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object matching
Mišta, Petr ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
Detection of objects based on color is not commonly used method of computer vision. There are many methods thats deals with the detection of significant points, but the color information has been omitted. The goal of this thesis is to design method of the detection significant color image areas and these areas match up with areas detected in another image. I analyzed features of detectors required to identify the reciprocal correspondence of images, defined the color significance concept, described basic color models and their properties, and a design of statistically compiled data - based method was described. Algorithms for the detection of color use color models RGB and HSV. Correspondence of areas detected in different images is performedy Kohonen neural network. The first input vector can teach Kohonen neural network and the second vector is classified by this network. To remove erroneous classifications RANSAC method is used. As a result, the method can be used for basic and rapid determination of correspondence between images, or to speed up commonly used methods for detection of significant points. At the end of the thesis are presented programs, showing functionality and options of design methods. The designed algorithms have been developed in MATLAB.
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Automatic rotational alignment of head CT scans
Karmazinová, Inna ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
The aim of this thesis is automatic alignment of head CT scan. Currently, the alignment is performed manually by an expert, however this process is time consuming. Therefore, methods for automatization of this process are being developed. Two algorithms for alignment in axial and coronal plane were designed based on bilateral symmetry of head. Following an algorithm for alignment in sagittal plane which uses CG-TOB reference line for rotation angle detection. Algorithms were implemented in MATLAB and tested and validated using a database of manually annotated head CT scans.
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Searching for Points of Interest in Raster Image
Kaněčka, Petr ; Sumec, Stanislav (referee) ; Herout, Adam (advisor)
This document deals with an image points of interest detection possibilities, especially corner detectors. Many applications which are interested in computer vision needs these points as their necessary step in the image processing. It describes the reasons why it is so useful to find these points and shows some basic methods to find them. There are compared features of these methods at the end.
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Automatic rotational alignment of head CT scans
Karmazinová, Inna ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
The aim of this thesis is automatic alignment of head CT scan. Currently, the alignment is performed manually by an expert, however this process is time consuming. Therefore, methods for automatization of this process are being developed. Two algorithms for alignment in axial and coronal plane were designed based on bilateral symmetry of head. Following an algorithm for alignment in sagittal plane which uses CG-TOB reference line for rotation angle detection. Algorithms were implemented in MATLAB and tested and validated using a database of manually annotated head CT scans.
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|
|
Searching for Points of Interest in Raster Image
Kaněčka, Petr ; Sumec, Stanislav (referee) ; Herout, Adam (advisor)
This document deals with an image points of interest detection possibilities, especially corner detectors. Many applications which are interested in computer vision needs these points as their necessary step in the image processing. It describes the reasons why it is so useful to find these points and shows some basic methods to find them. There are compared features of these methods at the end.
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|
|
object matching
Mišta, Petr ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
Detection of objects based on color is not commonly used method of computer vision. There are many methods thats deals with the detection of significant points, but the color information has been omitted. The goal of this thesis is to design method of the detection significant color image areas and these areas match up with areas detected in another image. I analyzed features of detectors required to identify the reciprocal correspondence of images, defined the color significance concept, described basic color models and their properties, and a design of statistically compiled data - based method was described. Algorithms for the detection of color use color models RGB and HSV. Correspondence of areas detected in different images is performedy Kohonen neural network. The first input vector can teach Kohonen neural network and the second vector is classified by this network. To remove erroneous classifications RANSAC method is used. As a result, the method can be used for basic and rapid determination of correspondence between images, or to speed up commonly used methods for detection of significant points. At the end of the thesis are presented programs, showing functionality and options of design methods. The designed algorithms have been developed in MATLAB.
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