Superresolution Metrology Methods based on Singular Distributions and Deep Learning
Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first...
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Format | Patent |
Language | English |
Published |
22.02.2024
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Online Access | Get full text |
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Abstract | Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first wavelength and a position of singularity is projected onto the physical object. Light excited by the singular light distribution that has interacted with the geometrical feature and that impinges upon a detector is detected and a return energy distribution is identified and quantified at one or more positions. A deep learning or neural network layer may be employed, using the detected light as direct input of the neural network layer, adapted to classify the scene, as a plurality of shapes, static or dynamic, the shapes being part of a set of shapes predetermined or acquired by learning. |
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AbstractList | Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first wavelength and a position of singularity is projected onto the physical object. Light excited by the singular light distribution that has interacted with the geometrical feature and that impinges upon a detector is detected and a return energy distribution is identified and quantified at one or more positions. A deep learning or neural network layer may be employed, using the detected light as direct input of the neural network layer, adapted to classify the scene, as a plurality of shapes, static or dynamic, the shapes being part of a set of shapes predetermined or acquired by learning. |
Author | Sirat, Gabriel Y |
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SubjectTerms | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING ANGLES MEASURING AREAS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS PHYSICS TESTING |
Title | Superresolution Metrology Methods based on Singular Distributions and Deep Learning |
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