Image processing apparatus and feature detection method
First Claim
Patent Images
1. An image processing apparatus, comprising:
- a processor configured to perform a process that arranges multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing and acquires features of multiple input images captured in different environments; and
a storage part configured to store a filter table that maintains the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property,wherein the process includesselecting, by referring to the filter table, one or more image filters within a depth restricted to a layer for each of layers;
generating a population that includes the multiple image filters being selected in the tree structure; and
conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers.
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Abstract
An image processing apparatus is disclosed. A processor performs a process that arranges multiple image filters in a tree structure and acquires features of multiple input images captured in different environments. A storage part stores a filter table that maintains information concerning the multiple image filters. In the process, an image filter having a different type for each of layers, in which multiple image processes are hierarchized, is selected from the filter table. A population, which includes the multiple image filters in the tree structure, is generated.
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Citations
12 Claims
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1. An image processing apparatus, comprising:
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a processor configured to perform a process that arranges multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing and acquires features of multiple input images captured in different environments; and a storage part configured to store a filter table that maintains the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property, wherein the process includes selecting, by referring to the filter table, one or more image filters within a depth restricted to a layer for each of layers; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers. - View Dependent Claims (2, 3)
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4. A feature detection method performed by a computer, the method comprising:
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performing, by the computer, a feature detection process for arranging multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing, and acquiring features of multiple input images captured in different environments, wherein the feature detection process includes selecting one or more image filters within a depth restricted to a layer for each of layers by referring to a filter table stored in a storage part, the filter table maintaining the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers.
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5. A non-transitory computer-readable recording medium storing a program which, when executed by a computer, causes the computer to perform a feature detection process for arranging multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing, and acquiring features of multiple input images captured in different environments, the feature detection process comprising:
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selecting one or more image filters within a depth restricted to a layer for each of layers by referring to a filter table stored in a storage part, the filter table maintaining the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers.
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6. An image processing apparatus for executing a process program, in which multiple filters are formed in a tree structure, with respect to multiple input images captured by an imaging device, the apparatus comprising:
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a processor configured to perform a process that arranges multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing and acquires features of multiple input images captured in different environments; and a storage part configured to store a filter table that maintains the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property, wherein the process includes classifying the multiple input images into clusters based on feature amounts concerning the multiple images at an initial learning stage; selecting a representative image close to a center of the feature amounts for each of the clusters; and generating the process program through learning employing genetic programming by using learning data, which include multiple representative images selected for the clusters, wherein the generating of the process program further includes selecting, by referring to the filter table, one or more image filters within a depth restricted to a layer for each of layers; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers. - View Dependent Claims (7, 8, 9, 10)
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11. An image processing method performed in a computer, the method comprising:
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performing, by the computer, a feature detection process for arranging multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing, and acquiring features of multiple input images captured in different environments, wherein the feature detection process includes classifying the multiple input images into clusters based on feature amounts concerning the multiple images at an initial learning stage; selecting a representative image close to a center of the feature amounts for each of the clusters; and generating the process program through learning employing genetic programming by using learning data, which include multiple representative images selected for the clusters, wherein the generating of the process program further includes selecting one or more image filters within a depth restricted to a layer for each of layers by referring to a filter table stored in a storage part, the filter table maintaining the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers.
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12. A non-transitory computer-readable recording medium storing a program which, when executed by a computer, causes the computer to perform a feature detection process for arranging multiple image filters in a tree structure hierarchized by an order of an image emphasis process, a threshold process, and a binary image processing, and acquiring features of multiple input images captured in different environments, the feature detection process comprising:
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classifying the multiple input images into clusters based on feature amounts concerning the multiple images at an initial learning stage; selecting a representative image close to a center of the feature amounts for each of the clusters; and generating the process program through learning employing genetic programming by using learning data, which include multiple representative images selected for the clusters, wherein the generating of the process program further includes selecting one or more image filters within a depth restricted to a layer for each of layers by referring to a filter table stored in a storage part, the filter table maintaining the multiple image filters, each of which is associated with a property indicating one of the image emphasis process, the threshold process, and the binary image processing, and a depth of a node restricted by the property; generating a population that includes the multiple image filters being selected in the tree structure; and conducting an evolutionary process using genetic programming, in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair, which restricts selection of the crossover pair for each of the layers, and a mutation process is conducted with respect to the population based on a second restriction rule of mutation, which restricts types of the mutation for each of the layers.
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Specification