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Panoramatic View Reconstruction
Kuzdas, Oldřich ; Kohoutek, Michal (referee) ; Říha, Kamil (advisor)
This paper deals step by step with process of stitching images taken by perspective camera rotated by its optical center into the panoramic image. There are described keypoint searching algorhytms, possibilities of calculating homography matrix and methods of eliminating unwanted seams between source images in final panoramic image. A part of this paper is also standalone application in which are implemented some algorhytms described in the work.
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Automorphing of the Image Pairs
Čermák, Pavel ; Nečas, Ondřej (referee) ; Beran, Vítězslav (advisor)
This thesis I deal with creating automorphing between source and destination image. The thesis describes existing methods and algorithms for creating morphing. The thesis focuses on the design and implementation methods for creating automorphing. For this purpose it is necessary to detect significant points in the image, find correspondence of to these points and create your own morphing.
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Image similarity measurement using points of interest
Jelínek, Ondřej ; Uher, Václav (referee) ; Burget, Radim (advisor)
This paper presents a new object detection method. The method is based on keypoints analysis and their parameters. Computed parameters are used for building a decision model using machine learning methods. The model is able to detect object in the picture based on input data and compares its similarity to the chosen example. The new method is described in detail, its accuracy is evaluated and this accuracy is compared to other existing detectors. The new method’s detection ability is by more than 40% better than detection ability of detectors like SURF. In order to understand the object detection this paper describes the process step by step including popular algorithms designed for specific roles in each step.
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Detection of changes in the image
Čech, Jan ; Zemčík, Tomáš (referee) ; Richter, Miloslav (advisor)
The aim of this thesis is finding changes in two similar pictures made in different periods of time. This task belongs to the field of computer vision. To solve this problem different methods of computer vision are used. First the angle of scanning has to be found. Then pictures go through the proces of preprocessing, afterwards the segmentation and the determination of descriptors are carried out. Finally, the pictures are compared and the differences are pointed out.
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Detection of changes in the image
Čech, Jan ; Zemčík, Tomáš (referee) ; Richter, Miloslav (advisor)
The aim of this thesis is finding changes in two similar pictures made in different periods. This task belongs to the field of computer vision. To solve this problem different methods of computer visions are used in several solution versions. Program is working with two scenes. In final version are objects from both scenes compared. The result is written into logging file.
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Detection of changes in the image
Čech, Jan ; Zemčík, Tomáš (referee) ; Richter, Miloslav (advisor)
The aim of this thesis is finding changes in two similar pictures made in different periods of time. This task belongs to the field of computer vision. To solve this problem different methods of computer vision are used. First the angle of scanning has to be found. Then pictures go through the proces of preprocessing, afterwards the segmentation and the determination of descriptors are carried out. Finally, the pictures are compared and the differences are pointed out.
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Automorphing of the Image Pairs
Čermák, Pavel ; Nečas, Ondřej (referee) ; Beran, Vítězslav (advisor)
This thesis I deal with creating automorphing between source and destination image. The thesis describes existing methods and algorithms for creating morphing. The thesis focuses on the design and implementation methods for creating automorphing. For this purpose it is necessary to detect significant points in the image, find correspondence of to these points and create your own morphing.
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|
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Image similarity measurement using points of interest
Jelínek, Ondřej ; Uher, Václav (referee) ; Burget, Radim (advisor)
This paper presents a new object detection method. The method is based on keypoints analysis and their parameters. Computed parameters are used for building a decision model using machine learning methods. The model is able to detect object in the picture based on input data and compares its similarity to the chosen example. The new method is described in detail, its accuracy is evaluated and this accuracy is compared to other existing detectors. The new method’s detection ability is by more than 40% better than detection ability of detectors like SURF. In order to understand the object detection this paper describes the process step by step including popular algorithms designed for specific roles in each step.
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