Method and apparatus of recognizing a moving object
First Claim
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1. A method for recognizing a moving three-dimensional object and for computing a description of motion of the object, said method comprising the steps of:
- detecting a physical property of the object to produce two-dimensional input image data;
applying the input image data to a bi-directional neural network comprising (I) pairs of velocity neurons for respectively obtaining x-and-y components of said optical flow and (ii) edge detecting line processes, each taking a value from a range between a high value and a low value in accordance with a function of the x and y components of optical flow and which is continuous within said range, interposed between said pairs of velocity neurons; and
obtaining an optical flow at respective points of said object based on the values taken by the line processes which optical flow provides the description of the motion of the object.
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Abstract
Disclosed is a moving object recognizing system, resistant to noise, which precisely obtains velocities, positions and configurations of an object moving in three-dimensional space. The system employs a bidirectional neural network including velocity neurons, coupled by twos and disposed, for respectively obtaining x-and-y components of an optical flow at respective points of the moving object and also line processes, interposed between the velocity neurons, for taking analog values for detecting edges of the moving object.
6 Citations
43 Claims
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1. A method for recognizing a moving three-dimensional object and for computing a description of motion of the object, said method comprising the steps of:
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detecting a physical property of the object to produce two-dimensional input image data; applying the input image data to a bi-directional neural network comprising (I) pairs of velocity neurons for respectively obtaining x-and-y components of said optical flow and (ii) edge detecting line processes, each taking a value from a range between a high value and a low value in accordance with a function of the x and y components of optical flow and which is continuous within said range, interposed between said pairs of velocity neurons; and obtaining an optical flow at respective points of said object based on the values taken by the line processes which optical flow provides the description of the motion of the object. - View Dependent Claims (2, 14, 15, 16, 32, 33)
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3. An apparatus for recognizing a moving object, comprising:
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means for detecting a physical property of the object to produce a two-dimensional image; means, connected to said means for projecting, for sampling the two-dimensional image at a plurality of points; a bidirectional neural network, having an input connected to said sampling means, and including velocity neurons for obtaining X-components of an optical flow at the respective points from the sampling data at the respective points, and velocity neurons for obtaining Y-components of said optical flow at the respective points from the sampling data at the respective points; first line process means, connected between said velocity neurons for obtaining said X-components, for detecting edges of said two-dimensional image and taking a value from a range between a low value and a high value according to a function of the X-components and which is continuous in the range; and second line process means, connected between said velocity neurons for obtaining said Y-components, for detecting the edges of said two-dimensional image and taking a value from a range between a low value and a high value according to a function of the Y-components and which is continuous in the range. - View Dependent Claims (4, 5, 17, 18, 34, 35)
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6. An apparatus for recognizing a moving three-dimensional object, comprising:
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means for detecting a physical property of the object to produce a two-dimensional image of the object wherein the image comprises sample data taken at a plurality of points in a two-dimensional plane; a bi-directional neural network connected to receive said sample data and including, for each point in the sample data, an X-component velocity neuron for obtaining an X-component of an optical flow at the point, and a Y-component velocity neuron, coupled to the X-component velocity neuron, for obtaining a Y-component of an optical flow at the point, and first line process means, connected between said X-component velocity neurons for detecting edges of said two-dimensional image and taking a value from a range between a low value and a high value according to a function of the X-component and which is continuous within the range; and second line process means, connected between said Y-component velocity neurons for detecting the edges of said two-dimensional image and taking a value from a range between a low value and a high value according to a function of the Y-component and which is continuous within the range. - View Dependent Claims (7, 8, 19, 20, 36, 37)
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9. A method for recognizing a three-dimensional moving object comprising the steps of:
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detecting a physical property of the object to produce two-dimensional image data of the object, the data being comprised of a plurality of points; determining an optical flow from the image data by using a bi-directional neural network including, for each point, an X-component velocity neuron for obtaining an X-component of an optical flow at the point, and a Y-component velocity neuron, for obtaining a Y-component of an optical flow at the point, and detecting edges of the image data using line processes, each taking a value from a range between a low value and a high value according to a function of the x and y components and which is continuous within the range, said line processes being interposed between a plurality of velocity neurons. - View Dependent Claims (21, 22, 23, 24, 38, 39)
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10. An apparatus for recognizing a moving three-dimensional object comprising:
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a camera interface for receiving a signal from a CCD camera providing a two-dimensional representation of the three-dimensional object; an image memory for storing the two-dimensional representation; and a central processing unit including means defining a bidirectional neural network including; velocity neurons for obtaining X components of an optical flow from corresponding points in the two-dimensional representation, velocity neurons for obtaining Y components of said optical flow from corresponding points in the two-dimensional representation, and line processes connected between the velocity neurons for obtaining the X components, for detecting edges of the two-dimensional image and each having a value taken from a range between a low value and a high value according to a function of the x components of the optical flow and which is continuous within the range. - View Dependent Claims (11, 12, 13, 25, 26)
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27. A method for determining optical flow of an object using a bidirectional neural network that is described by a general function, comprising the steps of:
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detecting a physical property of the object to produce image data; applying the image data to the bidirectional neural network; computing velocity vectors from the image data; computing statuses of line processes, as a function of the computed velocity vectors, of the bidirectional neural network; determining whether the general function converges; and when the general function does not converge, repeating both of the steps of computing and the step of determining, otherwise providing the velocity vectors and status of line processes as a description of the motion of the object. - View Dependent Claims (28, 29, 40, 41)
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30. A bidirectional neural network, comprising:
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a first set of velocity neurons, each for determining a component of flow in a first dimension of two-dimensional data; a second set of velocity neurons, each for determining a component of said flow in a second dimension of the two-dimensional data; line processes, each connected between two velocity neurons of the first set, for detecting edges of the two-dimensional image in the first dimension and each having a value taken from a first range between a low value and a high value according to a function of the components of the flow obtained by the velocity neurons, which function is continuous within the first range; and line processes connected between the velocity neurons of the second set, for detecting edges of the two-dimensional image in the second dimension and each having a value taken from a second range between a low value and a high value according to a function of the components of the flow obtained by the velocity neurons, which function is continuous within the second range.
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31. A method for determining a measure of flow in two-dimensional data of an object by using a bidirectional neural network, the bidirectional neural network having a general function and comprising a first set of velocity neurons adapted to determine a component of the flow in a first dimension, a second set of velocity neurons adapted to determine a component of the flow in a second dimension and line process elements, each connected between a pair of elements in one of the first and second dimensions and having a status determined according to a function of the components of the flow determined by the velocity neurons and which is continuous within a range between a high value and a low value, the method comprising the steps of:
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detecting a physical property of the object to produce image data; applying said image data to the velocity neurons to obtain velocity vectors; establishing the statuses of the line processes; evaluating the general function of the bidirectional neural network; and when the general function converges, obtaining the measure of the flow from the established statuses of the line processes. - View Dependent Claims (42, 43)
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Specification