A hinge that has a first mounting base for attachment to a first hinged object. The hinge also has first and second hinge members pivotally connected together. The first hinge member and the first base are configured and dimensioned for cooperatively positioning and aligning the first hinge member i
A hinge that has a first mounting base for attachment to a first hinged object. The hinge also has first and second hinge members pivotally connected together. The first hinge member and the first base are configured and dimensioned for cooperatively positioning and aligning the first hinge member in a plurality of mounted positions along the base length. At least one first locking member is associated with the first hinge member and the first base for locking the first hinge member to the first base in one of the mounted positions. The hinge may also be segmented. A positioning tool may be connected to at least the first base with an attachment portion and configured for positioning the first base and attachment portion on a first hinged object at a predetermined distance from the second hinge member.
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A hinge that has a first mounting base for attachment to a first hinged object. The hinge also has first and second hinge members pivotally connected together. The first hinge member and the first base are configured and dimensioned for cooperatively positioning and aligning the first hinge member i
A hinge that has a first mounting base for attachment to a first hinged object. The hinge also has first and second hinge members pivotally connected together. The first hinge member and the first base are configured and dimensioned for cooperatively positioning and aligning the first hinge member in a plurality of mounted positions along the base length. At least one first locking member is associated with the first hinge member and the first base for locking the first hinge member to the first base in one of the mounted positions. The hinge may also be segmented. A positioning tool may be connected to at least the first base with an attachment portion and configured for positioning the first base and attachment portion on a first hinged object at a predetermined distance from the second hinge member. ations using a device of fixed storage capacity for storing compressible data, such as video, images, audio, speech. 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.3. 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:&PHgr; 1=η2,0+η0,2;&PHgr; 2=4η1,12+(η2,0−η0,2)2;&PHgr; 3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;&PHgr; 4=(η3,0−η1,2)2+(η2,1−η0,3)2;&PHgr; 5=(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 &PHgr; 6=(η2,0−η0,2)[(η3,0−η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3) 4. 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:&PHgr; 1=η2,0+η0,2;&PHgr; 2=4η1,12+(η2,0−η0,2)2;&PHgr; 3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;&PHgr; 4=(η3,0−η1,2)2+(η2,1−η0,3)2; &PHgr; 5=(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 &PHgr; 6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3) 5. 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. 6. 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 p rocessor, 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.7. 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: &PHgr; 1=η2,0+η0,2;&PHgr; 2=4η1,12+(η2,0−η0,2)2;&PHgr; 3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;&PHgr; 4=(η3,0−η1,2)2+(η2,1−η0,3)2;&PHgr; 5−(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 &PHgr; 6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3) 8. The device as claimed in claim 7, wherein said image processor is further programmed to distinguish, before calculating said moments, a first set of pixels forming said image from a second set of pixels not part of said image by forming a connected component from a binarized version of a superimage containing both said first and second sets.9. The device as claimed in claim 7, wherein said image processor is further programmed to calculate at least one other shape-dependent feature based on feature points derived from said image.10. 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.11. 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; andforming 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.12. A method for classifying symbols in an image data stream containing symbols, said method co mprising 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:&PHgr; 1=η2,0+η0,2;&PHgr; 2=4η1,12+(η2,0−η0,2)2;&PHgr; 3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;&PHgr; 4=(η3,0−η1,2)2+(η2,1−η0,3)2;&PHgr; 5=(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 &PHgr; 6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3) 13. 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:&PHgr; 1=η2,0+η0,2;&PHgr; 2=4η1,12+(η2,0−η0,2)2;&PHgr; 3=(3η3,0−η1,2)2+(3η2,1−η0,3)2;&PHgr; 4=(η3,0−η1,2)2+(η2,1−η0,3)2;&PHgr; 5=(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 &PHgr; 6=(η2,0−η0,2)[(η3,0−3η1,2)2−(η2,1−η0,3)2]+4η1,1(η3,0+η1,2)(η2,1−η0,3) 14. The method as claimed in claim 13, wherein said symbol is a text character. The method in claim 9, wherein said hue controls a color of said filtering and said saturation controls a strength of said filtering.11. The method in claim 9, further comprising before said separating, converting said image into a linear exposure.12. The method in claim 11, further comprising converting said linear exposure to a logarithmic space.13. The method in claim 12, wherein said converting of said linear exposure to said logarithmic space allows said luminance channel to be altered independently from said chrominance channels.14. The method in claim 12, wherein said altering comprises creating a filtered RGB to YCrCb matrix and applying said matrix to said logarithmic space to produce a filtered logarithmic space.15. The method in claim 14, wherein said recombining comprises applying a YCrCb to RGB matrix to said filtered logarithmic space.16. A method of filtering a photographic image comprising: converting said image into a linear exposure; converting said linear exposure to a logarithmic space; separating said image into a luminance channel and a chrominance channels; altering said luminance channel by controlling saturation and hue to produce an altered luminance channel, wherein said altering comprises creating a filtered RGB to YCrCb matrix and applying said matrix to said logarithmic space to produce a filtered logarithmic space; and recombining said chrominance channels and said altered luminance channel, wherein said recombining comprises applying a YCrCb to RGB matrix to said filtered logarithmic space. 17. The method in claim 16, wherein said hue controls a color of said filtering and said saturation controls a strength of said filtering.18. The method in claim 16, wherein said converting of said linear exposure to said logarithmic space allows said luminance channel to be altered independently from said chrominance channels.19. A system for developing a photographic image comprising a filter applied to said image, said filter comprising: a divider adapted to separate said image into a luminance channel and chrominance channels; a saturation control and a hue control adapted to alter said luminance channel and to produce an altered luminance channel; and an adder adapted to recombine said chromaniance channels and said altered luminance channel. 20. The method in claim 19, wherein said hue control changes a color of said filter and said saturation control changes a strength of said filter.21. The method in claim 19, further comprising a converter adapted to convert said image into a linear exposure.22. The method in claim 21, wherein said converter further converts said linear exposure to a logarithmic space.23. The method in claim 22, wherein said converter allows said luminance channel to be altered independently from said chrominance channels.24. The method in claim 22, wherein said saturation input and said hue input produce a filtered RGB to YCrCb matrix and apply said matrix to said logarithmic space to produce a filtered logarithmic space.25. The method in claim 24, wherein said adder applies a YCrCb to RGB matrix to said filtered logarithmic space.
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이 특허에 인용된 특허 (23)
Schade Daniel J. (30831-13th Place S. Federal Way WA 98003), Adjustable hinge assembly.
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