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.
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Showing 1 to 20 of 38 for “"sparse coding"”.
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Deformation-invariant sparse coding
Sparse coding represents input signals each as a sparse linear combination of a set of basis or dictionary elements where sparsity encourages representing each input signal with a few of the most indicative dictionary elements. In this thesis, we extend sparse coding to allow dictionary elements to …
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A sparse coding approach to multiclass pixel labelling
… label consistency in local image regions through sparse modelling of image data. Individual pixels in an image may be assigned a label denoting a semantic category (such as sky, grass, human, car, etc.) based their observed features (such as colour, texture cues etc.). Such an independent …
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A sparse coding approach to multiclass pixel labelling
… label consistency in local image regions through sparse modelling of image data. Individual pixels in an image may be assigned a label denoting a semantic category (such as sky, grass, human, car, etc.) based their observed features (such as colour, texture cues etc.). Such an independent …
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Fast superresolution based on a network structure trained using sparse coding
… to superresolution using a network structure. Sparse representation of image signals forms the cornerstone of our approach and the goal is to obtain resolution enhancement of the low resolution images. I will discuss various dictionary learning methods and also a joint dictionary training …
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Bayesian modelling of music: algorithmic advances and experimental studies of shift-invariant sparse coding
… as classification,<br/>pattern recognition and coding, it is helpful to specify a signal model in<br/>terms of meaningful signal structures. In general, designing such a model<br/>is complicated and for many signals it is not feasible to specify the appropriate<br/>structure. Adaptive models …
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A general, context-aware pedestrian trajectory prediction model
… objective, first, the Augmented Semi Nonnegative Sparse Coding (ASNSC) [13] framework, for trajectory prediction, is extended to embed context, and build the Context-aware Augmented Semi Nonnegative Sparse Coding (CASNSC) algorithm. For prediction in new, unseen intersections with different …
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Image Compression and Channel Error Correction using Neurally-Inspired Network Models
… computational capacity as compared to DCT Zonal coding, DCT Threshold coding, Set Partitioning in Hierarchical Trees (SPIHT) and Gaussian Pyramid. An artificial neural network (ANN) with improved channel error-correction rate is also proposed. The experimental results indicate that the …
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Transform learning based image and video processing
In recent years, sparse signal modeling, especially using the synthesis dictionary model, has received much attention. Sparse coding in the synthesis model is, however, NP-hard. Various methods have been proposed to learn such synthesis dictionaries from data. Numerous applications such as image …
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Task-specific and interpretable feature learning
… dissertation starts by showing how the classical sparse coding models could be improved in a task-specific way, by formulating the entire pipeline as bi-level optimization. Then, it mainly illustrates how to incorporate the structure of classical learning models, e.g., sparse coding, into the …
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Learning sparse features and metric in signal and image processing
… in computational neuroscience in the context of sparse coding in the visual system, sparse coding plays a key role in feature representation learning, as the over-complete dictionary allows more representation flexibility and efficiency, and captures structures and patterns inherent in the raw …
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Speech perception in a sparse domain
… system may use redundancy reduction and sparse coding strategies to deal with complex stimuli every day. Both redundancy reduction and sparse coding theory emphasise the importance of high order statistics signals.<br/>This thesis includes psycho-acoustical experiments designed to …
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Learning image super resolution from joint examples
… SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the …
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Computational Techniques for Advancing Photoacoustic Microscopy
… to address these challenges. Specially, a sparse coding-based technique is developed to enhance image contrast and ensure robust functional measurements in noisy low-fluence conditions. Additionally, a spatiotemporal red blood cell tracking algorithm, together with a high-speed PAM system, …
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Deep learning for image restoration and enhancement
… domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We experimentally show that a sparse coding model particularly designed for SR can be incarnated as a neural …
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Similarity modeling for machine learning
… learning method, then introduce two novel sparse similarity modeling methods for high dimensional data from the perspective of manifold learning and subspace learning. Our sparse similarity modeling methods learn sparse similarity and consequently generate a sparse graph over the data. The …
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Dictionary learning for scalable sparse image representation
… one of the novel processing tools is signal sparse coding which represents signals as linear combinations of a few representational basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and …
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Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach
… the speaker features. The universal background sparse coding can provide useful speaker information for the speaker adaptation. These methods may lead to some new opportunities for research for the adaptation of the ASR.
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A Supervised Low-Rank Matrix Decomposition for Matching
… recent approaches rely on techniques such as sparse coding and low-rank matrix decomposition. Those build a generative representation of the data that on the one hand, attempts capturing all the information descriptive of an identity; on the other hand, training and testing are complex to …
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Adaptive sparse representations and their applications
… model and its relationship to prior linear sparse models. Then, we propose novel problem formulations for learning square sparsifying transforms from data. The proposed algorithms for transform learning alternate between a sparse coding step and a transform update step, and are highly …
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