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Image processing with neural networks
Gróf, Zoltán ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
This bachelor’s thesis centralizes on the possible uses of neural networks in the field of computer vision. This work contains basic theoretic knowledge of the field of neural networks and image processing. It discusses how successfully can neural networks be applied through the separate steps of image processing, what kind of neural networks are suitable for these steps, and what are the problems that might appear with their use. The work discusses the fields of classification and image understanding in a more detailed level. It’s shown how the use of neural networks can be appropriate in these applications. An own program was created as part of this work to demonstrate the classification capabilities of neural networks. It’s shown a neural network is created and trained for the recognition of handwritten numbers. The trained neural network was subject to different tests, through which the conclusion was reached, that it works with a high success rate, but is sensitive to changes in the input objects: change of size and location. A number of possible solutions were designed for this problem.
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Image processing with neural networks
Gróf, Zoltán ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
This bachelor’s thesis centralizes on the possible uses of neural networks in the field of computer vision. This work contains basic theoretic knowledge of the field of neural networks and image processing. It discusses how successfully can neural networks be applied through the separate steps of image processing, what kind of neural networks are suitable for these steps, and what are the problems that might appear with their use. The work discusses the fields of classification and image understanding in a more detailed level. It’s shown how the use of neural networks can be appropriate in these applications. An own program was created as part of this work to demonstrate the classification capabilities of neural networks. It’s shown a neural network is created and trained for the recognition of handwritten numbers. The trained neural network was subject to different tests, through which the conclusion was reached, that it works with a high success rate, but is sensitive to changes in the input objects: change of size and location. A number of possible solutions were designed for this problem.
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