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Object Description in Images
Dvořák, Pavel ; Šmirg, Ondřej (referee) ; Zukal, Martin (advisor)
This thesis consider description of segments identified in image. At first there are described main methods of segmentation because it is a process contiguous before describing of objects. Next chapter is devoted to methods which focus on description identified regions. There are studied algorithms used for characterizing of different features. There are parts devoted to color, location, size, orientation, shape and topology. The end of this chapter is devoted to moments. Next chapters are focused on designing fit algorithms for segments description and XML files creating according to MPEG-7 standards and their implementation into RapidMiner. In the last chapter there are described results of the implementation.
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Advanced Moment-Based Methods for Image Analysis
Höschl, Cyril ; Flusser, Jan (advisor) ; Papakostas, George (referee) ; Jiřík, Radovan (referee)
The Thesis consists of an introduction and four papers that contribute to the research of image moments and moment invariants. The first two papers focus on rectangular decomposition algorithms that rapidly speed up the moment calculations. The other two papers present a design of new moment invariants. We present a comparative study of cutting edge methods for the decomposition of 2D binary images, including original implementations of all the methods. For 3D binary images, finding the optimal decomposition is an NP-complete problem, hence a polynomial-time heuristic needs to be developed. We propose a sub-optimal algorithm that outperforms other state of the art approximations. Additionally, we propose a new form of blur invariants that are derived by means of projection operators in a Fourier domain, which improves mainly the discrimination power of the features. Furthermore, we propose new moment-based features that are tolerant to additive Gaussian image noise and we show by extensive image retrieval experiments that the proposed features are robust and outperform other commonly used methods.
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Advanced Moment-Based Methods for Image Analysis
Höschl, Cyril ; Flusser, Jan (advisor) ; Papakostas, George (referee) ; Jiřík, Radovan (referee)
The Thesis consists of an introduction and four papers that contribute to the research of image moments and moment invariants. The first two papers focus on rectangular decomposition algorithms that rapidly speed up the moment calculations. The other two papers present a design of new moment invariants. We present a comparative study of cutting edge methods for the decomposition of 2D binary images, including original implementations of all the methods. For 3D binary images, finding the optimal decomposition is an NP-complete problem, hence a polynomial-time heuristic needs to be developed. We propose a sub-optimal algorithm that outperforms other state of the art approximations. Additionally, we propose a new form of blur invariants that are derived by means of projection operators in a Fourier domain, which improves mainly the discrimination power of the features. Furthermore, we propose new moment-based features that are tolerant to additive Gaussian image noise and we show by extensive image retrieval experiments that the proposed features are robust and outperform other commonly used methods.
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Object Description in Images
Dvořák, Pavel ; Šmirg, Ondřej (referee) ; Zukal, Martin (advisor)
This thesis consider description of segments identified in image. At first there are described main methods of segmentation because it is a process contiguous before describing of objects. Next chapter is devoted to methods which focus on description identified regions. There are studied algorithms used for characterizing of different features. There are parts devoted to color, location, size, orientation, shape and topology. The end of this chapter is devoted to moments. Next chapters are focused on designing fit algorithms for segments description and XML files creating according to MPEG-7 standards and their implementation into RapidMiner. In the last chapter there are described results of the implementation.
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Detection of Elliptical Particles in Atomic Force Microscopy Images
Sedlář, Jiří ; Zitová, Barbara ; Kopeček, Jaromír ; Todorciuc, T. ; Kratochvílová, Irena
In this paper we describe a method for detection and measurement of elliptical particles in atomic force microscopy (AFM) images. AFM imaging is used in physics to scan surfaces; the measured heights are represented by pixel values. Each sample in our project consisted of elliptical particles of principally the same size; the size could be characterized by the average length and width of a number of salient particles. The method we proposed is based on segmentation of undamaged particles and their approximation by ellipses; the major and minor axes provide robust estimates of the lengths and widths of the particles, respectively. The method is robust to distortions typical of AFM images. Its performance was demonstrated on images of pyrroles and compared with manual detection. Results show that the automatic method could be used in place of the time-consuming manual detection.
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