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Structural Methods of Objects Identification for Industrial Robot Operation
Minařík, Martin ; Šlapal, Josef (referee) ; Konečný, Vladimír (referee) ; Šťastný, Jiří (advisor)
This PhD thesis deals with the use of structural methods of objects identification for industrial robots operation. First, the present state of knowledge in the field is described, i.e. the whole process of objects recognition with the aid of common methods of the syntactic analysis. The main disadvantage of these methods is that is impossible to recognize objects whose digitalized image is corrupted in some ways (due to excessive noise or image disturbances), objects are therefore deformed. Further, other methods for the recognition of deformed objects are described. These methods use structural description of objects for object recognition, i.e. methods which determine the distance between attribute descriptions of images. The core part of this PhD thesis begins in Chapter 5, where deformation grammars, capable of description of all possible object deformations, are described. The only complication in the analysis is the ambiguity of the deformation grammar, which lowers the effectiveness of the analysis. Further, PhD thesis deals with the selection and modification of a proper parser, which is able to analyze a deformation grammar effectively. Three parsers are described: the modified Earley parser, the modified Tomita parser and the modified hybrid LRE(k) parser. As for the modified Earley’s parser, ways of its effective implementation are described. One of the necessary parts of the object recognition is providing the invariances, which this PhD thesis covers in detail, too. Finally, the results of described algorithms are mentioned (successfulness and speed of deformed objects recognition) and suggested testing environment and implemented algorithms are described. In conclusion, all determined possibilities of deformation grammars and their results are summarized.
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Query by Pictorial Example
Vácha, Pavel ; Haindl, Michal (advisor) ; Drbohlav, Ondřej (referee) ; Geusebroek, Jan-Mark (referee)
Ongoing expansion of digital images requires new methods for sorting, browsing, and sear- ching through huge image databases. This is a domain of Content-Based Image Retrieval (CBIR) systems, which are database search engines for images. A user typically submit a query image or series of images and the CBIR system tries to find and to retrieve the most similar images from the database. Optimally, the retrieved images should not be sensitive to circumstances during their acquisition. Unfortunately, the appearance of natural objects and materials is highly illumination and viewpoint dependent. This work focuses on representation and retrieval of homogeneous images, called textu- res, under the circumstances with variable illumination and texture rotation. We propose a novel illumination invariant textural features based on Markovian modelling of spatial tex- ture relations. The texture is modelled by Causal Autoregressive Random field (CAR) or Gaussian Markov Random Field (GMRF) models, which allow a very efficient estimation of its parameters, without the demanding Monte Carlo minimisation. Subsequently, the estimated model parameters are transformed into the new illumination invariants, which represent the texture. We derived that our textural representation is invariant to changes of illumination intensity and...
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Query by Pictorial Example
Vácha, Pavel ; Haindl, Michal (advisor)
Ongoing expansion of digital images requires new methods for sorting, browsing, and sear- ching through huge image databases. This is a domain of Content-Based Image Retrieval (CBIR) systems, which are database search engines for images. A user typically submit a query image or series of images and the CBIR system tries to find and to retrieve the most similar images from the database. Optimally, the retrieved images should not be sensitive to circumstances during their acquisition. Unfortunately, the appearance of natural objects and materials is highly illumination and viewpoint dependent. This work focuses on representation and retrieval of homogeneous images, called textu- res, under the circumstances with variable illumination and texture rotation. We propose a novel illumination invariant textural features based on Markovian modelling of spatial tex- ture relations. The texture is modelled by Causal Autoregressive Random field (CAR) or Gaussian Markov Random Field (GMRF) models, which allow a very efficient estimation of its parameters, without the demanding Monte Carlo minimisation. Subsequently, the estimated model parameters are transformed into the new illumination invariants, which represent the texture. We derived that our textural representation is invariant to changes of illumination intensity and...
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Query by Pictorial Example
Vácha, Pavel ; Haindl, Michal (advisor)
Ongoing expansion of digital images requires new methods for sorting, browsing, and sear- ching through huge image databases. This is a domain of Content-Based Image Retrieval (CBIR) systems, which are database search engines for images. A user typically submit a query image or series of images and the CBIR system tries to find and to retrieve the most similar images from the database. Optimally, the retrieved images should not be sensitive to circumstances during their acquisition. Unfortunately, the appearance of natural objects and materials is highly illumination and viewpoint dependent. This work focuses on representation and retrieval of homogeneous images, called textu- res, under the circumstances with variable illumination and texture rotation. We propose a novel illumination invariant textural features based on Markovian modelling of spatial tex- ture relations. The texture is modelled by Causal Autoregressive Random field (CAR) or Gaussian Markov Random Field (GMRF) models, which allow a very efficient estimation of its parameters, without the demanding Monte Carlo minimisation. Subsequently, the estimated model parameters are transformed into the new illumination invariants, which represent the texture. We derived that our textural representation is invariant to changes of illumination intensity and...
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Query by Pictorial Example
Vácha, Pavel ; Haindl, Michal (advisor) ; Drbohlav, Ondřej (referee) ; Geusebroek, Jan-Mark (referee)
Ongoing expansion of digital images requires new methods for sorting, browsing, and sear- ching through huge image databases. This is a domain of Content-Based Image Retrieval (CBIR) systems, which are database search engines for images. A user typically submit a query image or series of images and the CBIR system tries to find and to retrieve the most similar images from the database. Optimally, the retrieved images should not be sensitive to circumstances during their acquisition. Unfortunately, the appearance of natural objects and materials is highly illumination and viewpoint dependent. This work focuses on representation and retrieval of homogeneous images, called textu- res, under the circumstances with variable illumination and texture rotation. We propose a novel illumination invariant textural features based on Markovian modelling of spatial tex- ture relations. The texture is modelled by Causal Autoregressive Random field (CAR) or Gaussian Markov Random Field (GMRF) models, which allow a very efficient estimation of its parameters, without the demanding Monte Carlo minimisation. Subsequently, the estimated model parameters are transformed into the new illumination invariants, which represent the texture. We derived that our textural representation is invariant to changes of illumination intensity and...
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Structural Methods of Objects Identification for Industrial Robot Operation
Minařík, Martin ; Šlapal, Josef (referee) ; Konečný, Vladimír (referee) ; Šťastný, Jiří (advisor)
This PhD thesis deals with the use of structural methods of objects identification for industrial robots operation. First, the present state of knowledge in the field is described, i.e. the whole process of objects recognition with the aid of common methods of the syntactic analysis. The main disadvantage of these methods is that is impossible to recognize objects whose digitalized image is corrupted in some ways (due to excessive noise or image disturbances), objects are therefore deformed. Further, other methods for the recognition of deformed objects are described. These methods use structural description of objects for object recognition, i.e. methods which determine the distance between attribute descriptions of images. The core part of this PhD thesis begins in Chapter 5, where deformation grammars, capable of description of all possible object deformations, are described. The only complication in the analysis is the ambiguity of the deformation grammar, which lowers the effectiveness of the analysis. Further, PhD thesis deals with the selection and modification of a proper parser, which is able to analyze a deformation grammar effectively. Three parsers are described: the modified Earley parser, the modified Tomita parser and the modified hybrid LRE(k) parser. As for the modified Earley’s parser, ways of its effective implementation are described. One of the necessary parts of the object recognition is providing the invariances, which this PhD thesis covers in detail, too. Finally, the results of described algorithms are mentioned (successfulness and speed of deformed objects recognition) and suggested testing environment and implemented algorithms are described. In conclusion, all determined possibilities of deformation grammars and their results are summarized.
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