2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis

This work proposes an extension of the 1-D Hilbert Huang transform for the analysis of images. The proposed method consists in (i) adaptively decomposing an image into oscillating parts called intrinsic mode functions (IMFs) using a mode decomposition procedure, and (ii) providing a local spectral analysis of the obtained IMFs in order to get the local amplitudes, frequencies, and orientations. For the decomposition step, we propose two robust 2-D mode decompositions based on non-smooth convex optimization: a "Genuine 2-D" approach, that constrains the local extrema of the IMFs, and a "Pseudo 2-D" approach, which constrains separately the extrema of lines, columns, and diagonals. The spectral analysis step is based on Prony annihilation property that is applied on small square patches of the IMFs. The resulting 2-D Prony-Huang transform is validated on simulated and real data.Comment: 24 pages, 7 figure

Sur la Restauration et l'Edition de Vidéo : Détection de Rayures et Inpainting de Scènes Complexes

The inevitable degradation of visual content such as images and films leads to the goal ofimage and video restoration. In this thesis, we look at two specific restoration problems : the detection ofline scratches in old films and the automatic completion of videos, or video inpainting as it is also known.Line scratches are caused when the film physically rubs against a mechanical part. This origin resultsin the specific characteristics of the defect, such as verticality and temporal persistence. We propose adetection algorithm based on the statistical approach known as a contrario methods. We also proposea temporal filtering step to remove false alarms present in the first detection step. Comparisons withprevious work show improved recall and precision, and robustness with respect to the presence of noiseand clutter in the film.The second part of the thesis concerns video inpainting. We propose an algorithm based on theminimisation of a patch-based functional of the video content. In this framework, we address the followingproblems : extremely high execution times, the correct handling of textures in the video and inpaintingwith moving cameras. We also address some convergence issues in a very simplified inpainting context.La degradation inévitable des contenus visuels (images, films) conduit nécessairementà la tâche de la restauration des images et des vidéos. Dans cetre thèse, nous nous intéresserons àdeux sous-problèmes de restauration : la détection des rayures dans les vieux films, et le remplissageautomatique des vidéos (“inpainting vidéo en anglais).En général, les rayures sont dues aux frottements de la pellicule du film avec un objet lors de laprojection du film. Les origines physiques de ce défaut lui donnent des caractéristiques très particuliers.Les rayures sont des lignes plus ou moins verticales qui peuvent être blanches ou noires (ou parfois encouleur) et qui sont temporellement persistantes, c’est-à-dire qu’elles ont une position qui est continuedans le temps. Afin de détecter ces défauts, nous proposons d’abord un algorithme de détection basésur un ensemble d’approches statistiques appelées les méthodes a contrario. Cet algorithme fournitune détection précise et robuste aux bruits et aux textures présentes dans l’image. Nous proposonségalement une étape de filtrage temporel afin d’écarter les fausses alarmes de la première étape dedétection. Celle-ci améliore la précision de l’algorithme en analysant le mouvement des détections spatiales.L’ensemble de l’algorithme (détection spatiale et filtrage temporel) est comparé à des approchesde la littérature et montre un rappel et une précision grandement améliorés.La deuxième partie de cette thèse est consacrée à l’inpainting vidéo. Le but ici est de remplirune région d’une vidéo avec un contenu qui semble visuellement cohérent et convaincant. Il existeune pléthore de méthodes qui traite ce problème dans le cas des images. La littérature dans le casdes vidéos est plus restreinte, notamment car le temps d’exécution représente un véritable obstacle.Nous proposons un algorithme d’inpainting vidéo qui vise l’optimisation d’une fonctionnelle d’énergiequi intègre la notion de patchs, c’est-à-dire des petits cubes de contenu vidéo. Nous traitons d’abord leprobl’‘eme du temps d’exécution avant d’attaquer celui de l’inpainting satisfaisant des textures dans lesvidéos. Nous traitons également le cas des vidéos dont le fond est en mouvement ou qui ont été prisesavec des caméras en mouvement. Enfin, nous nous intéressons à certaines questions de convergencede l’algorithme dans des cas très simplifiés

Understanding and advancing PDE-based image compression

This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf

Understanding and advancing PDE-based image compression

This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf

Image analysis for extracapsular hip fracture surgery

PhD ThesisDuring the implant insertion phase of extracapsular hip fracture surgery, a surgeon visually inspects digital radiographs to infer the best position for the implant. The inference is made by “eye-balling”. This clearly leaves room for trial and error which is not ideal for the patient. This thesis presents an image analysis approach to estimating the ideal positioning for the implant using a variant of the deformable templates model known as the Constrained Local Model (CLM). The Model is a synthesis of shape and local appearance models learned from a set of annotated landmarks and their corresponding local patches extracted from digital femur x-rays. The CLM in this work highlights both Principal Component Analysis (PCA) and Probabilistic PCA as regularisation components; the PPCA variant being a novel adaptation of the CLM framework that accounts for landmark annotation error which the PCA version does not account for. Our CLM implementation is used to articulate 2 clinical metrics namely: the Tip-Apex Distance and Parker’s Ratio (routinely used by clinicians to assess the positioning of the surgical implant during hip fracture surgery) within the image analysis framework. With our model, we were able to automatically localise signi cant landmarks on the femur, which were subsequently used to measure Parker’s Ratio directly from digital radiographs and determine an optimal placement for the surgical implant in 87% of the instances; thereby, achieving fully automatic measurement of Parker’s Ratio as opposed to manual measurements currently performed in the surgical theatre during hip fracture surgery

Face pose estimation with automatic 3D model creation for a driver inattention monitoring application

Texto en inglés y resumen en inglés y españolRecent studies have identified inattention (including distraction and drowsiness) as the main cause of accidents, being responsible of at least 25% of them. Driving distraction has been less studied, since it is more diverse and exhibits a higher risk factor than fatigue. In addition, it is present over half of the inattention involved crashes. The increased presence of In Vehicle Information Systems (IVIS) adds to the potential distraction risk and modifies driving behaviour, and thus research on this issue is of vital importance. Many researchers have been working on different approaches to deal with distraction during driving. Among them, Computer Vision is one of the most common, because it allows for a cost effective and non-invasive driver monitoring and sensing. Using Computer Vision techniques it is possible to evaluate some facial movements that characterise the state of attention of a driver. This thesis presents methods to estimate the face pose and gaze direction of a person in real-time, using a stereo camera as a basic for assessing driver distractions. The methods are completely automatic and user-independent. A set of features in the face are identified at initialisation, and used to create a sparse 3D model of the face. These features are tracked from frame to frame, and the model is augmented to cover parts of the face that may have been occluded before. The algorithm is designed to work in a naturalistic driving simulator, which presents challenging low light conditions. We evaluate several techniques to detect features on the face that can be matched between cameras and tracked with success. Well-known methods such as SURF do not return good results, due to the lack of salient points in the face, as well as the low illumination of the images. We introduce a novel multisize technique, based on Harris corner detector and patch correlation. This technique benefits from the better performance of small patches under rotations and illumination changes, and the more robust correlation of the bigger patches under motion blur. The head rotates in a range of ±90º in the yaw angle, and the appearance of the features change noticeably. To deal with these changes, we implement a new re-registering technique that captures new textures of the features as the face rotates. These new textures are incorporated to the model, which mixes the views of both cameras. The captures are taken at regular angle intervals for rotations in yaw, so that each texture is only used in a range of ±7.5º around the capture angle. Rotations in pitch and roll are handled using affine patch warping. The 3D model created at initialisation can only take features in the frontal part of the face, and some of these may occlude during rotations. The accuracy and robustness of the face tracking depends on the number of visible points, so new points are added to the 3D model when new parts of the face are visible from both cameras. Bundle adjustment is used to reduce the accumulated drift of the 3D reconstruction. We estimate the pose from the position of the features in the images and the 3D model using POSIT or Levenberg-Marquardt. A RANSAC process detects incorrectly tracked points, which are not considered for pose estimation. POSIT is faster, while LM obtains more accurate results. Using the model extension and the re-registering technique, we can accurately estimate the pose in the full head rotation range, with error levels that improve the state of the art. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area, parameter which gives much information about the distraction pattern of the driver. The resulting gaze estimation algorithm proposed in this thesis has been tested on a set of driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests. The driver?s fixation statistics obtained with the proposed system show how the utilisation of IVIS influences the distraction pattern of the drivers, increasing reaction times and affecting the fixation of attention on the road and the surroundings

Face pose estimation with automatic 3D model creation for a driver inattention monitoring application

Texto en inglés y resumen en inglés y españolRecent studies have identified inattention (including distraction and drowsiness) as the main cause of accidents, being responsible of at least 25% of them. Driving distraction has been less studied, since it is more diverse and exhibits a higher risk factor than fatigue. In addition, it is present over half of the inattention involved crashes. The increased presence of In Vehicle Information Systems (IVIS) adds to the potential distraction risk and modifies driving behaviour, and thus research on this issue is of vital importance. Many researchers have been working on different approaches to deal with distraction during driving. Among them, Computer Vision is one of the most common, because it allows for a cost effective and non-invasive driver monitoring and sensing. Using Computer Vision techniques it is possible to evaluate some facial movements that characterise the state of attention of a driver. This thesis presents methods to estimate the face pose and gaze direction of a person in real-time, using a stereo camera as a basic for assessing driver distractions. The methods are completely automatic and user-independent. A set of features in the face are identified at initialisation, and used to create a sparse 3D model of the face. These features are tracked from frame to frame, and the model is augmented to cover parts of the face that may have been occluded before. The algorithm is designed to work in a naturalistic driving simulator, which presents challenging low light conditions. We evaluate several techniques to detect features on the face that can be matched between cameras and tracked with success. Well-known methods such as SURF do not return good results, due to the lack of salient points in the face, as well as the low illumination of the images. We introduce a novel multisize technique, based on Harris corner detector and patch correlation. This technique benefits from the better performance of small patches under rotations and illumination changes, and the more robust correlation of the bigger patches under motion blur. The head rotates in a range of ±90º in the yaw angle, and the appearance of the features change noticeably. To deal with these changes, we implement a new re-registering technique that captures new textures of the features as the face rotates. These new textures are incorporated to the model, which mixes the views of both cameras. The captures are taken at regular angle intervals for rotations in yaw, so that each texture is only used in a range of ±7.5º around the capture angle. Rotations in pitch and roll are handled using affine patch warping. The 3D model created at initialisation can only take features in the frontal part of the face, and some of these may occlude during rotations. The accuracy and robustness of the face tracking depends on the number of visible points, so new points are added to the 3D model when new parts of the face are visible from both cameras. Bundle adjustment is used to reduce the accumulated drift of the 3D reconstruction. We estimate the pose from the position of the features in the images and the 3D model using POSIT or Levenberg-Marquardt. A RANSAC process detects incorrectly tracked points, which are not considered for pose estimation. POSIT is faster, while LM obtains more accurate results. Using the model extension and the re-registering technique, we can accurately estimate the pose in the full head rotation range, with error levels that improve the state of the art. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area, parameter which gives much information about the distraction pattern of the driver. The resulting gaze estimation algorithm proposed in this thesis has been tested on a set of driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests. The driver?s fixation statistics obtained with the proposed system show how the utilisation of IVIS influences the distraction pattern of the drivers, increasing reaction times and affecting the fixation of attention on the road and the surroundings

A novel parallel algorithm for surface editing and its FPGA implementation

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySurface modelling and editing is one of important subjects in computer graphics. Decades of research in computer graphics has been carried out on both low-level, hardware-related algorithms and high-level, abstract software. Success of computer graphics has been seen in many application areas, such as multimedia, visualisation, virtual reality and the Internet. However, the hardware realisation of OpenGL architecture based on FPGA (field programmable gate array) is beyond the scope of most of computer graphics researches. It is an uncultivated research area where the OpenGL pipeline, from hardware through the whole embedded system (ES) up to applications, is implemented in an FPGA chip. This research proposes a hybrid approach to investigating both software and hardware methods. It aims at bridging the gap between methods of software and hardware, and enhancing the overall performance for computer graphics. It consists of four parts, the construction of an FPGA-based ES, Mesa-OpenGL implementation for FPGA-based ESs, parallel processing, and a novel algorithm for surface modelling and editing. The FPGA-based ES is built up. In addition to the Nios II soft processor and DDR SDRAM memory, it consists of the LCD display device, frame buffers, video pipeline, and algorithm-specified module to support the graphics processing. Since there is no implementation of OpenGL ES available for FPGA-based ESs, a specific OpenGL implementation based on Mesa is carried out. Because of the limited FPGA resources, the implementation adopts the fixed-point arithmetic, which can offer faster computing and lower storage than the floating point arithmetic, and the accuracy satisfying the needs of 3D rendering. Moreover, the implementation includes Bézier-spline curve and surface algorithms to support surface modelling and editing. The pipelined parallelism and co-processors are used to accelerate graphics processing in this research. These two parallelism methods extend the traditional computation parallelism in fine-grained parallel tasks in the FPGA-base ESs. The novel algorithm for surface modelling and editing, called Progressive and Mixing Algorithm (PAMA), is proposed and implemented on FPGA-based ES’s. Compared with two main surface editing methods, subdivision and deformation, the PAMA can eliminate the large storage requirement and computing cost of intermediated processes. With four independent shape parameters, the PAMA can be used to model and edit freely the shape of an open or closed surface that keeps globally the zero-order geometric continuity. The PAMA can be applied independently not only FPGA-based ESs but also other platforms. With the parallel processing, small size, and low costs of computing, storage and power, the FPGA-based ES provides an effective hybrid solution to surface modelling and editing