Automatic compression for image sets using a graph theoretical framework

x, 77 leaves ; 29 cm.A new automatic compression scheme that adapts to any image set is presented in this thesis. The proposed scheme requires no a priori knowledge on the properties of the image set. This scheme is obtained using a unified graph-theoretical framework that allows for compression strategies to be compared both theoretically and experimentally. This strategy achieves optimal lossless compression by computing a minimum spanning tree of a graph constructed from the image set. For lossy compression, this scheme is near-optimal and a performance guarantee relative to the optimal one is provided. Experimental results demonstrate that this compression strategy compares favorably to the previously proposed strategies, with improvements up to 7% in the case of lossless compression and 72% in the case of lossy compression. This thesis also shows that the choice of underlying compression algorithm is important for compressing image sets using the proposed scheme

Discovering Regularity in Point Clouds of Urban Scenes

Despite the apparent chaos of the urban environment, cities are actually replete with regularity. From the grid of streets laid out over the earth, to the lattice of windows thrown up into the sky, periodic regularity abounds in the urban scene. Just as salient, though less uniform, are the self-similar branching patterns of trees and vegetation that line streets and fill parks. We propose novel methods for discovering these regularities in 3D range scans acquired by a time-of-flight laser sensor. The applications of this regularity information are broad, and we present two original algorithms. The first exploits the efficiency of the Fourier transform for the real-time detection of periodicity in building facades. Periodic regularity is discovered online by doing a plane sweep across the scene and analyzing the frequency space of each column in the sweep. The simplicity and online nature of this algorithm allow it to be embedded in scanner hardware, making periodicity detection a built-in feature of future 3D cameras. We demonstrate the usefulness of periodicity in view registration, compression, segmentation, and facade reconstruction. The second algorithm leverages the hierarchical decomposition and locality in space of the wavelet transform to find stochastic parameters for procedural models that succinctly describe vegetation. These procedural models facilitate the generation of virtual worlds for architecture, gaming, and augmented reality. The self-similarity of vegetation can be inferred using multi-resolution analysis to discover the underlying branching patterns. We present a unified framework of these tools, enabling the modeling, transmission, and compression of high-resolution, accurate, and immersive 3D images

On the design of architecture-aware algorithms for emerging applications

This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.Ph.D.Committee Chair: Bader, David; Committee Member: Hong, Bo; Committee Member: Riley, George; Committee Member: Vuduc, Richard; Committee Member: Wills, Scot

Context-Based Trit-Plane Coding for Progressive Image Compression

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. Our codes are available at https://github.com/seungminjeon-github/CTC.Comment: Accepted to CVPR 202

Lossless compression of hyperspectral images

Band ordering and the prediction scheme are the two major aspects of hyperspectral imaging which have been studied to improve the performance of the compression system. In the prediction module, we propose spatio-spectral prediction methods. Two non-linear spectral prediction methods have been proposed in this thesis. NPHI (Non-linear Prediction for Hyperspectral Images) is based on a band look-ahead technique wherein a reference band is included in the prediction of pixels in the current band. The prediction technique estimates the variation between the contexts of the two bands to modify the weights computed in the reference band to predict the pixels in the current band. EPHI (Edge-based Prediction for Hyperspectral Images) is the modified NPHI technique wherein an edge-based analysis is used to classify the pixels into edges and non-edges in order to perform the prediction of the pixel in the current band. Three ordering methods have been proposed in this thesis. The first ordering method computes the local and global features in each band to group the bands. The bands in each group are ordered by estimating the compression ratios achieved between the entire band in the group and then ordering them using Kruskal\u27s algorithm. The other two methods of ordering compute the compression ratios between b-neighbors in performing the band ordering

Detecting Poisoning Attacks on Hierarchical Malware Classification Systems

Anti-virus software based on unsupervised hierarchical clustering (HC) of malware samples has been shown to be vulnerable to poisoning attacks. In this kind of attack, a malicious player degrades anti-virus performance by submitting to the database samples specifically designed to collapse the classification hierarchy utilized by the anti-virus (and constructed through HC) or otherwise deform it in a way that would render it useless. Though each poisoning attack needs to be tailored to the particular HC scheme deployed, existing research seems to indicate that no particular HC method by itself is immune. We present results on applying a new notion of entropy for combinatorial dendrograms to the problem of controlling the influx of samples into the data base and deflecting poisoning attacks. In a nutshell, effective and tractable measures of change in hierarchy complexity are derived from the above, enabling on-the-fly flagging and rejection of potentially damaging samples. The information-theoretic underpinnings of these measures ensure their indifference to which particular poisoning algorithm is being used by the attacker, rendering them particularly attractive in this setting

End-to-end 3D video communication over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Three-dimensional technology, more commonly referred to as 3D technology, has revolutionised many fields including entertainment, medicine, and communications to name a few. In addition to 3D films, games, and sports channels, 3D perception has made tele-medicine a reality. By the year 2015, 30% of the all HD panels at home will be 3D enabled, predicted by consumer electronics manufacturers. Stereoscopic cameras, a comparatively mature technology compared to other 3D systems, are now being used by ordinary citizens to produce 3D content and share at a click of a button just like they do with the 2D counterparts via sites like YouTube. But technical challenges still exist, including with autostereoscopic multiview displays. 3D content requires many complex considerations--including how to represent it, and deciphering what is the best compression format--when considering transmission or storage, because of its increased amount of data. Any decision must be taken in the light of the available bandwidth or storage capacity, quality and user expectations. Free viewpoint navigation also remains partly unsolved. The most pressing issue getting in the way of widespread uptake of consumer 3D systems is the ability to deliver 3D content to heterogeneous consumer displays over the heterogeneous networks. Optimising 3D video communication solutions must consider the entire pipeline, starting with optimisation at the video source to the end display and transmission optimisation. Multi-view offers the most compelling solution for 3D videos with motion parallax and freedom from wearing headgear for 3D video perception. Optimising multi-view video for delivery and display could increase the demand for true 3D in the consumer market. This thesis focuses on an end-to-end quality optimisation in 3D video communication/transmission, offering solutions for optimisation at the compression, transmission, and decoder levels.Brunel University - Isambard Research Scholarshi

Contribution to the Design AND Implementation of a Microwave Tomography System for Breast Cancer Detection

Abstract This thesis represents a contribution to the design and implementation of a microwave tomography system applied to breast cancer detection. Microwave tomography is an imaging technique that aims to reconstruct the permittivity and conductivity of an unknown object from measurements of its scattered field. This technique has been used in a variety of applications such as non-destructive testing, geophysical surveys and biomedical imaging. Here, we will concentrate in the breast cancer detection, where this technique has received a lot of attention in the recent years. A microwave tomography system usually involves two separate parts, a measurement system capable of performing accurate measurements of the scattered field and a set of algorithms for solving the inverse problem of retrieving the permittivity and conductivity spatial distribution of the unknown object from the scattered field measurements. This inverse problem is particularly difficult to solve, since it is non-linear and ill posed. In order to achieve a good reconstruction of the object, we need to illuminate it under several independent conditions, such as different antenna positions, frequencies or polarizations. In this thesis, we concentrate in the design of an efficient illumination configuration that tries to maximize the quality of the reconstructed images. After a literature review, it is observed that most of the proposed measurement systems share a common configuration, where in order to maximize the comfort of the patient; the antennas are arranged in a cylindrical or hemi-spherical configuration. On the other hand, the most popular method for breast cancer detection is mammography, where an X-ray image of the compressed breast at two different projections is performed. Taking this into account, two alternative configurations based on a compression of the breast are proposed, the camera and waveguide configurations. The main hypothesis behind this proposition is that a compression of the breast will allow placing the receivers very close to the breast where it is possible to measure the evanescent component of the scattered field and thus allow an enhancement of the quality of the reconstructed images. In order to prove this hypothesis, a rigorous study of the proposed configurations against a classical circular tomography setup is performed, and we determine under what conditions the reconstructed images can be enhanced. Next, the placement of the receiving antennas very close to the object under test, poses some challenges for an accurate measurement of the scattered fields, since the measurement probe itself can distort the quantity to be measured. For this purpose, an enhanced version of a previously designed near-field probe based on the modulated scattering technique is designed and validated. The probe is then used in the practical implementation of the proposed waveguide configuration. polarisations à l’intérieur du guide d’onde.----------Résumé Cette thèse représente une contribution à la conception et mise en œuvre d’un système de tomographie micro-onde pour la détection du cancer du sein. La tomographie micro-onde est une technique d’imagerie donc le but est de reconstruire la permittivité et la conductivité d’un objet inconnu à partir des mesures du champ diffusé par l’objet. Cette technique a été utilisée dans une variété d'applications comme le control non-destructif, la géophysique et l’imagerie biomédicale. Dans cette thèse, l'emphase sera mise sur la détection du cancer du sein, où cette technique a reçu énormément d’attention dans les années précédentes. Un système de tomographie micro-onde est normalement composé de deux parties séparées; un système de mesures capable de fournir des mesures précises du champ diffusé et une série d’algorithmes capable de retrouver la distribution spatiale de la permittivité et la conductivité de l’objet inconnu à partir des mesures du champ diffusé. Ce problème inverse est particulièrement difficile à résoudre, puisqu’il est non-linéaire et mal posé. Dans le but d’obtenir une bonne reconstruction de l’objet, il est nécessaire d’illuminer l’objet sous une série de conditions indépendantes, comme différentes positions d’antenne, des fréquences ou des polarisations. Dans cette thèse, l'emphase sera mise sur la conception d’une configuration d’illumination efficace qui essaie de maximiser la qualité des images reconstruites. Après une revue de littérature, on observe que la plupart des systèmes de mesures partagent une configuration commune o\`u les antennes sont placées sur une configuration cylindrique ou hémisphérique pour maximiser le confort de la patiente. D’un autre coté, la méthode la plus populaire pour le dépistage du cancer du sein est la mammographie, o\`u on utilise une image à rayons X du sein compressé en deux projections. En prenant compte de ce fait, on propose deux configurations alternatives basées sur la compression du sein, les configurations caméra et guide d’onde. L’hypothèse derrière cette proposition est que la compression du sein permet de placer les capteurs très près de ce dernier donc il est possible de mesurer la composante évanescente du champ diffusé, ce qui pourrait permettre l'amélioration de la qualité des images reconstruites. Afin de prouver cette hypothèse, une étude rigoureuse des configurations proposées et sa comparaison avec une configuration classique de tomographie circulaire est réalisée. Grace à cette étude on détermine les conditions qui permettent d’améliorer les images reconstruites. Le placement des capteurs très proche de l’objet sous test représente un défi pour une mesure précise des champs diffusés, puisque le capteur lui-même peut perturber le signal à mesurer. Pour cette raison, une version améliorée d’une sonde de mesure en champ proche basé sur la technique de diffusion modulée est conçue et validée. La sonde est utilisée pour la mise en œuvre de la configuration guide d’onde proposée. Un réseau d’antennes est développé pour l’excitation de différents modes avec différente

3D oceanographic data compression using 3D-ODETLAP

This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit