Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 12 of 12 for “"Complex Wavelet"”.
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Improved detection and quantisation of keypoints in the complex wavelet domain
… categoriser (or quantiser). The Dual Tree Complex Wavelet Transform (DTCWT) decomposes an image into oriented subbands at a range of scales. The resulting domain is arguably well suited for further image analysis tasks such as feature identification. This thesis develops feature …
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Enhancing land seismic data with compressive sensing and processing
… over a single-channel variant. I establish that complex wavelet domain is an optimal choice for sparsifying highly non-stationary land wavefields for single-channel compressive sensing and develop thresholding techniques that can be used for a sparsity-promoting data reconstruction and for …
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Wavelet Transforms for Stereo Imaging
… especially when a new mathematical tool such as wavelet analysis becomes mature.<br/><br/>The aim of the thesis is to investigate the stereo matching approach using wavelet transform with a view to producing efficient and dense disparity map outputs. After the shift invariance property of various …
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Analysis of Wavelet Based Alternatives for OFDM
The objective of this thesis is to analyze wavelet based alternatives for orthogonal frequency division multiplexing (OFDM) and find whether a better system performance is achieved when compared to the discrete Fourier transform (DFT)-based OFDM. We analyze DFT, discrete wavelet transform (DWT), …
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Surface electromyography based speech recognition system and development toolkit
… Fourier transform (STFT), the dual-tree complex wavelet transform (DTCWT), a non-causal time-domain based (E4-NC), and a causal version of E4-NC (E4-C) were implemented. Classification was performed using a hidden Markov model (HMM). The system implemented was able to achieve an accuracy …
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Assessing self-similarity in redundant complex and quaternion wavelet domains: Theory and applications
… due to irregularities in the signals or images. Wavelet-based spectral tools have become standard solutions for such problems in signal and image processing and achieved outstanding performances in real applications. This thesis proposes three novel wavelet-based spectral tools to improve the …
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Sparse separation of sources in 3D soundscapes
… is the novel application of a dual-tree complex wavelet transform to sparse source separation, providing an alternative transformation to the short-time Fourier transform often used in this area. Results are presented showing compara- ble signal-to-interference performance, and …
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Low-complexity methods for image and video watermarking
… advantage of the properties of the dual-tree complex wavelet transform (DT CWT). This transform offers the advantages of both the regular and the complex wavelets (perfect reconstruction, approximate shift invariance and good directional selectivity). Our methods use these characteristics to …
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Multiscale techniques for image segmentation, classification and retrieval
… Markov Tree. This framework is based on the complex wavelet decomposition of a given image. The unsupervised Mean Shift Procedure is used to determine the number of object classes. Most unsupervised techniques are difficult to evaluate due to a lack of ground data on which evaluation of …
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Development and evaluation of a multiscale keypoint detector based on complex wavelets
… detector and descriptor based on the Dual-Tree Complex Wavelet Transform (DTCWT). First, we develop a scale-space framework called the 4S-DTCWT that uses the dyadic decomposition of the DTCWT but achieves denser sampling in scale by interleaving several DTCWT trees, leading to reduced …
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Uses of Complex Wavelets in Deep Convolutional Neural Networks
… and many other tasks). In particular, we use complex wavelets (rather than the Fourier transform or the discrete wavelet transform) as basis functions to reformulate image understanding with deep networks. In this thesis, we explore the most popular and well-developed form of using complex …
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A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain
… Feature extraction models based on PCA or wavelets judge feature importance by the magnitude of the transform coefficients, rarely leading to an appropriate set of features for classification. We analyze a recent neural paradigm, Generalized Relevance Learning Vector Quantization (GRLVQ) …