Information selection and fusion in vision systems

Handling the enormous amounts of data produced by data-intensive imaging systems, such as multi-camera surveillance systems and microscopes, is technically challenging. While image and video compression help to manage the data volumes, they do not address the basic problem of information overflow. In this PhD we tackle the problem in a more drastic way. We select information of interest to a specific vision task, and discard the rest. We also combine data from different sources into a single output product, which presents the information of interest to end users in a suitable, summarized format. We treat two types of vision systems. The first type is conventional light microscopes. During this PhD, we have exploited for the first time the potential of the curvelet transform for image fusion for depth-of-field extension, allowing us to combine the advantages of multi-resolution image analysis for image fusion with increased directional sensitivity. As a result, the proposed technique clearly outperforms state-of-the-art methods, both on real microscopy data and on artificially generated images. The second type is camera networks with overlapping fields of view. To enable joint processing in such networks, inter-camera communication is essential. Because of infrastructure costs, power consumption for wireless transmission, etc., transmitting high-bandwidth video streams between cameras should be avoided. Fortunately, recently designed 'smart cameras', which have on-board processing and communication hardware, allow distributing the required image processing over the cameras. This permits compactly representing useful information from each camera. We focus on representing information for people localization and observation, which are important tools for statistical analysis of room usage, quick localization of people in case of building fires, etc. To further save bandwidth, we select which cameras should be involved in a vision task and transmit observations only from the selected cameras. We provide an information-theoretically founded framework for general purpose camera selection based on the Dempster-Shafer theory of evidence. Applied to tracking, it allows tracking people using a dynamic selection of as little as three cameras with the same accuracy as when using up to ten cameras

Learning-based Wavelet-like Transforms For Fully Scalable and Accessible Image Compression

The goal of this thesis is to improve the existing wavelet transform with the aid of machine learning techniques, so as to enhance coding efficiency of wavelet-based image compression frameworks, such as JPEG 2000. In this thesis, we first propose to augment the conventional base wavelet transform with two additional learned lifting steps -- a high-to-low step followed by a low-to-high step. The high-to-low step suppresses aliasing in the low-pass band by using the detail bands at the same resolution, while the low-to-high step aims to further remove redundancy from detail bands by using the corresponding low-pass band. These two additional steps reduce redundancy (notably aliasing information) amongst the wavelet subbands, and also improve the visual quality of reconstructed images at reduced resolutions. To train these two networks in an end-to-end fashion, we develop a backward annealing approach to overcome the non-differentiability of the quantization and cost functions during back-propagation. Importantly, the two additional networks share a common architecture, named a proposal-opacity topology, which is inspired and guided by a specific theoretical argument related to geometric flow. This particular network topology is compact and with limited non-linearities, allowing a fully scalable system; one pair of trained network parameters are applied for all levels of decomposition and for all bit-rates of interest. By employing the additional lifting networks within the JPEG2000 image coding standard, we can achieve up to 17.4% average BD bit-rate saving over a wide range of bit-rates, while retaining the quality and resolution scalability features of JPEG2000. Built upon the success of the high-to-low and low-to-high steps, we then study more broadly the extension of neural networks to all lifting steps that correspond to the base wavelet transform. The purpose of this comprehensive study is to understand what is the most effective way to develop learned wavelet-like transforms for highly scalable and accessible image compression. Specifically, we examine the impact of the number of learned lifting steps, the number of layers and the number of channels in each learned lifting network, and kernel support in each layer. To facilitate the study, we develop a generic training methodology that is simultaneously appropriate to all lifting structures considered. Experimental results ultimately suggest that to improve the existing wavelet transform, it is more profitable to augment a larger wavelet transform with more diverse high-to-low and low-to-high steps, rather than developing deep fully learned lifting structures

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Image Restoration Methods for Retinal Images: Denoising and Interpolation

Retinal imaging provides an opportunity to detect pathological and natural age-related physiological changes in the interior of the eye. Diagnosis of retinal abnormality requires an image that is sharp, clear and free of noise and artifacts. However, to prevent tissue damage, retinal imaging instruments use low illumination radiation, hence, the signal-to-noise ratio (SNR) is reduced which means the total noise power is increased. Furthermore, noise is inherent in some imaging techniques. For example, in Optical Coherence Tomography (OCT) speckle noise is produced due to the coherence between the unwanted backscattered light. Improving OCT image quality by reducing speckle noise increases the accuracy of analyses and hence the diagnostic sensitivity. However, the challenge is to preserve image features while reducing speckle noise. There is a clear trade-off between image feature preservation and speckle noise reduction in OCT. Averaging multiple OCT images taken from a unique position provides a high SNR image, but it drastically increases the scanning time. In this thesis, we develop a multi-frame image denoising method for Spectral Domain OCT (SD-OCT) images extracted from a very close locations of a SD-OCT volume. The proposed denoising method was tested using two dictionaries: nonlinear (NL) and KSVD-based adaptive dictionary. The NL dictionary was constructed by adding phases, polynomial, exponential and boxcar functions to the conventional Discrete Cosine Transform (DCT) dictionary. The proposed denoising method denoises nearby frames of SD-OCT volume using a sparse representation method and combines them by selecting median intensity pixels from the denoised nearby frames. The result showed that both dictionaries reduced the speckle noise from the OCT images; however, the adaptive dictionary showed slightly better results at the cost of a higher computational complexity. The NL dictionary was also used for fundus and OCT image reconstruction. The performance of the NL dictionary was always better than that of other analytical-based dictionaries, such as DCT and Haar. The adaptive dictionary involves a lengthy dictionary learning process, and therefore cannot be used in real situations. We dealt this problem by utilizing a low-rank approximation. In this approach SD-OCT frames were divided into a group of noisy matrices that consist of non-local similar patches. A noise-free patch matrix was obtained from a noisy patch matrix utilizing a low-rank approximation. The noise-free patches from nearby frames were averaged to enhance the denoising. The denoised image obtained from the proposed approach was better than those obtained by several state-of-the-art methods. The proposed approach was extended to jointly denoise and interpolate SD-OCT image. The results show that joint denoising and interpolation method outperforms several existing state-of-the-art denoising methods plus bicubic interpolation.4 month

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Distributed Compressed Representation of Correlated Image Sets

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoints or at different time instants. Therefore, these images are generally correlated through displacement of scene objects. Efficient compression techniques have to exploit this correlation in order to efficiently communicate the 3D scene information. Instead of joint encoding that requires communication between the cameras, in this thesis we concentrate on distributed representation, where the captured images are encoded independently, but decoded jointly to exploit the correlation between images. One of the most important and challenging tasks relies in estimation of the underlying correlation from the compressed correlated images for effective reconstruction or analysis in the joint decoder. This thesis focuses on developing efficient correlation estimation algorithms and joint representation of multiple correlated images captured by various sensing methodologies, e.g., planar, omnidirectional and compressive sensing (CS) sensors. The geometry of the 2D visual representation and the acquisition complexity vary for each sensor type. Therefore, we need to carefully consider the specific geometric nature of the captured images while developing distributed representation algorithms. In this thesis we propose robust algorithms in different scene analysis and reconstruction scenarios. We first concentrate on the distributed representation of omnidirectional images captured by catadioptric sensors. The omnidirectional images are captured from different viewpoints and encoded independently with a balanced rate distribution among the different cameras. They are mapped on the sphere which captures the plenoptic function in its radial form without Euclidean discrepancies. We propose a transform-based distributed coding algorithm, where the spherical images initially undergo a multi-resolution decomposition. The visual information is then split into two correlated partitions. The encoder transmits one partition after entropy coding, as well as the syndrome bits resulting from the Slepian-Wolf encoding of the other partition. The joint decoder estimates a disparity image to take benefit of the correlation between views and uses the syndrome bits to decode the missing information. Such a strategy proves to be beneficial with respect to the independent processing of images and shows only a small performance loss compared to the joint encoding of different views. The encoding complexity in the previous approach is non-negligible due to the visual information processing based on Slepian-Wolf coding and its associated rate parameter estimation. We therefore discard the Slepian-Wolf encoding and propose a distributed coding solution, where the correlated images are encoded independently using transform-based coding solutions (e.g., SPIHT). The central decoder now builds a correlation model from the compressed images, which is used to jointly decode a pair of images. Experimental results demonstrate that the proposed distributed coding solution improves the rate-distortion performance of the separate coding results for both planar and omnidirectional images. However, this improvement is significant only at medium to high bit rates. We therefore propose a rate allocation scheme that identifies and transmits the necessary visual information from each image to improve the correlation estimation accuracy at low bit rate. Experimental results show that for a given bit budget the proposed encoding scheme permits to compute an accurate correlation estimation comparing to the one obtained with SPIHT, JPEG 2000 or JPEG coding schemes. We show however that the improvement in the correlation estimation comes at the price of penalizing the image reconstruction quality; therefore there exists an interesting trade-off between the accurate correlation estimation and image reconstruction as encoding optimization objectives are different in both cases. Next, we further simplify the encoding complexity by replacing the classical imaging sensors with the simple CS sensors, that directly acquire the compressed images in the form of quantized linear measurements. We now concentrate on the particular problem, where one image is selected as the reference and it is used as a side information for the correlation estimation. We propose a geometry-based model to describe the correlation between the visual information in a pair of images. The joint decoder first captures the most prominent visual features in the reconstructed reference image using geometric functions. Since the images are correlated, these features are likely to be present in the other images too, possibly with geometric transformations. Hence, we propose to estimate the correlation model with a regularized optimization problem that locates these features in the compressed images. The regularization terms enforce smoothness of the transformation field, and consistency between the estimated images and the quantized measurements. Experimental results show that the proposed scheme is able to efficiently estimate the correlation between images for several multi-view and video datasets. The proposed scheme is finally shown to outperform DSC schemes based on unsupervised disparity (or motion) learning, as well as independent coding solutions based on JPEG 2000. We then extend the previous scenario to a symmetric decoding problem, where we are interested to estimate the correlation model directly from the quantized linear measurements without explicitly reconstructing the reference images. We first show that the motion field that represents the main source of correlation between images can be described as a linear operator. We further derive a linear relationship between the correlated measurements in the compressed domain. We then derive a regularized cost function to estimate the correlation model directly in the compressed domain using graph-based optimization algorithms. Experimental results show that the proposed scheme estimates an accurate correlation model among images in both multi-view and video imaging scenarios. We then propose a robust data fidelity term that improves the quality of the correlation estimation when the measurements are quantized. Finally, we show by experiments that the proposed compressed correlation estimation scheme is able to compete the solution of a scheme that estimates a correlation model from the reconstructed images without the complexity of image reconstruction. Finally, we study the benefit of using the correlation information while jointly reconstructing the images from the compressed linear measurements. We consider both the asymmetric and symmetric scenarios described previously. We propose joint reconstruction methodologies based on a constrained optimization problem which is solved using effective proximal splitting methods. The constraints included in our framework enforce the reconstructed images to satisfy both the correlation and the quantized measurements consistency objectives. Experimental results demonstrate that the proposed joint reconstruction scheme improves the quality of the decoded images, when compared to a scheme where the images are handled independently. In this thesis we build efficient distributed scene representation algorithms for the multiple correlated images captured in planar, omnidirectional and CS cameras. The coding rate in our symmetric distributed coding solution stays balanced between the encoders and stays close to the joint encoding solutions. Our novel algorithms lead to effective correlation estimation in different sensing and coding scenarios. In addition, we provide innovative solutions for robust correlation estimation from highly compressed images in simple sensing frameworks. Our CS-based joint reconstruction frameworks effectively exploit the inter-view correlation, that permits to achieve high compression gains compared to state-of-the-art independent and distributed coding solutions