Fusion of Images and Videos using Multi-scale Transforms

This thesis deals with methods for fusion of images as well as videos using multi-scale transforms. First, a novel image fusion algorithm based primarily on an improved multi-scale coefficient decomposition framework is proposed. The proposed framework uses a combination of non-subsampled contourlet and wavelet transforms for the initial multi-scale decompositions. The decomposed multi-scale coefficients are then fused twice using various local activity measures. Experimental results show that the proposed approach performs better or on par with the existing state-of-the art image fusion algorithms in terms of quantitative and qualitative performance. In addition, the proposed image fusion algorithm can produce high quality fused images even with a computationally inexpensive two-scale decomposition. Finally, we extend the proposed framework to formulate a novel video fusion algorithm for camouflaged target detection from infrared and visible sensor inputs. The proposed framework consists of a novel target identification method based on conventional thresholding techniques proposed by Otsu and Kapur et al. These thresholding techniques are further extended to formulate novel region-based fusion rules using local statistical measures. The proposed video fusion algorithm, when used in target highlighting mode, can further enhance the hidden target, making it much easier to localize the hidden camouflaged target. Experimental results show that the proposed video fusion algorithm performs much better than its counterparts in terms of quantitative and qualitative results as well as in terms of time complexity. The relative low complexity of the proposed video fusion algorithm makes it an ideal candidate for real-time video surveillance applications

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Unsupervised Learning Techniques for Microseismic and Croswell Geophysical Data

Machine learning has served to develop and explore a wide range of applications for geoscientists and petroleum engineers. Fundamental limitations of conventional methodologies include mathematical formulations of physical systems, multi-scale heterogeneity, processing of large datasets, and computational time. The impact of these new technologies has brought the interest of multiple energy industries such as renewables, oil and gas, carbon sequestration, and geothermal. The acquisition of subsurface measurements has been a key factor to characterize reservoir properties. Hence, the integration of machine learning could provide essential information and new knowledge of subsurface monitoring signals. In this work, we focus on the use of unsupervised learning to determine new insights into geophysical tools and subsurface physical properties. We propose three methodologies using microseismic, distributed acoustic sensing (DAS), seismic and electrical resistivity tomography. A critical aspect of monitoring tools is the high computational power of big data. We applied unsupervised dimensionality reduction to compress, denoise and retrieve vital information of microseismic and DAS data. To achieve this, we implemented high-order SVD for high-dimensional arrays of 3D and 4D space. For the 3D microseismic, we achieved a compression of approximately 75% and a reduction of samples from 1,728,000 to 431,303. We also tested the model to the 3D DAS data where we obtained a compression of 70.2% for a data size of 3.5 GB. Lastly, a 4D HOSVD model was established using a synthetic microseismic tensor, accomplishing a reduction of 83%. Another major application of unsupervised learning is the clustering algorithms to group observations of similar characteristics. We applied spatial-temporal clustering to identify hidden patterns of subsurface mapping for a geological carbon storage field. The studies were divided according to the geophysical method (crosswell seismic and ERT) and temporal component (single time or time-series). Using crosswell seismic, we developed a multi-level clustering approach to visualize the CO2 plume behavior. For the first level, we obtained a silhouette score of 0.85, a Calinski-Harabasz of 160666.50, and a Davies-Bouldin value of 0.43. The second level achieved a silhouette, CalinskiHarabasz, and Davies-Bouldin score of 0.74, 59656.01, and 0.32 respectively. We established a total of four clusters of non, low, medium, and high SCO2. Finally, we elaborated a spatial-temporal clustering using derived-SCO2 from daily ERT images. A novel feature extraction methodology was designed to retrieve the spatial and temporal changes of the moving CO2. Four clusters were determined and linked to the saturation levels. The interval validation of clusters was 0.58 for the DTW-silhouette score, 262791.45 for Calinski-Harabasz, and 0.71 for the Davies-Bouldin index. To evaluate the dynamics of CO2 flow regimes, we performed a second clustering where 6 distinctive plume patterns were observed. Therefore, machine learning and in particular unsupervised learning can be used to describe complex systems and optimize data processing

Sparse Representation-Based Framework for Preprocessing Brain MRI

This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1