Apparatus and method for classifying and recognizing image patterns using neural network
First Claim
1. An apparatus for classifying and recognizing an image received from an object, irrespective of scale and/or rotation of the image, the apparatus comprising:
- a complex-log mapping means having optical input elements arranged in a polar exponential manner, for mapping the image received from the object into an orthogonal coordinate system, and for providing an output processed in parallel in the orthogonal coordinate system, the output being invariant to the scale and/or rotation of the imagea second-order neural network means for multiplying the output of the complex-log mapping means to produce a multiplication output and for adding the multiplication output to produce an output invariant to a positional translation caused by the complex-log mapping means; and
a classifying and recognizing neural network means, having a neural network, for classifying and recognizing the output of the second-order neural network means.
1 Assignment
0 Petitions
Accused Products
Abstract
The present invention provides an apparatus and a method for classifying and recognizing image patterns using a second-order neural network, thereby achieving high-rate parallel processing while lowering the complexity. The second-order neural network, which is made of adders and multipliers, corrects positional translations generated in a complex-log mapping unit to output the same result for the same object irrespective of the scale and/or rotation of the object. The present invention enables high-rate image pattern classification and recognition based on parallel processing, which is the advantage obtained in neural network models, because consistent neural networks and consistent network structure computation models are applied to all steps from the image input step to the pattern classifying and recognizing step.
-
Citations
11 Claims
-
1. An apparatus for classifying and recognizing an image received from an object, irrespective of scale and/or rotation of the image, the apparatus comprising:
-
a complex-log mapping means having optical input elements arranged in a polar exponential manner, for mapping the image received from the object into an orthogonal coordinate system, and for providing an output processed in parallel in the orthogonal coordinate system, the output being invariant to the scale and/or rotation of the image a second-order neural network means for multiplying the output of the complex-log mapping means to produce a multiplication output and for adding the multiplication output to produce an output invariant to a positional translation caused by the complex-log mapping means; and a classifying and recognizing neural network means, having a neural network, for classifying and recognizing the output of the second-order neural network means. - View Dependent Claims (2, 3)
-
-
4. An apparatus for classifying and recognizing an image received from an object, irrespective of scale and/or rotation of the image, the apparatus comprising:
-
a complex-log mapping means having optical input elements arranged in a polar exponential manner, for mapping the image received from the object into an orthogonal coordinate system, and for providing an output processed in parallel in the orthogonal coordinate system, the output being invariant to the scale and/or rotation of the image; a second-order neural network means for ANDing and ORing the output of the complex-log mapping means, to produce an output invariant to a positional translation caused by the complex-log mapping means; and a classifying and recognizing neural network means, having a neural network, for classifying and recognizing the output of the second-order neural network means. - View Dependent Claims (5, 6, 7)
-
-
8. A method for classifying and recognizing an image received from an object through optical input elements disposed in a polar exponential arrangement, irrespective of scale and/or rotation of the image, the method comprising the steps of:
-
(a) mapping the image from the optical input elements to an orthogonal coordinate system to obtain an output irrespective of scale and/or rotation of the image; (b) multiplying values of the output from step (a) to obtain multiplication outputs; (c) adding the multiplication outputs from step (b) to obtain outputs irrespective of a position variation of the image; and (d) classifying and recognizing outputs from step (c). - View Dependent Claims (9)
-
-
10. A method for classifying and recognizing an image received from an object through optical input elements disposed in a polar exponential arrangement, irrespective of scale and/or rotation of the image, the method comprising the steps of:
-
(a) mapping the image from the optical input elements to an orthogonal coordinate system to obtain an output irrespective of scale and/or rotation of the image; (b) horizontally and/or vertically ANDing and ORing values of the output from step (a) using a second-order neural network to produce an output from the output from step (a), irrespective of the position of the image; and (c) classifying and recognizing the output from step (b). - View Dependent Claims (11)
-
Specification