Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Segmentation-assisted detection of dirt impairments in archived film sequences

A novel segmentation-assisted method for film dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection for a wide variety of test material

Modeling, Estimation, and Pattern Analysis of Random Texture on 3-D Surfaces

To recover 3-D structure from a shaded and textural surface image involving textures, neither the Shape-from-shading nor the Shape-from-texture analysis is enough, because both radiance and texture information coexist within the scene surface. A new 3-D texture model is developed by considering the scene image as the superposition of a smooth shaded image and a random texture image. To describe the random part, the orthographical projection is adapted to take care of the non-isotropic distribution function of the intensity due to the slant and tilt of a 3-D textures surface, and the Fractional Differencing Periodic (FDP) model is chosen to describe the random texture, because this model is able to simultaneously represent the coarseness and the pattern of the 3-D texture surface, and enough flexible to synthesize both long-term and short-term correlation structures of random texture. Since the object is described by the model involving several free parameters and the values of these parameters are determined directly from its projected image, it is possible to extract 3-D information and texture pattern directly from the image without any preprocessing. Thus, the cumulative error obtained from each pre-processing can be minimized. For estimating the parameters, a hybrid method which uses both the least square and the maximum likelihood estimates is applied and the estimation of parameters and the synthesis are done in frequency domain. Among the texture pattern features which can be obtained from a single surface image, Fractal scaling parameter plays a major role for classifying and/or segmenting the different texture patterns tilted and slanted due to the 3-dimensional rotation, because of its rotational and scaling invariant properties. Also, since the Fractal scaling factor represents the coarseness of the surface, each texture pattern has its own Fractal scale value, and particularly at the boundary between the different textures, it has relatively higher value to the one within a same texture. Based on these facts, a new classification method and a segmentation scheme for the 3-D rotated texture patterns are develope

Co-adaptive myoelectric control for upper limb prostheses

[ES] Mucha gente en el mundo se ve afectada por la pérdida de una extremidad (las predicciones estiman que en 2050 habrá más de 3 millones de personas afectadas únicamente en los Estados Unidos de América). A pesar de la continua mejora en las técnicas de amputación y la prostética, vivir sin una extremidad sigue limitando las actividades de los afectados en su vida diaria, provocando una disminución en su calidad de vida. En este trabajo nos centramos en los casos de amputaciones de extremidades superiores, entendiendo por ello la pérdida de cualquier parte del brazo o antebrazo. Esta tesis trata sobre el control mioeléctrico (potenciales eléctricos superficiales generados por la contracción de los músculos) de prótesis de extremidades superiores. Los estudios en este campo han crecido exponencialmente en las últimas décadas intentando reducir el hueco entre la parte investigadora más dinámica y propensa a los cambios e innovación (por ejemplo, usando técnicas como la inteligencia artificial) y la industria prostética, con una gran inercia y poco propensa a introducir cambios en sus controladores y dispositivos. El principal objetivo de esta tesis es desarrollar un nuevo controlador implementable basado en filtros adaptativos que supere los principales problemas del estado del arte. Desde el punto de vista teórico, podríamos considerar dos contribuciones principales. Primero, proponemos un nuevo sistema para modelar la relación entre los patrones de la señales mioélectricas y los movimientos deseados; este nuevo modelo tiene en cuenta a la hora de estimar la posición actual el valor de los estados pasados generando una nueva sinergia entre máquina y ser humano. En segundo lugar, introducimos un nuevo paradigma de entrenamiento más eficiente y personalizado autónomamente, el cual puede aplicarse no sólo a nuestro nuevo controlador, sino a otros regresores disponibles en la literatura. Como consecuencia de este nuevo protocolo, la estructura humano-máquina difiere con respecto del actual estado del arte en dos características: el proceso de aprendizaje del controlador y la estrategia para la generación de las señales de entrada. Como consecuencia directa de todo esto, el diseño de la fase experimental resulta mucho más complejo que con los controladores tradicionales. La dependencia de la posición actual de la prótesis con respecto a estados pasados fuerza a la realización de todos los experimentos de validación del nuevo controlador en tiempo real, algo costoso en recursos tanto humanos como de tiempo. Por lo tanto, una gran parte de esta tesis está dedicada al trabajo de campo necesario para validar el nuevo modelo y estrategia de entrenamiento. Como el objetivo final es proveer un nuevo controlador implementable, la última parte de la tesis está destinada a testear los métodos propuestos en casos reales, tanto en entornos simulados para validar su robustez ante rutinas diarias, como su uso en dispositivos prostéticos comerciales. Como conclusión, este trabajo propone un nuevo paradigma de control mioélectrico para prótesis que puede ser implementado en una prótesis real. Una vez se ha demostrado la viabilidad del sistema, la tesis propone futuras líneas de investigación, mostrando algunos resultados iniciales.[CA] Molta gent en el món es veu afectada per la pèrdua d'una extremitat (les prediccions estimen que en 2050 hi haurà més de 3 milions de persones afectades únicament als Estats Units d'Amèrica). Malgrat la contínua millora en les tècniques d'amputació i la prostètica, viure sense una extremitat continua limitant les activitats dels afectats en la seua vida diària, provocant una disminució en la seua qualitat de vida. En aquest treball ens centrem en els casos d'amputacions d'extremitats superiors, entenent per això la pèrdua de qualsevol part del braç o avantbraç. Aquesta tesi tracta sobre el control mioelèctric (potencials elèctrics superficials generats per la contracció dels músculs) de pròtesis d'extremitats superiors. Els estudis en aquest camp han crescut exponencialment en les últimes dècades intentant reduir el buit entre la part investigadora més dinàmica i propensa als canvis i innovació (per exemple, usant tècniques com la intel·ligència artificial) i la indústria prostètica, amb una gran inèrcia i poc propensa a introduir canvis en els seus controladors i dispositius. Aquesta tesi contribueix a la investigació des de diversos punts de vista. El principal objectiu és desenvolupar un nou controlador basat en filtres adaptatius que supere els principals problemes de l'estat de l'art. Des del punt de vista teòric, podríem considerar dues contribucions principals. Primer, proposem un nou sistema per a modelar la relació entre els patrons de la senyals mioelèctrics i els moviments desitjats; aquest nou model té en compte a l'hora d'estimar la posició actual el valor dels estats passats generant una nova sinergia entre màquina i ésser humà. En segon lloc, introduïm un nou paradigma d'entrenament més eficient i personalitzat autònomament, el qual pot aplicar-se no sols al nostre nou controlador, sinó a uns altres regresors disponibles en la literatura. Com a conseqüència d'aquest nou protocol, l'estructura humà-màquina difereix respecte a l'actual estat de l'art en dues característiques: el procés d'aprenentatge del controlador i l'estratègia per a la generació dels senyals d'entrada. Com a conseqüència directa de tot això, el disseny de la fase experimental resulta molt més complex que amb els controladors tradicionals. La dependència de la posició actual de la pròtesi respecte a estats passats força a la realització de tots els experiments de validació del nou controlador en temps real, una cosa costosa en recursos tant humans com de temps. Per tant, una gran part d'aquesta tesi està dedicada al treball de camp necessari per a validar el nou model i estratègia d'entrenament. Com l'objectiu final és proveir un nou controlador implementable, l'última part de la tesi està destinada a testar els mètodes proposats en casos reals, tant en entorns simulats per a validar la seua robustesa davant rutines diàries, com el seu ús en dispositius prostètics comercials. Com a conclusió, aquest treball proposa un nou paradigma de control mioelèctric per a pròtesi que pot ser implementat en una pròtesi real. Una vegada s'ha demostrat la viabilitat del sistema, la tesi proposa futures línies d'investigació, mostrant alguns resultats inicials.[EN] Many people in the world suffer from the loss of a limb (predictions estimate more than 3 million people by 2050 only in the USA). In spite of the continuous improvement in the amputation rehabilitation and prosthetic restoration, living without a limb keeps limiting the daily life activities leading to a lower quality of life. In this work, we focus in the upper limb amputation case, i.e., the removal of any part of the arm or forearm. This thesis is about upper limb prosthesis control using electromyographic signals (the superficial electric potentials generated during muscle contractions). Studies in this field have grown exponentially in the past decades trying to reduce the gap between a fast growing prosthetic research field, with the introduction of machine learning, and a slower prosthetic industry and limited manufacturing innovation. This thesis contributes to the field from different perspectives. The main goal is to provide and implementable new controller based on adaptive filtering that overcomes the most common state of the art concerns. From the theoretical point of view, there are two main contributions. First, we propose a new system to model the relationship between electromyographic signals and the desired prosthesis movements; this new model takes into account previous states for the estimation of the current position generating a new human-machine synergy. Second, we introduce a new and more efficient autonomously personalized training paradigm, which can benefit not only to our new proposed controller but also other state of the art regressors. As a consequence of this new protocol, the human-machine structure differs with respect to current state of the art in two features: the controller learning process and the input signal generation strategy. As a direct aftereffect of all of this, the experimental phase design results more complex than with traditional controllers. The current state dependency on past states forces the experimentation to be in real time, a very high demanding task in human and time resources. Therefore, a major part of this thesis is the associated fieldwork needed to validate the new model and training strategy. Since the final goal is to provide an implementable new controller, the last part of the thesis is devoted to test the proposed methods in real cases, not only analyzing the robustness and reliability of the controller in real life situations but in real prosthetic devices. As a conclusion, this work provides a new paradigm for the myoelectric prosthetic control that can be implemented in a real device. Once the thesis has proven the system's viability, future work should continue with the development of a physical device where all these ideas are deployed and used by final patients in a daily basis.The work of Carles Igual Bañó to carry out this research and elaborate this dissertation has been supported by the Ministerio de Educación, Cultura y Deporte under the FPU Grant FPU15/02870. One visiting research fellowships (EST18/00544) was also funded by the Ministerio de Educación, Cultura y Deporte of Spain.Igual Bañó, C. (2021). Co-adaptive myoelectric control for upper limb prostheses [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168192TESI

Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L

Dynamic texture synthesis in image and video processing.

Xu, Leilei.Thesis submitted in: October 2007.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 78-84).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Texture and Dynamic Textures --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Image/Video Processing --- p.8Chapter 2.1 --- Bayesian Analysis --- p.8Chapter 2.2 --- Markov Property --- p.10Chapter 2.3 --- Graph Cut --- p.12Chapter 2.4 --- Belief Propagation --- p.13Chapter 2.5 --- Expectation-Maximization --- p.15Chapter 2.6 --- Principle Component Analysis --- p.15Chapter 3 --- Linear Dynamic System --- p.17Chapter 3.1 --- System Model --- p.18Chapter 3.2 --- Degeneracy and Canonical Model Realization --- p.19Chapter 3.3 --- Learning of Dynamic Textures --- p.19Chapter 3.4 --- Synthesizing Dynamic Textures --- p.21Chapter 3.5 --- Summary --- p.21Chapter 4 --- Dynamic Color Texture Synthesis --- p.25Chapter 4.1 --- Related Work --- p.25Chapter 4.2 --- System Model --- p.26Chapter 4.2.1 --- Laplacian Pyramid-based DCTS Model --- p.28Chapter 4.2.2 --- RBF-based DCTS Model --- p.28Chapter 4.3 --- Experimental Results --- p.32Chapter 4.4 --- Summary --- p.42Chapter 5 --- Dynamic Textures using Multi-resolution Analysis --- p.43Chapter 5.1 --- System Model --- p.44Chapter 5.2 --- Multi-resolution Descriptors --- p.46Chapter 5.2.1 --- Laplacian Pyramids --- p.47Chapter 5.2.2 --- Haar Wavelets --- p.48Chapter 5.2.3 --- Steerable Pyramid --- p.49Chapter 5.3 --- Experimental Results --- p.51Chapter 5.4 --- Summary --- p.55Chapter 6 --- Motion Transfer --- p.59Chapter 6.1 --- Problem formulation --- p.60Chapter 6.1.1 --- Similarity on Appearance --- p.61Chapter 6.1.2 --- Similarity on Dynamic Behavior --- p.62Chapter 6.1.3 --- The Objective Function --- p.65Chapter 6.2 --- Further Work --- p.66Chapter 7 --- Conclusions --- p.67Chapter A --- List of Publications --- p.68Chapter B --- Degeneracy in LDS Model --- p.70Chapter B.l --- Equivalence Class --- p.70Chapter B.2 --- The Choice of the Matrix Q --- p.70Chapter B.3 --- Swapping the Column of C and A --- p.71Chapter C --- Probability Density Functions --- p.74Chapter C.1 --- Probability Distribution --- p.74Chapter C.2 --- Joint Probability Distributions --- p.75Bibliography --- p.7