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US 6,731,788 B1Granted
Symbol Classification with shape features applied to neural network
AssigneeKoninklijke Philips Electronics N.V.
InventorsLalitha Agnihotri, Nevenka Dimitrova
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Abstract
An image processing device and method for classifying symbols, such as text, in a video stream employs a back propagation neural network (BPNN) whose feature space is derived from size, translation, and rotation invariant shape-dependent features. Various example feature spaces are discussed such as regular and invariant moments and an angle histogram derived from a Delaunay triangulation of a thinned, thresholded, symbol. Such feature spaces provide a good match to BPNN as a classifier because of the poor resolution of characters in video streams.
Claims14
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least two shape-dependent features;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein:
- said image processor is programmed to identify feature points in said image;
- and said at least two shape-dependent features include a measure of an incidence of angles appearing in a triangulation of said feature points.
4 more elements
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least two shape-dependent features;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein:
- said image processor is programmed to identify feature points in said image and to form at least one of a Delaunay triangulation and a Voronoy diagram based on said feature points;
- and said at least two shape-dependent features include a histogram representing an incidence of angles appearing in said at least one of a Delaunay triangulation and Voronoy diagram.
4 more elements
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least two shape-dependent features;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein:
- said at least two shape-dependent features include at least one moment from the set:
- Φ1=η2,0+η0,2;
- Φ2=4η1,12+(η2,0−η0,2)2;
- Φ3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;
- Φ4=(η3,0−η1,2)2+(η2,1−η0,3)2;
- Φ5=(3η2,1−η0,3)(η2,1−η0,3)[3(η3,0−η1,2)2−3(η2,1−η0,3)2]+(η3,0−3η1,2)(η3,0−η1,2)[(η3,0−η1,2)2−3(η2,1−η0,3)2]
- and
- Φ6=(η2,0−η0,2)[(η3,0−η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3)
- where η p , q = M p , q M 0 , 0 γ and γ = 1 + p + q 2 .
11 more elements
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least two shape-dependent features;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein:
- said at least two shape-dependent features include the set of invariant moments:
- Φ1=η2,0+η0,2;
- Φ2=4η1,12+(η2,0−η0,2)2;
- Φ3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;
- Φ4=(η3,0−η1,2)2+(η2,1−η0,3)2;
- Φ5=(3η2,1−η0,3)(η2,1−η0,3)[3(η3,0−η1,2)2−3(η2,1−η0,3)2]+(η3,0−3η1,2)(η3,0−η1,2)[(η3,0−η1,2)2−3(η2,1−η0,3)2];
- and
- Φ6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3)
- where η p , q = M p , q M 0 , 0 γ and γ = 1 + p + q 2 .
11 more elements
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least one shape-dependent feature;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein said classifier is a text classifier and said feature space includes an angle histogram and at least one invariant moment.
2 more elements
A device for classifying symbols in an image data stream containing symbols, comprising:
- an image data storage unit with an input connected to capture data from said image data stream and an output;
- an image processor, connected to said image data storage unit output, programmed to detect an image coextensive with a symbol to be classified embedded therein;
- said image processor including a back propagation neural network (BPNN) trained on a feature space;
- said feature space including at least two shape-dependent features;
- said image processor being programmed to derive a feature vector from said image based on said feature space and to apply said feature vector to said BPNN to classify said symbol, wherein:
- said image processor is programmed to identify feature points in said image and to form at least one of a Delaunay triangulation and a Voronoy diagram based on said feature points;
- said derivation of said feature points includes thinning a binarized version of said image;
- and said at least two shape-dependent features include a histogram representing an incidence of angles appearing in said at least one of a Delaunay triangulation and a Voronoy diagram.
5 more elements
A device for classifying symbols in an image data stream containing symbols, comprising an image processor programmed to calculate invariant moments and applying them to a neural network, said moments including substantially at least the set:
- Φ1=η2,0+η0,2;
- Φ2=4η1,12+(η2,0−η0,2)2;
- Φ3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;
- Φ4=(η3,0−η1,2)2+(η2,1−η0,3)2;
- Φ5−(3η2,1−η0,3)(η2,1−η0,3)[3(η3,0−η1,2)2−3(η2,1−η0,3)2]+(η3,0−3η1,2)(η3,0−η1,2)[(η3,0−η1,2)2−3(η2,1−η0,3)2]
- and
- Φ6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3)
- where η p , q = M p , q M 0 , 0 γ and γ = 1 + p + q 2 .
5 more elements
A method for classifying symbols in an image data stream containing symbols, said method comprising the steps:
- training a back propagation neural network (BPNN) on a feature space including at least two shape-dependent features;
- capturing an image from a video data stream;
- detecting an image region coextensive with a symbol to be classified embedded therein;
- deriving a feature vector from said image based on said feature space;
- and
- applying said feature vector to said BPNN to classify said symbol,
- wherein said method further comprises the step:
- identifying feature points in said image,
- and wherein said at least two shape-dependent features include a measure of an incidence of angles appearing in a triangulation of said feature points.
6 more elements
A method for classifying symbols in an image data stream containing symbols, said method comprising the steps:
- training a back propagation neural network (BPNN) on a feature space including at least two shape-dependent features;
- capturing an image from a video data stream;
- detecting an image region coextensive with a symbol to be classified embedded therein;
- deriving a feature vector from said image based on said feature space;
- and
- applying said feature vector to said BPNN to classify said symbol,
- wherein said method further comprises the steps:
- identifying feature points in said image;
- and
- forming at least one of a Delaunay triangulation and a Voronoy diagram based on said feature points,
- and wherein said at least two shape-dependent features include a histogram representing an incidence of angles appearing in said at least one of a Delaunay triangulation and a Voronoy diagram.
8 more elements
A method for classifying symbols in an image data stream containing symbols, said method comprising the steps:
- training a back propagation neural network (BPNN) on a feature space including at least two shape-dependent features;
- capturing an image from a video data stream;
- detecting an image region coextensive with a symbol to be classified embedded therein;
- deriving a feature vector from said image based on said feature space;
- and
- applying said feature vector to said BPNN to classify said symbol, wherein:
- said at least two shape-dependent features include at least one moment from the set:
- Φ1=η2,0+η0,2;
- Φ2=4η1,12+(η2,0−η0,2)2;
- Φ3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;
- Φ4=(η3,0−η1,2)2+(η2,1−η0,3)2;
- Φ5=(3η2,1−η0,3)(η2,1−η0,3)[3(η3,0−η1,2)2−3(η2,1−η0,3)2]+(η3,0−η1,2)(η3,0−η1,2)[(η3,0−η1,2)2−3(η2,1−η0,3)2]
- and
- Φ6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3)
- where η p , q = M p , q M 0 , 0 γ and γ = 1 + p + q 2 .
12 more elements
A method for classifying symbols in an image data stream containing symbols, said method comprising the steps:
- training a back propagation neural network (BPNN) on a feature space including at least two shape-dependent features;
- capturing an image from a video data stream;
- detecting an image region coextensive with a symbol to be classified embedded therein;
- deriving a feature vector from said image based on said feature space;
- and
- applying said feature vector to said BPNN to classify said symbol, wherein:
- said at least two shape-dependent features include the set of invariant moments:
- Φ1=η2,0+η0,2;
- Φ2=4η1,12+(η2,0−η0,2)2;
- Φ3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;
- Φ4=(η3,0−η1,2)2+(η2,1−η0,3)2;
- Φ5=(3η2,1−η0,3)(η2,1−η0,3)[3(η3,0−η1,2)2−3(η2,1−η0,3)2]+(η3,0−3η1,2)(η3,0−η1,2)[(η3,0−η1,2)2−3(η2,1−η0,3)2]
- and
- Φ6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3)
- where η p , q = M p , q M 0 , 0 γ and γ = 1 + p + q 2 .
12 more elements
Dependencies are inferred from the claim text.