US 6,208,758 B1 · Method for learning by a neural network including extracting a target object image for which learning operations are to be carried out

A learning method for a neural network, comprising the steps of:

automatically determining the location of a target object image within an image for which learning operations are to be carried out;
extracting said target object image from said image,
iii) feeding a signal, which represents the extracted target object image, into a neural network, and
iv) carrying out the learning operations of said neural network in accordance with said input target object image.

1 more element

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by
cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
detecting a contour line of said target object image, which line extends in a predetermined direction, from said cut-out image,
extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as azimuth vectors,
composing a vector from said azimuth vectors, a vector for the travel of said view window being thereby determined,
causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

7 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
extracting a region, which approximately coincides in color with said target object image, from said cut-out image,
detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
detecting from said azimuth and said distance a vector for a travel of said view window,
causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

6 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image,
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween,
detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
b) detecting contour lines of object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
c) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
d) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for, being thereby obtained,
e) calculating the difference between the plurality of said images, in which the components of the movement of the background have been compensated for, a contour line of an object, which shows a movement different from the movement of the background, being thereby detected,
f) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
g) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as azimuth vectors,
h) composing a vector from said azimuth vectors, a vector for a travel of said view window being thereby determined,
i) causing the center point of said view window to travel in a direction heading towards said object in accordance with said vector for the travel of said view window, and
j) determining an extraction area, from which the target object image showing a movement with respect to the background is to be extracted, in accordance with the size and/or the shape of said object, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

18 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
b) detecting a contour line of said target object image, which line extends in a predetermined direction, from said cut-out image,
c) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
d) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
e) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
f) extracting a region, which approximately coincides in color with said target object image, from said cut-out image,
g) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
h) detecting said azimuth and said distance as a second travel vector
i) composing a vector from said first and second travel vectors, a vector for a travel of said view window being thereby determined,
j) causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

11 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the, extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image, said image being an image including a movement,
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween, and
detecting a contour line of said target object image, which line extends in a predetermined direction, from one of the plurality of said cut-out images,
b) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
c) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
d) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
e) detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
f) detecting contour lines of object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
g) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
h) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for, being thereby obtained,
i) calculating the difference between the plurality of said images, in which the components of the movement of the background have been compensated for, a contour line of an object, which shows a movement different from the movement of the background, being thereby detected,
j) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
k) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as second azimuth vectors,
l) composing a vector from said second azimuth vectors, a second travel vector being thereby determined,
m) composing a vector from said first and second travel vectors, a vector for a travel of said view window being thereby determined,
n) causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
o) determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

23 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image, said image being an image including a movement,
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween, and
detecting a contour line of said target object image, which line extends in a predetermined direction, from one of the plurality of said cut-out images,
b) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
c) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
d) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
e) detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
f) detecting contour lines of object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
g) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
h) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for, being thereby obtained,
i) calculating the difference between the plurality of said images, in which the components of the movement of the background have been compensated for, a contour line of an object, which shows a movement different from the movement of the background, being thereby detected,
j) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
k) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as second azimuth vectors,
l) composing a vector from said second azimuth vectors, a second travel vector being thereby determined,
m) extracting a region, which approximately coincides in color with said target object image, from one of the plurality of said cut-out images,
n) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
o) detecting said azimuth and said distance as a third travel vector,
p) composing a vector from said first, second, and third travel vectors, a vector for a travel of said view window being thereby determined,
q) causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
r) determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

26 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out a first image, which falls in a region inside of a view window having a predetermined size, from said image,
b) detecting a contour line of an object, which is embedded in said cut-out first image,
c) after a predetermined time has elapsed, cutting out a second image, which falls in the region inside of said view window, from said image,
d) detecting a contour line of an object, which is embedded in said cut-out second image,
e) calculating the difference between said contour line, which has been detected from said first image, and said contour line, which has been detected from said second image,
f) detecting a movement of a background from said calculated difference,
g) subtracting said detected movement of said background from said image, an object, which shows a movement different from the movement of said background, being thereby detected,
h) recognizing said object, which shows a movement different from the movement of said background, as said target object image,
i) detecting a vector directed towards said target object image as a first travel vector,
j) detecting a contour line of said target object image, which line extends in a predetermined direction, from said cut-out first image,
k) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
l) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as second azimuth vectors,
m) composing a vector from said second azimuth vectors, a second travel vector being thereby determined,
n) extracting a region, which approximately coincides in color with said target object image, from said cut-out first image,
o) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
p) detecting said azimuth and said distance as a third travel vector,
q) composing a vector from said first, second, and third travel vectors, a vector for a travel of said view window being thereby determined,
r) causing the center point of said view window to travel in accordance with said vector for the travel of said view window, and
s) determining an extraction area in accordance with the size and/or the shape of said target object image, the center point of said view window, which has thus been caused to travel, being taken as a reference during the determination of said extraction area.

19 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
b) detecting a contour line of said target object image, which line extends in a predetermined direction, from said cut-out image,
c) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
d) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as azimuth vectors,
e) composing a vector from said azimuth vectors, the composed vector being taken as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
f) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
g) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
h) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

8 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
b) extracting a region, which approximately coincides in color with said target object image, from said cut-out image,
c) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
d) detecting said azimuth and said distance as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
e) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
f) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

7 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image, said image being an image including a movement,
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween,
detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
b) detecting contour lines of object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
c) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
d) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for, being thereby obtained,
e) calculating the difference between the plurality of said images, in which the components of the movement of the background have been compensated for, a contour line of an object, which shows a movement different from the movement of the background, being thereby detected,
f) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
g) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as azimuth vectors,
h) composing a vector from said azimuth vectors, the composed vector being taken as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
i) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
j) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
k) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

19 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out a first image, which falls in a region inside of a view window having a predetermined size, from said image,
b) detecting a contour line of an object, which is embedded in said cut-out first image,
c) after a predetermined time has elapsed, cutting out a second image, which falls in the region inside of said view window, from said image,
d) detecting a contour line of an object, which is embedded in said cut-out second image,
e) calculating the difference between said contour line, which has been detected from said first image, and said contour line, which has been detected from said second image,
f) detecting a movement of a background from said calculated difference,
g) subtracting said detected movement of said background from said image, an object, which shows a movement different from the movement of said background, being thereby detected,
h) recognizing said object, which shows a movement different from the movement of said background, as said target object image,
i) detecting a vector directed towards said target object image as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
j) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
k) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
l) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

12 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) cutting out an image, which falls in a region inside of a view window having a predetermined size, from said image,
b) detecting a contour line of said target object image, which line extends in a predetermined direction, from said cut-out image,
c) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
d) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
e) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
f) extracting a region, which approximately coincides in color with said target object image, from said cut-out image,
g) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
h) detecting said azimuth and said distance as a second travel vector
i) composing a vector from said first and second travel vectors, the composed vector being taken as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
j) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
k) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
l) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

12 more elements

A learning method for a neural network, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image, said image being an image including a movement
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween, and
detecting a contour line of said target object image, which line extends in a predetermined direction, from one of the plurality of said cut-out images,
b) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
c) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
d) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
e) detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
f) detecting contour lines of object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
g) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
h) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating-direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for being thereby obtained,
i) calculating the difference between the plurality of said images, in which the components of the movement of the background have been compensated for, a contour line of an object, which shows a movement different from the movement of the background, being thereby detected,
j) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
k) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as second azimuth vectors,
l) composing a vector from said second azimuth vectors, a second travel vector being thereby determined,
m) composing a vector from said first and second travel vectors, the composed vector being taken as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
n) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
o) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
p) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

24 more elements

A learning method for a neural, comprising the steps of:

i) extracting a target object image, for which learning operations are to be carried out, from an image,
ii) feeding a signal, which represents the extracted target object image, into a neural network, and
iii) carrying out the learning operations of said neural network in accordance with said input target object image wherein the extraction of said target object image is carried out by:
a) setting a view window, which has a predetermined size, on said image, said image being an image including a movement,
cutting out a plurality of images, which fall in a region inside of said view window, at a plurality of times having a predetermined time difference therebetween, and
detecting a contour line of said target object image, which line extends in a predetermined direction, from one of the plurality of said cut-out images,
b) extracting all of components of said detected contour line, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said target object image,
c) detecting azimuths and intensities of said extracted components with respect to the center point of said view window, the azimuths and the intensities being detected as first azimuth vectors,
d) composing a vector from said first azimuth vectors, a first travel vector being thereby determined,
e) detecting contour lines of object images, which are embedded in the plurality of said cut-out images,
calculating the difference between images, which represent said detected contour lines, and
detecting a movement of said image in an in-plane parallel direction in the region inside of said view window, the movement being detected from said calculated difference,
f) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in a radial direction with respect to the center point of said view window,
calculating the difference between images, which represent said detected contour lines extending in the radial direction, and
detecting a movement of said image in an in-plane rotating direction in the region inside of said view window, the movement being detected from said calculated difference,
g) detecting contour lines of said object images, which are embedded in the plurality of said cut-out images, said contour lines extending in an annular direction,
calculating the difference between images, which represent said detected contour lines extending in the annular direction, and
detecting a movement of said image in the radial direction in the region inside of said view window, the movement being detected from said calculated difference,
h) compensating for components of a movement of a background in said cut-out images, which fall in the region inside of said view window, in accordance with said detected movement of said image in the in-plane parallel direction, in the in-plane rotating direction, and/or in the radial direction, a plurality of images, in which the components of the movement of the background have been compensated for, being thereby obtained,
i) calculating difference, from among the plurality of said images in which the components of the movement of the background have been compensated for, thereby detecting a contour line of an object, which shows a movement different from the movement of the background,
j) extracting all of components of said detected contour line of said object showing a movement different from the movement of the background, which are tilted at a predetermined angle with respect to circumferential directions of concentric circles surrounding the center point of said view window, from said detected contour line of said object showing a movement different from the movement of the background,
k) detecting azimuths and intensities of said extracted components of said detected contour line of said object, which shows the movement different from the movement of the background, with respect to the center point of said view window, the azimuths and the intensities being detected as second azimuth vectors,
l) composing a vector from said second azimuth vectors, a second travel vector being thereby determined,
m) extracting a region, which approximately coincides in color with said target object image, from one of the plurality of said cut-out images,
n) detecting an azimuth and a distance of said extracted region with respect to the center point of said view window,
o) detecting said azimuth and said distance as a third travel vector,
p) composing a vector from said first, second, and third travel vectors, the composed vector being taken as a gradient vector of a potential field in a Cartesian plane having its origin at the center point of said view window,
q) scanning the whole area of said image with said view window, thereby calculating the gradient vectors of the potential field with respect to the whole area of said image,
r) creating a map of the potential field of the whole area of said image from the gradient vectors of the potential field, which have been calculated with respect to the whole area of said image, and
s) determining an extraction area in accordance with the size and/or the shape of said target object image, a minimum point of the potential in said map being taken as a reference during the determination of said extraction area.

27 more elements

Method for learning by a neural network including extracting a target object image for which learning operations are to be carried out

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Method for learning by a neural network including extracting a target object image for which learning operations are to be carried out

Life of this patent

Abstract

Claims29