US5497430A
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US
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Method and apparatus for image recognition using invariant feature signals
Assignee: Sanwa Bank California
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Filing: 1994-11-07
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Granted: 1996-03-05
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Expires: 2014-11-07
Abstract
Abstract A method of operating an image recognition system including providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator; initializing the indicator to an initialized state; obtaining an image of a structure; digitizing the image so as to obtain a plurality of input intensity cells and define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation-invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h[k,I(x,y)]dxdy, where I k is the set of n scale-, position- and rotation-invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h[k,I(x,y)] is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality of output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Inventors
- 👤 Lev S. Sadovnik
- 👤 Taiwei Lu
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-30 14:59
CPC Classifications
G; G06; G06V; G06V40/00; G06V40/10; G06V40/16; G06V10/00; G06V10/40; G06V10/42; G06V10/431
Claims Analysis
Total Claims: 23
Claim 1
Independent Claim
345 words
A method of operating an image recognition system comprising: providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator, said display being electrically connected to said neural network; initializing the indicator to an initialized state; obtaining an image of a structure; digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality of output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 2
Independent Claim
345 words
A method of operating an image recognition system comprising: providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator, said display being electrically connected to said neural network; initializing the indicator to an initialized state; obtaining an image of a structure; digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality of output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 3
Dependent Claim
58 words
The method of claim 1 wherein transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where ##EQU9## and ΔI is a quantization level.
Claim 4
Dependent Claim
56 words
The method of claim 1 wherein transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where h(k,I(x,y)) is a Bessel function.
Claim 5
Dependent Claim
47 words
The method of claim 1 further comprising providing a reference database, wherein transforming the set of structure recognition output signals to a structure classification signal includes updating the interconnection weight matrix based on a comparison of the set of structure recognition output signals with the reference database.
Claim 6
Dependent Claim
11 words
The method of claim 1 wherein h(k,I(x,y)) decreases as k increases.
Claim 7
Dependent Claim
58 words
The method of claim 4 wherein transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where ##EQU10## and ΔI is a quantization level.
Claim 8
Independent Claim
350 words
A method of operating an image recognition system comprising: providing a camera; providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator, said display being electrically connected to said neural network; initializing the indicator to an initialized state; obtaining an image of a structure from the camera; digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h[k,I(x,y)ldxdy , where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 9
Independent Claim
350 words
A method of operating an image recognition system comprising: providing a camera; providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator, said display being electrically connected to said neural network; initializing the indicator to an initialized state; obtaining an image of a structure from the camera; digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h[k,I(x,y)ldxdy , where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 10
Dependent Claim
26 words
The method of claim 7 wherein providing a camera includes providing an infra-red filtered camera and the image of the structure is an infrared filtered image.
Claim 11
Dependent Claim
38 words
The method of claim 7 further comprising providing a pulsed infrared illuminator electrically connected to the camera, wherein providing a camera includes providing an infra-red filtered camera and the image of the structure is an infra-red filtered image.
Claim 12
Dependent Claim
58 words
The method of claim 7 wherein transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where ##EQU11## and ΔI is a quantization level.
Claim 13
Dependent Claim
56 words
The method of claim 7 wherein transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where h(k,I(x,y)) is a Bessel function.
Claim 14
Dependent Claim
47 words
The method of claim 7 further comprising providing a reference database, wherein transforming the set of structure recognition output signals to a structure classification signal includes updating the interconnection weight matrix based on a comparison of the set of structure recognition output signals with the reference database.
Claim 15
Dependent Claim
11 words
The method of claim 5 wherein h(k,I(x,y)) decreases as k increases.
Claim 16
Dependent Claim
354 words
An image recognition system comprising: a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; a display including an indicator, said display being electrically connected to said neural network; means for initializing the indicator to an initialized state; means for obtaining an image of a structure; means for digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; means for transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; means for transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; means for transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; means for transforming the set of structure recognition output signals to a structure classification signal; and means for transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 17
Dependent Claim
354 words
An image recognition system comprising: a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; a display including an indicator, said display being electrically connected to said neural network; means for initializing the indicator to an initialized state; means for obtaining an image of a structure; means for digitizing the image so as to obtain a plurality of input intensity cells and thereby define an input object space; means for transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space according to a set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where I k is the set of n scale-, position- and rotation- invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, Ω is an area of integration of input intensity cells, and h(k,I(x,y)) is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; means for transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons of the neural network; means for transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; means for transforming the set of structure recognition output signals to a structure classification signal; and means for transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.
Claim 18
Dependent Claim
50 words
The image recognition system of claim 14 further comprising an infra-red filter optically connected to the means for obtaining an image, wherein the means for obtaining an image of a structure comprises an infra-red filtered camera and the image of the structure is an infra-red filtered image of a structure.
Claim 19
Dependent Claim
19 words
The image recognition system of claim 15 further comprising a pulsed infra-red illuminator synchronized with the infra-red filtered camera.
Claim 20
Dependent Claim
65 words
The image recognition system of claim 14 wherein the means for transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes means for normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where ##EQU12## and ΔI is a quantization level.
Claim 21
Dependent Claim
63 words
The image recognition system of claim 14 wherein the means for transforming the input object space to a feature vector by extracting the set of n scale-, position- and rotation- invariant feature signals from the input object space includes means for normalizing the input object space according to the set of relationships I k =∫.sub.Ω ∫I(x,y)h(k,I(x,y)) dxdy, where h(k,I(x,y)) is a Bessel function.
Claim 22
Dependent Claim
62 words
The image recognition system of claim 14 further comprising means for providing a reference database electrically connected to the neural network, wherein the means for transforming the set of structure recognition output signals to a structure classification signal includes means for updating the interconnection weight matrix based on a comparison of the set of structure recognition output signals with the reference database.
Claim 23
Dependent Claim
26 words
The image recognition system of claim 14 wherein said means for obtaining an image of a structure includes a camera and h(k,I(x,y)) decreases as k increases.
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| 3.5 Years (Year 4) | 1999-03-05 | 1999-09-04 | 2000-03-05 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-03-05 | 2003-09-04 | 2004-03-05 | $3760.00 | Window Closed / Lapsed |
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