Global ETD Search

1	Segmentačních techniky zpracování obrazu / Segmentation techniques for image processing Bajgar, Aleš January 2011 (has links) For image segmentation are used several methods. Good results can be achieved by the correct choice of method. Best results are using more methods together. Methods such as thresholding, the search area or methods founded on edge detection in an image. We can use different filters for noise removal. For example Gaussian, median, etc. Lowpass filter can also be used. Image segmentation Mean Shift, K-mean, watershed and pyramid segmentation performed by using methods from the OpenCV library.
2	Geometric approach to multi-scale 3D gesture comparison Ochoa Mayorga, Victor Manuel 11 1900 (has links) The present dissertation develops an invariant framework for 3D gesture comparison studies. 3D gesture comparison without Lagrangian models is challenging not only because of the lack of prediction provided by physics, but also because of a dual geometry representation, spatial dimensionality and non-linearity associated to 3D-kinematics. In 3D spaces, it is difficult to compare curves without an alignment operator since it is likely that discrete curves are not synchronized and do not share a common point in space. One has to assume that each and every single trajectory in the space is unique. The common answer is to assert the similitude between two or more trajectories as estimating an average distance error from the aligned curves, provided that the alignment operator is found. In order to avoid the alignment problem, the method uses differential geometry for position and orientation curves. Differential geometry not only reduces the spatial dimensionality but also achieves view invariance. However, the nonlinear signatures may be unbounded or singular. Yet, it is shown that pattern recognition between intrinsic signatures using correlations is robust for position and orientation alike. A new mapping for orientation sequences is introduced in order to treat quaternion and Euclidean intrinsic signatures alike. The new mapping projects a 4D-hyper-sphere for orientations onto a 3D-Euclidean volume. The projection uses the quaternion invariant distance to map rotation sequences into 3D-Euclidean curves. However, quaternion spaces are sectional discrete spaces. The significance is that continuous rotation functions can be only approximated for small angles. Rotation sequences with large angle variations can only be interpolated in discrete sections. The current dissertation introduces two multi-scale approaches that improve numerical stability and bound the signal energy content of the intrinsic signatures. The first is a multilevel least squares curve fitting method similar to Haar wavelet. The second is a geodesic distance anisotropic kernel filter. The methodology testing is carried out on 3D-gestures for obstetrics training. The study quantitatively assess the process of skill acquisition and transfer of manipulating obstetric forceps gestures. The results show that the multi-scale correlations with intrinsic signatures track and evaluate gesture differences between experts and trainees. 3D gesture comparison Multiscale curvature Curvature-torsion differential geometry complex trajectories quaternion trajectories quaternion projections quaternion metrics 3D trajectory smoothing curvature signature correlations qualitative skill transfer measure medical manipulative gestures nonlinear signatures anisotropic kernel filter curvature numerical stability quaternion smoothing kinematics motor skill transfer curvature signature torsion signature arc-length parameterization tensors Frenet-Serret quaternion framework nonlinear kernel filter geodesic distance finite difference methods differential convolution filter Savitzky-Golay filter least squares curve fitting curvature neighborhood multilevel curvature analysis medical training child bearing simulator robotic simulator motion tracking 3D electromagnetic trackers sequence comparison medical training
3	Geometric approach to multi-scale 3D gesture comparison Ochoa Mayorga, Victor Manuel Unknown Date No description available. 3D gesture comparison Multiscale curvature Curvature-torsion differential geometry complex trajectories quaternion trajectories quaternion projections quaternion metrics 3D trajectory smoothing curvature signature correlations qualitative skill transfer measure medical manipulative gestures nonlinear signatures anisotropic kernel filter curvature numerical stability quaternion smoothing kinematics motor skill transfer curvature signature torsion signature arc-length parameterization tensors Frenet-Serret quaternion framework nonlinear kernel filter geodesic distance finite difference methods differential convolution filter Savitzky-Golay filter least squares curve fitting curvature neighborhood multilevel curvature analysis medical training child bearing simulator robotic simulator motion tracking 3D electromagnetic trackers sequence comparison medical training
4	Parameters Selection for Optimising Time-Frequency Distributions and Measurements of Time-Frequency Characteristics of Nonstationary Signals Sucic, Victor January 2004 (has links) The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed. Time-frequency distribution nonstationary signal monocomponent signal multicomponent signal frequency modulated (FM) signal linear FM signal non-linear FM signal kernel filter separable kernel Fourier transform crossterms autoterms concentration resolution mainlobe amplitude sidelobe amplitude instantaneous frequency instantaneous bandwidth crossterms amplitude comparison criteria performance measure parameter optimisation optimal time-frequency distribution design requirements additive white Gaussian noise real-life signal Modified B distribution separable kernel TFD.

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