Method for automatic detection and classification of objects and patterns in low resolution environments
First Claim
1. A method of automatically detecting and classifying objects, the method comprising:
- a. providing an original image comprising one or more objects;
b. creating a new image of each of said objects isolated from all other objects of said original image by performing a process of image separation;
c. extracting geometrical characteristics of the object from the new image, and storing them in a feature vector;
d. transforming at least one of said new images of said isolated object into a vector using at least one of erasing and shifting, wherein said transforming comprises reading the pixels of the new image vector by vector, where said vector contains information of said isolated object, and said vector comprises at least one consecutive background pixel for each vector of the at least one new image;
e. applying a 1 Dimensional Continuous Wavelet Transform to said vector;
f. extracting from said 1 Dimensional Continuous Wavelet Transform, both the scale, and coefficient values of said isolated object and storing said extracted values in said feature vector;
g. comparing said feature vector to at least one other feature vector from a data base; and
h. classifying each of said one or more objects responsive to said comparing of said feature vector.
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Abstract
The invention is a method of using Wavelet Transformation and Artificial Neural Network (ANN) systems for automatic detecting and classifying objects. To train the system in object recognition different images, which usually contain desired objects alongside other objects are used. These objects may appear at different angles. Different characteristics regarding the objects are extracted from the images and stored in a data bank. The system then determines the extent to which each inserted characteristic will be useful in future recognition and determines its relative weight. After the initial insertion of data, the operator tests the system with a set of new images, some of which contain the class objects and some of which contain similar and/or dissimilar objects of different classification. The system learns from the images containing similar objects of different classes as well as from the images containing the class objects, since each specific class characteristic needs to be set apart from other class characteristic. The system may be tested and trained again and again until the operator is satisfied with the system'"'"'s success rate of object recognition and classification.
10 Citations
21 Claims
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1. A method of automatically detecting and classifying objects, the method comprising:
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a. providing an original image comprising one or more objects; b. creating a new image of each of said objects isolated from all other objects of said original image by performing a process of image separation; c. extracting geometrical characteristics of the object from the new image, and storing them in a feature vector; d. transforming at least one of said new images of said isolated object into a vector using at least one of erasing and shifting, wherein said transforming comprises reading the pixels of the new image vector by vector, where said vector contains information of said isolated object, and said vector comprises at least one consecutive background pixel for each vector of the at least one new image; e. applying a 1 Dimensional Continuous Wavelet Transform to said vector; f. extracting from said 1 Dimensional Continuous Wavelet Transform, both the scale, and coefficient values of said isolated object and storing said extracted values in said feature vector; g. comparing said feature vector to at least one other feature vector from a data base; and h. classifying each of said one or more objects responsive to said comparing of said feature vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification