focusing system may be evaluated, in this 
respect, in terms of stability and quick responsivity. The stability means 
that a focusing lens is not unnecessarily operated. In other words, it 
means that no faulty action that causes a blur by unnecessarily moving the 
focusing lens for focusing occurs. The quick responsivity means that the 
focusing lens is promptly moved to an in-focus position by correctly 
determining the direction and speed of the focusing action. The automatic 
focusing system for a motion picture must be arranged to meet these 
requirements in a well balanced state and to be capable of responding to 
any change of an image at a speed apposite to the change. To meet these 
requirements, the system must have information on the current state of 
focus and also accurate control information on the operating direction and 
speed of a focusing motor. 
It is a general tendency of these days that the video camera is arranged to 
extract a signal component which varies with the state of focus from an 
image signal and to adjust focus on the basis of the signal component 
extracted. This method permits focus adjustment irrespective of the 
distance at which a photographed object is located. The automatic focusing 
systems of the kind obtaining the above-stated information from the image 
signal can be roughly divided into two kinds. One employs an optical path 
modulating method which detects focus by modulating an optical path. The 
other uses a trial method. 
In the modulating method, the optical path is modulated by periodically 
vibrating a lens or an image sensor or the like by means of a 
piezoelectric element or the like. Information on the result of a 
discrimination made between a near-focus state, a far-focus state and an 
in-focus state is thus actively obtained. While it is an advantage of this 
method that information on the current state of focus and on the driving 
direction of the focusing motor can be accurately and promptly obtained, a 
disadvantage of it lies in the addition of the piezoelectric element and a 
driving circuit for it, which necessitates a complex structural 
arrangement and an increase in cost of the automatic focusing (AF) system. 
In the case of the trial method, AF control information on the focusing 
lens shifting direction and a focused state, etc., is obtained from 
changes caused in the image signal by driving the focusing motor. That 
method is called the trial method as the focusing lens is first 
tentatively moved to a very small extent. Unlike the modulation method, 
the trial method permits preparation of the AF system at a low cost as it 
requires no complex arrangement. However, it is a disadvantage of the 
trial method that it requires a longer operating time than the modulation 
method. Another disadvantage of the method resides in an increased 
probability that a temporal change taking place in the image is 
undistinguishable from a change brought about by the tentative (or trial) 
focusing action. Therefore, the control information obtained by that 
method tends to become ambiguous. 
In the event of binary control performed by simply comparing the focus 
control information with a threshold value, a faulty determination would 
often be made in accordance with the trial method, if restart of focusing 
is determined after attainment of an in-focus state. In such a case, the 
focusing motor would restart despite the in-focus state to greatly degrade 
picture quality by blurring an image from an in-focus state. 
Patent applications filed prior to the present invention relative to 
automatic focusing include, among others, U.S. Pat. No. 4,762,986 and U.S. 
Pat. No. 4,804,831 and U.S. patent applications Ser. No. 017,183 filed on 
Feb. 19, 1987, Ser. No. 046,252 filed on May 5, 1987 and Ser. No. 121,624 
filed on Nov. 17, 1987. 
SUMMARY OF THE INVENTION 
This invention is directed to the solution of the above-stated problems of 
the prior art. It is, therefore, a first object of the invention to 
provide an automatic focusing system which is capable of always accurately 
continuing a focusing action on any object that changes in a complex 
manner. 
It is a second object of the invention to provide an automatic focusing 
system which is of the kind obtaining focus control information from an 
image signal and is capable of performing optimum control by making a 
fuzzy inference in processing ambiguous information. 
To attain this object, an automatic focusing system arranged as a preferred 
embodiment of the invention comprises: detecting means for detecting from 
an image signal a signal component which varies with the state of focus; 
computing means for computing and producing output control information by 
computing a degree to which information based on the signal component 
detected by the detecting means conforms to preset conditions; and focus 
control means for adjusting focus on the basis of an output of the 
computing means. 
It is a third object of the invention to provide an automatic focusing 
system which is of the kind obtaining focus control information from an 
image signal and is arranged to be capable of appositely determining a 
restart of focusing by evaluating information in a state of being allowed 
to include ambiguities and by evaluating information of varied kinds in an 
organically combined state. 
It is a fourth object of the invention to provide an automatic focusing 
system which is of the kind obtaining focus control information from an 
image signal and of the trial type having many ambiguities included in the 
information, the system being arranged to be capable of high reliably and 
stably carrying out optimum control in a manner suited to the human 
sensation of the operator. The system is capable of appositely determining 
a restart of focusing by evaluating the information in a state of 
including ambiguities by a focus motor restart determining algorithm to 
which a fuzzy inference algorithm of evaluating information of varied 
kinds in an organically combined state is applied. This eliminates the 
possibilities that the focusing lens is not moved when there obtains an 
out-of-focus state and that the quality of images deteriorates because of 
a poor responsivity. 
To attain the fourth object, an automatic focusing system which is of the 
kind obtaining focus control information from an image signal and is 
arranged as a preferred embodiment of this invention comprises: focus 
control means for adjusting focus on the basis of a given signal component 
which is extracted from an image signal and varies with the state of 
focus; and restart determining means for restarting the focus adjustment 
according to a change in the state of focus after the focus adjusting 
action of the focus control means comes to a stop with an in-focus state 
attained, the restart determining means being arranged to compare, with 
preset conditions, detected information including the signal component 
which varies with the state of focus and to determine a restart of the 
focus adjusting action of the focus control means on the basis of a degree 
to which the detected information conforms to the preset conditions. 
It is a fifth object of the invention to provide an automatic focusing 
system which obtains focus control information from an image signal by a 
trial method including many ambiguities in the information and is capable 
of adequately performing a focusing action in a manner agreeable to the 
human sensation, the system being arranged to control the speed of a focus 
motor by an algorithm which evaluates the information in the state of 
including the ambiguities and being organically combined with other 
information of varied kinds. 
It is a sixth object of the invention to provide an automatic focusing 
system which is of the kind obtaining focus control information from an 
image signal and is arranged to control the speed of a focus motor with a 
fuzzy inference algorithm applied for the control. The arrangement enables 
the system to evaluate the state of focus by using focus detecting 
information in a state of including ambiguities and also in a state of 
being organically combined with information of varied kinds, so that a 
focusing speed can be set at a speed apposite to each of various 
situations. Therefore, the system has a good responsivity and is capable 
of performing optimum control over an automatic focusing action in a 
manner reliable, stable and apposite to the sensation of the operator. 
To attain the above-stated object, an automatic focusing system arranged as 
a preferred embodiment of the invention comprises: focus detecting means 
for detecting the state of focus on the basis of a signal component which 
is extracted from an image signal and varies with the state of focus; and 
speed control means for controlling the speed of focus adjustment on the 
basis of an output of the focus detecting means, the speed control means 
including computing means which is arranged to compare detected 
information on the signal component with conditions preset for the 
detected information and to set the speed of focus adjustment of the basis 
of a degree to which the detected information conforms to the preset 
conditions. 
It is a seventh object of the invention to provide an automatic focusing 
system wherein a plurality of pieces of information based on a signal 
component extracted from an image signal and indicating the states of 
focus are checked for the degree to which they conform to a plurality of 
preset rules; and the system is arranged never to be incapable of 
controlling even in cases where they fail to conform to any of the preset 
rules. 
To attain the above-stated object, an automatic focusing system which is 
arranged as a preferred embodiment of the invention comprises: focus 
detecting means for detecting the state of focus on the basis of a signal 
component which is extracted from an image signal and varies with the 
state of focus; speed control means arranged to compare detected 
information on the signal component with conditions preset for the 
detected information and to set a focus adjusting speed on the basis of a 
degree to which the detected information conforms to each of the preset 
conditions; and escaping means for escaping the control of the speed 
control means when the detected information fails to conform to any of the 
preset conditions. 
It is an eighth object of the invention to provide an automatic focusing 
system including detecting means for detecting a signal component which is 
extracted from an image signal and varies with the state of focus; speed 
control means for controlling a focus adjusting speed on the basis of 
information detected by the detecting means; and restart determining means 
for determining whether or not a focus adjusting action is to be resumed 
after an in-focus state is detected on the basis of the detected 
information. The system has at least one of the speed control means and 
the restart determining means arranged to use a computing algorithm for 
computing the amount of control on the basis of a degree to which the 
detected information conforms to preset rules. This arrangement enables 
the system to accurately perform focus control in a manner natural for the 
human sensation. 
It is a ninth object of the invention to provide an automatic focusing 
system which is arranged to perform optimum focus control by using a fuzzy 
inference for a control mode determining routine which determines a 
control mode between a focus control mode and a trial focusing mode 
according to the change of input information. 
These and other objects and features of the invention will become apparent 
from the following detailed description of embodiments thereof taken in 
connection with the accompanying drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The details of the automatic focusing system of this invention are 
described below through the embodiments thereof with reference to the 
drawings: 
FIG. 1 shows in a block diagram the arrangement of the automatic focusing 
system of a video camera embodying this invention as a first embodiment 
thereof. Referring to FIG. 1, incident light coming through a focusing 
lens 101 is converted into an image signal 103 by an image sensing block 
102 which consists of an image sensor and a signal processing circuit. The 
image sensor is a CCD or the like. The signal processing circuit is 
arranged to process the signal of the image sensor. The image signal 103 
is supplied to a signal processing circuit 105 directly thereto and also 
indirectly through a high-pass filter 104. The signal processing circuit 
105 is arranged to produce a high-frequency signal component which is used 
as focus information for determining the focused state of image, a 
normalized edge signal and a composite signal of them. 
The normalized edge signal indicates the width of the edge part of the 
contour or the like of the image of an object formed on the image sensing 
plane of the image sensor. The value of the edge signal decreases 
accordingly as the focusing lens comes closer to an in-focus point. This 
signal permits focus detection to be made at a high degree of accuracy as 
it is not affected by the contrast of the object. The details of 
arrangement for obtaining the edge signal has been disclosed in U.S. Pat. 
No. 4,804,831. Therefore, the arrangement is omitted from the following 
description. 
An A/D (analog-to-digital) converter 106 is arranged to A/D-convert these 
signals and to supply the digital signals thus obtained to a microcomputer 
107. The microcomputer 107 is arranged to determine, according to these 
signals, the driving speed of a focus motor M which is provided for 
driving the focusing lens and to control the focusing lens 101 through a 
focus driver 108. In determining the driving speed of the focus motor M, 
the microcomputer 107 computes a depth of field as sub-information. For 
this purpose, the microcomputer 107 obtains focal length data and aperture 
value data from a zoom encoder 109 and an iris encoder 110 respectively 
for use in computing the speed at which the focusing lens 101 is to be 
moved. This is because, in adjusting focus, the sensitivity of position of 
the focusing lens varies with the depth of field. 
The variation characteristics of the high-frequency signal component and 
the normalized edge signal which are obtained by the signal processing 
circuit 105 in relation to the position of the focusing lens are as shown 
in FIG. 2. Referring to FIG. 2, a reference numeral 201 denotes the 
variation characteristic of the high-frequency signal component of the 
image (video) signal and a numeral 203 that of the normalized edge signal. 
The above-stated high-frequency signal component is a component of a 
luminance signal extracted by the high-pass filter (HPF) 104. The 
normalized edge signal is obtained, as described in detail in U.S. Pat. 
No. 4,804,831 referred to above, Japanese Laid-Open Patent Application No. 
SHO 63-128878, etc., by computing a signal component corresponding to the 
width of the edge part of an object image obtained by normalizing, with 
the contrast of the object image, a differential value which is obtained 
by differentiating the high-frequency signal component having passed 
through the HPF. 
All these signals reach their peaks when an in-focus state is attained. The 
focus control, therefore, can be carried out as a rule by driving the 
focusing lens toward the peak in a hill-climbing manner. Further, the edge 
width signal reaches a minimum value at an in-focus point. The actual 
process is, therefore, carried out by using the reciprocal of it in such a 
way as to have the reciprocal at a maximum value at the in-focus point. 
A difference between the high-frequency signal component and the normalized 
edge signal lies in the steepness of peak in the neighborhood of the 
in-focus point. The normalized edge signal forms a steep peak only in the 
neighborhood of the in-focus point and seldom forms any hill like shape at 
points greatly deviating from the in-focus point. This gives reliable 
information for focus detection. Meanwhile, the high-frequency signal 
component information moderately forms a peak. Therefore, the 
high-frequency signal component information is set in such a way as to 
make the focusing lens driving direction readily determinable even in the 
event of an excessively blurred state of the image. 
A reference numeral 205 denotes a differential value of the normalized edge 
signal obtained when the focusing lens 101 is moved toward the infinite 
distance. This value comes to a peak in the neighborhood of the in-focus 
point. The differential value of the normalized edge signal is used in 
deciding the focusing lens to be brought to a stop when an in-focus state 
is attained. 
Broken line curves 202 and 204 represent the characteristics of the 
high-frequency signal component and the normalized edge signal obtained in 
the event of the deep depth of field respectively. In that event, their 
characteristic curves become flatter to show more moderate inclinations. 
In cases where the depth of field becomes deeper, therefore, the control 
data which is set on the basis of the normal characteristic curves 201 and 
203 must be corrected according to the change of inclination. 
Information on the normalized edge is further described as follows: FIG. 
3(a) shows an image signal representing an edge part of the image. FIG. 
3(b) shows the differential waveform of the image signal. A reference 
numeral 301 denotes the waveform of signal level obtained when the 
contrast of the object is high and a numeral 302 a waveform obtained in 
the event of a low contrast. The information necessary for focus 
determination is represented by the width .DELTA.x of a slanting part. 
Considering this on the differential waveform 303 of FIG. 3(b), the height 
.DELTA.h of the slanting part depends on the contrast. Therefore, the area 
S of a hill part 304 having the peak point at its summit also depends on 
the contrast. Considering this waveform to be a triangle, the area S can 
be expressed as follows: 
EQU .DELTA.S=.DELTA.x..DELTA.h 
Therefore, .DELTA.x=.DELTA.S/.DELTA.h 
The width .DELTA.x of the slanting part of the normalized edge which is 
independent of the contrast thus can be obtained. The signal processing 
circuit 105 performs this computing operation. However, since this circuit 
can be arranged in accordance with known arrangement as mentioned in the 
foregoing, the details of the circuit 105 are omitted from the description 
given herein. 
FIG. 4 shows in outline the control algorithm to be used in accordance with 
this invention. Fundamentally, there are two control loops. One is a focus 
motor control loop which is executed as follows: At a step 1, the focus 
motor is controlled. At a step 2, a check is made for the state of focus. 
If an in-focus state is found, the flow of control comes to a step 3 to 
bring the motor to a stop. If not, the flow comes back to the step 1 to 
continue the motor driving control. The other is a restart determining 
loop, which is executed in the following manner: With the focus motor 
brought to a stop at the step 3 after detection of the in-focus state at 
the step 2, the flow comes to a step 4 to determine whether the motor is 
to be restarted by making a check for deviation from an in-focus point. In 
the actual control process, one round of either of the two control loops 
is carried out per field. The control loop is changed from one loop over 
to the other according to the result of each checking and determining 
routine. 
The focus motor control routine of the step 1 is described as follows: This 
is a hill-climbing control action which is performed in accordance with 
the signal waveform of FIG. 2 as described in the foregoing. The direction 
in which the focusing lens is to be driven is determined on the basis of 
the high-frequency signal component. Then, an in-focus point is detected 
through the normalized edge signal. 
In this instance, there is the following problem: In actual shooting, a 
noise or the object causes a local peak in the waveform. In other words, a 
peak of waveform arises in a position not intended by the camera operator 
to bring about a faulty action which is performed in such a way as to 
bring the focusing lens to a stop while a desired focusing object (or main 
object) is still out of focus. This is a serious drawback of the system. 
To avoid this, the focus motor control input information must be processed 
through a filter, an averaging process, etc. in such a way as to ensure 
that the control is made to climb the maximum peak hill without fail and 
that the motor is not readily brought to a stop at a local peak. 
The motor stopping determination after attainment of an in-focus state is 
made by the in-focus state determining routine, i.e., a stop determining 
routine, of the step 2. This routine is executed as follows: The 
differential value 205 of the normalized edge signal which is as shown in 
FIG. 2 forms a peak immediately before the in-focus point. Therefore, a 
zero-crossing point obtained next to detection of the peak is assumed to 
be the in-focus point and the focus motor is brought to a stop 
accordingly. After stopping the focus motor, the flow of control comes to 
the restart determining loop of step 4. 
The restart determining loop is executed as follows: The degree of blur of 
the object image is detected. Whether the focus motor is to be restarted 
or not is decided according to the result of the blur detection. During 
the process of this loop, a discrimination must be accurately made between 
an in-focus state and a defocus state. If the probability of mistaking a 
blurred state for an in-focus state is high, the focus motor cannot be 
restarted despite the blurred state to give a serious result. If the 
probability of restarting the motor by mistaking an in-focus state for a 
defocus state is high, the focusing action becomes unstable to degrade the 
picture quality. To avoid this, in the restart determining loop, a 
discrimination between an in-focus state and a defocus state is made by 
changing the focusing lens to a very small extent after detection of any 
change of image through a change in the input information. The algorithm 
of this control operation is shown in FIG. 5. 
In FIG. 5, a step 10 shows a focus motor control loop which consists of the 
steps 1 and 2 of FIG. 4. In a case where the input information used for 
detecting the state of focus changes after the focus motor is brought to a 
stop with an in-focus state attained, this change is detected at a step 11 
by an input information change detecting routine. Then, the flow of 
control comes to a step 12 to make trial focus adjustment by a trial 
routine. In the trial routine, the focusing lens is tentatively moved to a 
slight extent in either direction. The current state of focus is detected 
by tentatively changing it. In other words, focus determining information 
is sampled by this action to find whether or not the current position of 
the focusing lens is located at the peak of the hill of the curve. 
This determining routine is further described as follows with reference to 
FIG. 6: Referring to FIG. 6, the level of the high-frequency signal 
component 404 obtained in the current focus position 401 and that of the 
normalized edge signal 405 are first sampled respectively. Next, the 
position of the focusing lens 101 is slightly moved in the direction of 
arrow (1) and the data of each of the signals (or the above-stated levels) 
is sampled at a focus position 402 obtained in the lens moving direction. 
After that, the lens is again slightly moved in the directions of arrows 
(2) and (3). Then, the data of each signal is likewise sampled. At the 
point 401, the data of each signal is sampled first and last and thus 
sampled twice. After sampling, the flow of control comes to a step 13. At 
the step 13: An in-focus state determining routine is executed by using 
the data obtained at the above-stated four sampling points. 
In the in-focus state determining routine of the step 13, an inferential 
discrimination is made between an in-focus state and a defocus state on 
the basis of the four pairs of data obtained by the above-stated trial 
process and a change or difference from the latest past data obtained in 
the in-focus state (i.e., the change of input information). When the lens 
is determined to be in an in-focus position, the flow comes back to the 
step 11 to make a check for any change of the input information. If no 
change is found, the flow comes to the step 14 to set the direction of 
restart by executing a restart direction determining routine. After the 
step 14, the flow comes back to the step 10 to restart the driving action 
on the focusing lens by executing the focus motor control loop. 
In the in-focus state determining routine, if there is no temporal change 
in the image, a set of data are determined in an upward convex pattern for 
a change in the state of focus as shown by way of example in FIG. 6. It 
is, however, important for an actual AF system to have an adequate dynamic 
characteristic, that is, to have an adequate responsivity to temporal 
changes taking place in the image and the input information. This relates 
not only to quick responsivity but also to the capability for repulsing 
unnecessary disturbances and to stably operate. 
In actuality, some temporal changes, etc., are included in the set of data 
obtained by the trial focus adjustment. To be exact, accurate focus 
determination is difficult in many cases. The factors possibly included in 
the set of data obtained by the trial focus adjustment are as listed 
below: 
(1) Changes in focus: Minute focus fluctuations resulting from the trial 
focus adjustment. 
(2) Temporal changes of image (changes in the object): Input information 
changes due to a change in contrast while the object distance remains 
unchanged. 
(3) Temporal changes of image (changes in distance). 
(4) Zooming, depth of field and noises. 
It is only the factor (1) that is necessary for determining an in-focus 
state. However, it is difficult to accurately distinguish these factors 
(1) to (3) and the noise (4) from each other. It is only possible to infer 
them from the pattern of the input data. In other words, such uncertain 
factors can be regarded as always existing in cases where the object image 
continues to change in actual shooting. 
To cope with such uncertain factors of the input data, the invented 
automatic focusing system is arranged to use the so-called fuzzy inference 
for the in-focus state determining routine of the step 13. FIG. 12 shows, 
as a table-1, some rules to be used in determining the state of focus on 
the basis of the fuzzy inference. 
The formula of each condition part of these rules is described with 
reference to FIGS. 7(a), 7(b) and 7(c) as follows: 
FIG. 7(a) shows on a time axis the data sampling pattern obtained by the 
trial focus adjustment. In FIG. 7(a), a reference numeral 501 denotes 
input data indicating the state of focus. In other words, the data 501 
gives information on the level of the high-frequency signal component or 
information on the normalized edge. Numerals 502 to 505 respectively 
denote the above-stated data sampling positions which temporally differ 
from each other. Hereinafter, these data are called data 1, data 2, data 3 
and data 4, respectively. A broken line 508 in FIG. 7(a) shows a change 
which is assumed to take place when the trial focus adjustment, i.e., the 
tentative minute focus adjusting action, is not performed. Numerals 507-1 
and 507-2 denote differences between the curve 508 and the data sampled by 
carrying out the trial focus adjustment. This indicates the factor 
resulting from the minute change in the state of focus, i.e., the minute 
movement of the focusing lens. However, these differences cannot be 
directly found. A numeral 506 denotes the amount of change taking place 
within a period of time between the start and the end of the trial focus 
adjustment. 
In the table-1, a condition part (1) means the amount of the above-stated 
change 506 of FIG. 7(a). The length of time required for focusing 
increases accordingly as the amount of change 506 becomes bigger. 
Therefore, it is inferable that the probability of an out-of-focus state 
is high in cases where this amount is big. 
A condition part (2) of the table-1 means a difference of the present input 
data value from input data value obtained at the latest point of time in 
the past in repeating the in-focus and stop determining process of the 
in-focus state determining routine, i.e., the motor stop determining 
routine of the focus motor control loop. The degree of change of the 
object may be considered to increase accordingly as this difference 
increases. It is, therefore, inferable that the probability of 
out-of-focus is high in cases where this difference is big. 
A condition part (3) of the table-1 corresponds to the illustration given 
in FIG. 7(b). FIG. 7(b) shows the input data sampling pattern with the 
focusing lens position taken on the axis of abscissa like in the case of 
FIG. 6. It indicates that an in-focus state is attained at the peak of 
hill of the curve. A value S1=data 2-data 1 and a value S2=data 3-data 1 
are of negative values while the initial point 502 of the trial focus 
adjustment is in a higher position to form a convex pattern toward above. 
In this instance, it is inferable that the hill is steeper and the degree 
of in-focus is greater accordingly as 
.vertline.S1.vertline.+.vertline.S2.vertline. is bigger than 0 to a 
greater extent. 
A condition part (4) of the table-1 means a case where the data pattern is, 
unlike the condition part (3), not in the upward convex shape and the lens 
can be considered to be almost in an in-focus position in the neighborhood 
of the peak of the pattern as shown by way of example in FIG. 7(c). 
Referring to FIG. 7(c), this is a case where the level of the input data 
obtained at the focus point 502 is higher than a focusable limit level 
509. In this case, the probability of an in-focus state increases 
accordingly as a value S+ in the positive direction is smaller and also a 
value S- in the negative direction is smaller (larger in absolute value). 
In the case of FIG. 7(c), data 2 which is obtained at a sampling point 503 
is larger than data 1 obtained at another sampling point 502. Therefore 
S1=S+and S2=S.increment.. However, this becomes S1=S- and S2=S+ in a case 
where the data 2 obtained at the sampling point 503 is smaller than the 
data 1 obtained at the point 502. 
The focusable limit 509 cannot be unconditionally determined and is 
unmeasurable. However, it is possible to infer, by S+, whether the data 
obtained at the current sampling point 502 is larger or smaller than the 
focusable limit 509. 
FIGS. 8(a) to 8(f) shows by way of example the shapes of the membership 
functions of this rule. The meaning of the membership function is apparent 
from the table-1. Referring to FIG. 8(a), functions 601 and 602 indicate 
the probability of a condition that a value .vertline.data 4-data 
1.vertline. is small or big. In FIG. 8(b), functions 603 and 604 indicate 
the probability of a condition that .vertline.the present data-the latest 
past data obtained at the time of in-focus.vertline. is small or big. In 
FIG. 8(c), functions 605 and 606 indicate the probability of a condition 
that a value .vertline.S1.vertline.+.vertline.S2.vertline. is small or 
big. In FIG. 8(d), functions 607 and 608 indicate the probability of a 
condition that the value S+ is small or big. In FIG. 8(e), functions 609 
and 610 indicate the probability of a condition that the value S- is small 
or big. In FIG. 8(f), output membership functions 611 and 612 indicate the 
probability of a condition for an out-of-focus state or an in-focus state. 
Normally, values which conform to the rules are substituted for the input 
membership functions of FIGS. 8(a) to 8(e). Then, in the last place, an 
AND condition is obtained from the output membership functions of FIG. 
8(f) and a centroid computing operation is carried out on functional areas 
which are formed on the output membership functions indicating the 
probability of satisfying the rules. 
The computing operation on the conclusion part shown in FIG. 8(f) may be 
carried out by using membership functions like in the generally practiced 
fuzzy inference. In other words, the probability of each input membership 
function as to its conformity with the applicable rule is first obtained; 
and then, the barycenter of its external shape on the applicable output 
membership function is obtained by collating it with output membership 
functions. This system is arranged to produce an output simply showing 
either an in-focus state or an out-of-focus state. Therefore, the output 
may be obtained by simply comparing the evaluation values of the condition 
parts. 
Next, the depth of field and the power zooming which define a shooting 
condition are described as follows: The depth of field is obtained by 
computation from a focal length detected by the zoom encoder 109 shown in 
FIG. 1 and an aperture value detected by the iris encoder 110. When the 
depth of field becomes deep, the high-frequency signal component and the 
normalized edge signal show moderate curves as represented by the curves 
202 and 204 in FIG. 2. In this case, the width of a signal change 
resulting from a change in the state of focus is small. More specifically, 
the values of changes in the data sampled during the process of the trial 
focus adjustment are smaller when the depth of field is deep. Therefore, 
these values must be corrected. For that purpose, the input sample data 
values are subjected to a scaling process in such a way as to make them 
fit for the set membership functions. This process may be changed to 
adjust by scaling the axis of abscissa of the membership function in such 
a way as to compensate for changes taking place in the depth of field. 
The depth of field becomes deeper at the time of power zooming, 
particularly on the wide-angle side. In that instance, the state of focus 
changes to a less degree in response to the same degree of movement of the 
focusing lens. Therefore, the speed of focusing must be increased for a 
higher following speed. For this purpose, the system is arranged to detect 
a power zooming action (zooming by means of a motor), to increase a 
defocus-state determining rate and to more readily restart the focus 
motor. To meet this requirement, the focusing rule of the table-1 is set 
as follows: Turning off of power zooming from a wide-angle position to a 
telephoto position (wide-to-tele) can be expressed by a binary value "1" 
or "0". The rule can be set as follows: 
IF "the degree of convexity toward above is big or near the peak" and 
"power zoom (wide.fwdarw.tele) off" 
THEN in focus 
Or, the restart determining loop of the flow of control of FIG. 5 may be 
changed to insert in between the steps 11 and 12 a power zoom 
(wide-to-tele) detecting routine as a step 15 as shown in FIG. 9. Then, 
the control is performed in accordance with the flow of control as shown 
in FIG. 9, in which the same parts as those of FIG. 5 are indicated by the 
same step numbers. 
Referring to FIG. 9, upon detection of power zooming from a telephoto 
position to a wide-angle position (tele-to-wide), the flow comes back to 
the step 10 which is a focus motor control loop. At the step 10, the focus 
motor is restarted. In a case where the state of focus is judged to be out 
of focus by an in-focus state determining routine, the flow comes to the 
step 14 to determine the direction of restart by executing a restart 
direction deciding routine. After that, the flow comes back to the step 10 
for the focus motor control loop. In this instance, the value of data 2 
and that of data 3 which are sampled during the process of the trial focus 
adjustment are compared with each other. Then, the restart is decided to 
be made in the direction of the bigger of the data 2 and 3. 
In the case of the system described above, the fuzzy inference is used only 
for determining an in-focus state in the restart determining (deciding) 
loop. However, it is possible to let an input-information-change detecting 
routine perform a power zoom detecting function and to make a fuzzy 
inference for that function. An example of such arrangement is shown in a 
flow chart in FIG. 10, in which the same parts as those of FIGS. 5 and 9 
are indicated by the same step numbers. 
Referring to FIG. 10, a step 16 is provided for the above-stated 
input-information-change detecting routine. The fuzzy inference rules for 
this routine are set as shown in a table-2 in FIG. 13. Membership 
functions corresponding to these rules are approximately shown in FIGS. 
11(a) and 11(b). 
In FIG. 11(a), functions 701 and 702 indicate the probability of a 
condition that the time of wide-to-tele power zooming is short or long. 
The probability is obtained by applying the rules of FIG. 13 to the 
membership functions as applicable and is used in computing an output. In 
FIG. 11(b), functions 703, 704 and 705 indicate the probability of a 
condition that an input information change, i.e., the absolute value of a 
difference between the present data and the latest past data obtained at 
the time of in-focus, is small, middle or big. The probability is obtained 
by applying the rules of FIG. 13 to the membership functions as applicable 
and is used in computing an output. 
Input data includes the time of wide-to-tele power zooming and the change 
of input information (.vertline.the present focus control data-the latest 
past data obtained at the time of in-focus.vertline.). 
In the table-2, a rule 1 is provided for improving the capability of 
following a focus change due to zooming in cases where the input 
information is changed by wide-to-tele power zooming. In accordance with 
this rule, the system promptly comes back to the focus motor control loop. 
The rule 2 of the table-2 is provided for making the trial focus 
adjustment if the time of wide-to-tele power zooming is short when the 
input information changes either to a middle degree or to a big degree. 
The rule 3 of the table-2 is provided for repeating the 
input-information-change detecting routine in a case where the input 
information changes to a small degree and the power zooming action is 
performed for a short period of time. 
The output of the system is obtained by comparing the evaluation values of 
the condition parts of these three rules and by selecting the biggest of 
the values. This enables the system to perform an optimum AF action in a 
manner apposite to any of the varied conditions. 
As described in the foregoing, the arrangement according to this invention 
enables an AF system of the kind obtaining focus control information from 
an image signal, even if the system employs the trial method which 
involves many ambiguities, to be capable of highly reliably and stably 
carrying out optimum control in a manner suited to the operability of the 
operator, because: The system appositely decides to restart focusing by 
evaluating the information in a state of including ambiguities by a focus 
motor restart determining algorithm and also by employing a fuzzy 
inference algorithm of evaluating information of varied kinds in an 
organically combined state. This eliminates the possibilities that the 
focusing lens is not moved when there obtains an out-of-focus state and 
that the quality of images deteriorates because of a poor responsivity. 
The foregoing description of the automatic focus adjusting action includes 
the control algorithm of restarting the focusing lens moving action after 
the focusing lens 101 is brought to a stop with an in-focus state 
obtained. It is, however, very important for carrying out the focus 
adjusting action in a natural manner to control the speed at which the 
focusing lens is driven. 
The focusing lens driving speed must be finely controlled according to the 
ambient conditions including the degree of focus, etc. Another embodiment 
of this invention is arranged to meet this requirement. The following 
describes the focusing lens driving speed control performed by the 
embodiment: 
This embodiment is arranged in the same manner as in the case of the first 
embodiment shown in FIG. 1. Therefore, the details of the arrangement are 
omitted from the following description. The control operation of this 
embodiment is carried out by a control program stored in a microcomputer 
107. Like the preceding embodiment, the focusing lens 101 is driven also 
under the hill climbing control according to the changes of the 
high-frequency signal component of the video (image) signal and the 
normalized edge signal which are taken out by the signal processing 
circuit 105. 
FIG. 14 shows the characteristics of changes of the high-frequency signal 
component and the normalized edge signal which take place in relation to 
the position of the focusing lens. The characteristics of these signal 
components are similar to those shown in FIG. 2. However, FIG. 14 shows 
the focusing lens driving speeds in addition to these characteristics. As 
shown, the focusing lens driving speed increases accordingly as the 
position of the lens deviates further from an in-focus point and decreases 
accordingly as it comes closer to the in-focus point. 
FIG. 15 shows in outline the control algorithm of this embodiment. The 
control operation of the embodiment can be divided into two basic control 
loops. Referring to FIG. 15, one of the control loop is executed in the 
following manner: At a step 20, the driving operation of the focus motor 
is controlled. At a step 21: A check is made for an in-focus state. If the 
lens is found to be in an in-focus position, the flow of control comes to 
a step 23 to bring the motor to a stop. If not, the flow proceeds to a 
step 22 to execute a zero escape routine. After that the flow comes back 
to the step 20 to continue the motor driving control. The other loop is a 
restart determining loop, which is executed as follows: To restart, if 
necessary, the focus motor which has been brought to a stop after an 
in-focus state is found at the step 21, the flow comes to a step 24 to 
determine whether or not the motor is to be restarted by making a check to 
find if the lens has deviated from the in-focus point. In actually 
carrying out the control, one round of either of these control loops is 
executed per field. One control loop is changed over to the other 
according to the result of each of the above-stated determining routines. 
The details of the focus motor control routine of the step 20 are as 
follows: The control is performed in the manner of climbing the hill of 
the signal waveform shown in FIG. 2 as mentioned in the foregoing. In 
actuality, the high-frequency signal component and the normalized edge 
signal which are input information signals are dependent on the object. 
The waveforms and levels of them vary with the object, the environment 
thereof, etc. The normalized edge signal is theoretically unaffected by 
the contrast of the object. However, in actuality, the edge component 
itself is sometimes a small object. Besides, in some cases, the edge 
signal is not accurately obtainable because of the adverse effect of the 
S/N ratio and the dynamic range of the circuit. Hence, the input 
information includes ambiguities. To cope with such ambiguous data, the 
system is arranged to control the focus motor by making a fuzzy inference. 
The algorithm of the control action is as described below: 
Ideal focus motor speed control is as shown in the lower part of FIG. 14. 
The speed is set at a high speed when the image is in a greatly blurred 
state deviating much from an in-focus state. The speed is changed from a 
middle speed to a low speed accordingly as the lens comes closer to an 
in-focus point; and the motor is preferably brought to a stop at the 
in-focus point. However, there is no clear boundary between one speed area 
and another. 
In accordance with the rules of the fuzzy inference, the relation of the 
speeds to the input information (or data) is set forth as shown by a 
table-3 shown in FIGS. 19(a) and 19(b). 
Referring to FIGS. 19(a) and 19(b), a rule 0 is provided for bringing the 
motor to a stop when the edge signal comes to its peak level (when an 
in-focus state is attained). A rule 1 (7) is provided for high-speed hill 
climbing control in the event of a big blur. A rule 2 (8) is provided for 
high-speed reverse rotation in case of a big blur. A rule 3 (9) is 
provided for middle-speed hill climbing control in case of a middle blur. 
A rule 4 (10) is provided for middle-speed reverse rotation in case of a 
middle blur. A rule 5 (11) is provided for low-speed hill climbing control 
in the neighborhood of an in-focus point. A rule 6 (12) is provided for 
low-speed reverse rotation in the neighborhood of an in-focus point. In 
the table-3, the rules 1 to 6 apply to cases where the focusing lens is 
shifted by the focus motor toward the infinite distance position. The 
rules 7 to 12 apply to cases where the focusing lens is shifted by the 
focus motor toward the nearest distance position. 
In each of the formulas of the condition parts ("IF" parts), the left side 
member of the formula represents input information and the right side 
member the membership function thereof. In each formula: "P-Small" and 
"P-Big" represent positive (P) values; "N-Small" and "N-Big" negative (N) 
values. In each of the output ("THEN") parts, the left side member of it 
represents the output information and the right side member its membership 
function. 
In the input information, the term "focus motor" as used in the "hill 
climbing" rule means the current (immediately before the inference is 
made) focus motor driving direction. The term "focus motor" as used in the 
"reverse rotation" rule means a direction in which the rotation of the 
focus motor is delayed for a period of time after effecting the reverse 
rotation of the motor and before the result of the reverse rotation 
appears in the input information. The right side member of the formula may 
be arranged to represent the membership function according to the accuracy 
of focus motor driving direction detecting means. It is also possible to 
use a binary value "1" or "0" for the right side member. 
FIGS. 16(a), 16(b) and 16(c) show in outline the membership functions 
corresponding to the input and output parts of the table-3. FIG. 16(a) 
shows the membership functions relative to the conditions of bigness and 
smallness of levels of the normalized edge signal and the high-frequency 
signal component. FIG. 16(b) shows the membership functions relative to 
the bigness, smallness and polarity (direction) of the differential values 
of the normalized edge signal and the high-frequency signal component. 
FIG. 16(c) shows the membership functions relative to the output, i.e., 
speed, of the focus motor. 
In a case where the input information is obtained as indicated by marks 
.DELTA. and .tangle-solidup., for example, a computing operation is 
performed as follows: The focus motor is assumed in this case to be 
operating in the direction of the infinite distance. 
In this case, the applicable rules are the rules 0, 1 and 3 of the table-3 
of FIG. 19(a). Other rules are not used as they are inapposite to this 
case. For the input value of each formula of each condition part, the 
crossing point of the membership function of the right hand side member of 
the formula is the evaluation value of the formula representing the rule. 
In this instance, each condition formula of each rule is in an && (AND) 
combination. Therefore, the minimum value of the formula becomes the 
evaluation value of the condition part. 
In the case of the rule 1, for example, the focus motor is driven toward 
the infinite distance point. Therefore, the degree (probability) of 
conformity to the condition that "the rotation of the focus motor is 
toward the infinite distance" is "1". The conforming degree of the actual 
level of the high-frequency component to the condition that "the 
high-frequency component level is small" is obtained in the following 
manner: The actual level value (indicated by the mark .tangle-solidup.) is 
applied to the function which is shown in FIG. 16(a) representing the 
condition that the high-frequency component is small. Then, the conforming 
degree becomes 0.5. Referring to FIG. 16(b), the conforming degree of the 
differential value of the high-frequency component to the condition that 
"the focus motor is driven in the positive direction and the differential 
value of the high-frequency component is small" can be likewise obtained 
from the function P-Small which represents this condition. The degree of 
conformity to this condition is thus obtained as 0.4 as shown in FIG. 
16(b). These processes can be summarized as shown below: 
RULE 1: 
Rotating direction of focus motor==Infinity: 1 
High-frequency signal==Small: 0.5 
Differential value of high-frequency signal==P-Small: 0.4 
Then, taking AND, the evaluation value becomes 0.4. 
In the case of the rule 0, the degree to which the normalized edge signal 
is big can be obtained as 0.4 from FIG. 16(a) and the degree to which the 
differential value of the normalized edge signal is "0" is obtained as 0.3 
from FIG. 16 (b). This can be summarized as follows: 
RULE 0: 
Normalized edge signal==Big: 0.4 
Differential value of normalized edge signal==Zo: 0.3 
Then, taking AND, the evaluation value becomes 0.3, 
A summary for the rule 3 is as follows: 
RULE 3: 
Focus motor==Infinite distance: 1 
High-frequency signal==Small: 0.5 
Differential value of high-frequency signal==P-Big: 0.6 
Differential value of normalized edge signal==P-Small: 0.7 
Then, taking AND, the evaluation value becomes 0.5 
The output obtained in the case of this example becomes as shown in FIG. 
17. FIG. 17 shows a computing operation performed for obtaining a focus 
lens driving speed output. In FIG. 17, a reference numeral 0 at a middle 
point of the drawing denotes a speed 0 which means that the focusing lens 
lies at rest. The focusing lens driving direction toward the infinite 
distance position is shown on the right hand side of the middle point 0. 
The lens driving speed increases accordingly as the point of output is 
further away in this direction. The focusing lens driving direction toward 
the nearest distance position is shown on the left hand side of the middle 
point 0. The lens driving speed increases accordingly as the output point 
is further away from the middle point 0 in that direction. In FIG. 17, 
full lines indicate values obtained by multiplying the output by the 
evaluation values of the condition parts described in the foregoing. The 
actual output is obtained at the barycenter point of the full line part 
which is indicated by a downward arrow mark. This barycenter point is 
determined by taking into consideration all the degrees of conformity to 
the above-stated applicable rules. This is an evaluation value most 
appositely and naturally representing the current conditions. Therefore, 
it is most apposite to drive the focusing lens toward the infinite 
distance position at the speed indicated by this barycenter point. 
The method for computing the condition-conforming-degree evaluating value 
and obtaining the output value is not limited to the above-stated method. 
They can be computed also by some other suitable methods. 
The above-stated control based on the fuzzy inference enables the system to 
perform natural focus control by smoothly controlling the speed and the 
direction of the focus motor relative to input information. 
A stop determining (or deciding) routine is described as follows: As 
mentioned in the foregoing, the normalized edge signal has a steep peak in 
the neighborhood of the in-focus point. Therefore, the differential signal 
of the edge signal also has a peak immediately before the in-focus point 
as indicated by the curve 205 in FIG. 14. Referring again to FIG. 15, the 
in-focus state determining routine, i.e., the stop deciding routine, of 
the step 21 is executed by detecting the peak waveform and then by 
bringing the focusing motor to a stop at a zero-crossing point which 
appears next to the peak. If the motor is decided to be stopped, the flow 
of control comes to the restart determining loop of the step 24. 
The zero escape routine of the step 23 is executed as follows: The rules of 
the above-stated fuzzy inference for the focus motor control routine do 
not always assure that one or more of these rules apply to any of various 
natural image shooting conditions. 
In other words, under some special conditions, any of the rules might fail 
to apply during the process of the motor control loop and the system might 
become incapable of coming out of the loop. The zero escape routine is 
provided against this sort of situation. In the zero escape routine, the 
flow of control is shifted to the restart determining loop upon detection 
of the zero speed of the focus motor. 
The details of the restart determining routine of the step 24 are as 
follows: The flow comes to the step 24 for the restart determining loop 
when the focus motor is brought to a stop with an in-focus state 
determined by the stop determining routine or when no rule of the fuzzy 
inference is found applicable by the zero escape routine. In the restart 
determining routine, a check is made for an in-focus state every time a 
change of the image is detected through a change taking place in the input 
information. If the lens is determined to be out of focus, the focus motor 
is restarted and the flow of control comes to the step 20 to execute the 
focus motor control loop. Further, in determining the lens to be in focus, 
the focus motor is tentatively moved to a slight extent to determine if 
the focusing lens is located at the peak of the hill in the same manner as 
in the case of the first embodiment described in the foregoing. 
As described above, the speed and direction of the focus lens can be 
smoothly controlled by the automatic focusing algorithm including the 
fuzzy inference. 
While the high-frequency signal component and the normalized edge signal 
are employed as the focus control information by the system described, the 
system according to this invention may be arranged to use the outputs of a 
plurality of high-pass filters in place of them. More specifically, a 
plurality of frequency bands may be extracted from the video signal, 
because: A very high-frequency component of the video signal is obtained 
in the neighborhood of an in-focus point while a low-frequency component 
corresponds to a greatly blurred part. 
An example of the above-stated arrangement is as shown in FIG. 18. The 
arrangement shown in FIG. 18 differs from the arrangement of FIG. 1 in the 
following point: A plurality of high-pass filters 701, 702, . . . are 
arranged to have different pass-band characteristics and to obviate the 
necessity of the signal processing circuit 105. In this case, the A/D 
converter 106 is arranged to be used in a time sharing manner. The 
characteristics of the high-pass filters are arranged in such a way as to 
form a steeper peak in the neighborhood of an in-focus point accordingly 
as the cut-off frequency of them is higher, so that the shape of their 
characteristics resembles that of the normalized edge signal. However, 
their dynamic range is much larger than that of the normalized edge 
signal. The inference algorithm is similar to the algorithm used for the 
normalized edge signal. In this case, the use of the normalized edge 
signal is replaced with the high-frequency signal component having a steep 
peak. Therefore, the membership function must be set according to the 
steep peak. 
The system dispenses with the signal processing circuit 105 which is used 
in signal processing for computing the normalized edge signal. This is an 
advantage of the system in terms of cost. 
As described in the foregoing, the automatic focusing system according to 
this invention uses the fuzzy inference algorithm for controlling the 
speed of the focus motor. The system is thus arranged to evaluate the 
state of focus by using information of varied kinds in an organically 
combined state with ambiguities allowed to be included in the focus 
detection information. This arrangement enables the system to set the 
focus adjustment speed at a speed apposite to any of various situations. 
The invented system is therefore capable of reliably and stably performing 
optimum control over the automatic focusing action with a good 
responsivity in a manner suited to the sensation of the operator. 
While, in one of the embodiments described, the fuzzy inference is applied 
to the restart determining process, the fuzzy inference is applied to the 
focusing adjustment speed control process in the case of another 
embodiment. However, it is, of course, possible to apply the fuzzy 
inference to both of these control processes. The arrangement according to 
this invention enables an automatic focusing system of the kind making 
focus adjustment for images that give information including many 
ambiguities, like in the case of a moving object in particular, to perform 
the control in a more natural manner than the conventional system by 
processing the ambiguous information as it is, instead of simply making a 
binary decision.