Digital image processing techniques as a tool for evaluating and mapping patinas on granite monuments

Monument conservation is related to the interaction between the original petrological parameters of the rock and external factors in the area where the building is sited, such as weather conditions, pollution, and so on. Depending on the environmental conditions and the characteristics of the materials used, different types of weathering predominate. In all, the appearance of surface crusts constitutes a first stage, whose origin can often be traced to the properties of the material itself. In the present study, different colours of “patinas” were distinguished by defining the threshold levels of greys associated with “pathology” in the histogram. These data were compared to background information and other parameters, such as mineralogical composition, porosity, and so on, as well as other visual signs of deterioration. The result is a map of the pathologies associated with “cover films” on monuments, which generate images by relating colour characteristics to desired properties or zones of interest.

Efficient Model-based 3D Tracking by Using Direct Image Registration

This thesis deals with the problem of efficiently tracking 3D objects in sequences of images. We tackle the efficient 3D tracking problem by using direct image registration. This problem is posed as an iterative optimization procedure that minimizes a brightness error norm. We review the most popular iterative methods for image registration in the literature, turning our attention to those algorithms that use efficient optimization techniques. Two forms of efficient registration algorithms are investigated. The first type comprises the additive registration algorithms: these algorithms incrementally compute the motion parameters by linearly approximating the brightness error function. We centre our attention on Hager and Belhumeur’s factorization-based algorithm for image registration. We propose a fundamental requirement that factorization-based algorithms must satisfy to guarantee good convergence, and introduce a systematic procedure that automatically computes the factorization. Finally, we also bring out two warp functions to register rigid and nonrigid 3D targets that satisfy the requirement. The second type comprises the compositional registration algorithms, where the brightness function error is written by using function composition. We study the current approaches to compositional image alignment, and we emphasize the importance of the Inverse Compositional method, which is known to be the most efficient image registration algorithm. We introduce a new algorithm, the Efficient Forward Compositional image registration: this algorithm avoids the necessity of inverting the warping function, and provides a new interpretation of the working mechanisms of the inverse compositional alignment. By using this information, we propose two fundamental requirements that guarantee the convergence of compositional image registration methods. Finally, we support our claims by using extensive experimental testing with synthetic and real-world data. We propose a distinction between image registration and tracking when using efficient algorithms. We show that, depending whether the fundamental requirements are hold, some efficient algorithms are eligible for image registration but not for tracking.

A set of canonical problems in sloshing. Part 0: Experimental setup and data processing

In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data

In this work, we present a novel method to compensate the movement in images acquired during free breathing using first-pass gadolinium enhanced, myocardial perfusion magnetic resonance imaging (MRI). First, we use independent component analysis (ICA) to identify the optimal number of independent components (ICs) that separate the breathing motion from the intensity change induced by the contrast agent. Then, synthetic images are created by recombining the ICs, but other then in previously published work (Milles et al. 2008), we omit the component related to motion, and therefore, the resulting reference image series is free of motion. Motion compensation is then achieved by using a multi-pass non-rigid image registration scheme. We tested our method on 15 distinct image series (5 patients) consisting of 58 images each and we validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration. The average correlation to the manually obtained curves before registration 0:89 0:11 was increased to 0:98 0:02

Toward Multi-Scale Object-Based Data Fusion

This paper proposes a new methodology for object based 2-D data fu- sion, with a multiscale character. This methodology is intended to be use in agriculture, specifically in the characterization of the water status of different crops, so as to have an appropriate water management at a farm-holding scale. As a first approach to its evaluation, vegetation cover vigor data has been integrated with texture data. For this purpose, NDVI maps have been calculated using a multispectral image and Lacunarity maps from the panchromatic image. Preliminary results show this methodology is viable in the integration and management of large volumes of data, which characterize the behavior of agricultural covers at farm-holding scale.

A Multiresolution Image Alignment Technique Based on Direct Methods for Pose Estimation of Aerial Vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi-Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles' state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle's state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle's state for more than one minute, at real-time frame rates based, only on visual information.

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Multimodal non-rigid image registration methods for therapy and surgery planning

La planificación pre-operatoria se ha convertido en una tarea esencial en cirugías y terapias de marcada complejidad, especialmente aquellas relacionadas con órgano blando. Un ejemplo donde la planificación preoperatoria tiene gran interés es la cirugía hepática. Dicha planificación comprende la detección e identificación precisa de las lesiones individuales y vasos así como la correcta segmentación y estimación volumétrica del hígado funcional. Este proceso es muy importante porque determina tanto si el paciente es un candidato adecuado para terapia quirúrgica como la definición del abordaje a seguir en el procedimiento. La radioterapia de órgano blando es un segundo ejemplo donde la planificación se requiere tanto para la radioterapia externa convencional como para la radioterapia intraoperatoria. La planificación comprende la segmentación de tumor y órganos vulnerables y la estimación de la dosimetría. La segmentación de hígado funcional y la estimación volumétrica para planificación de la cirugía se estiman habitualmente a partir de imágenes de tomografía computarizada (TC). De igual modo, en la planificación de radioterapia, los objetivos de la radiación se delinean normalmente sobre TC. Sin embargo, los avances en las tecnologías de imagen de resonancia magnética (RM) están ofreciendo progresivamente ventajas adicionales. Por ejemplo, se ha visto que el ratio de detección de metástasis hepáticas es significativamente superior en RM con contraste Gd–EOB–DTPA que en TC. Por tanto, recientes estudios han destacado la importancia de combinar la información de TC y RM para conseguir el mayor nivel posible de precisión en radioterapia y para facilitar una descripción precisa de las lesiones del hígado. Con el objetivo de mejorar la planificación preoperatoria en ambos escenarios se precisa claramente de un algoritmo de registro no rígido de imagen. Sin embargo, la gran mayoría de sistemas comerciales solo proporcionan métodos de registro rígido. Las medidas de intensidad de voxel han demostrado ser criterios de similitud de imágenes robustos, y, entre ellas, la Información Mutua (IM) es siempre la primera elegida en registros multimodales. Sin embargo, uno de los principales problemas de la IM es la ausencia de información espacial y la asunción de que las relaciones estadísticas entre las imágenes son homogéneas a lo largo de su domino completo. La hipótesis de esta tesis es que la incorporación de información espacial de órganos al proceso de registro puede mejorar la robustez y calidad del mismo, beneficiándose de la disponibilidad de las segmentaciones clínicas. En este trabajo, se propone y valida un esquema de registro multimodal no rígido 3D usando una nueva métrica llamada Información Mutua Centrada en el Órgano (Organ-Focused Mutual Information metric (OF-MI)) y se compara con la formulación clásica de la Información Mutua. Esto permite mejorar los resultados del registro en áreas problemáticas incorporando información regional al criterio de similitud, beneficiándose de la disponibilidad real de segmentaciones en protocolos estándares clínicos, y permitiendo que la dependencia estadística entre las dos modalidades de imagen difiera entre órganos o regiones. El método propuesto se ha aplicado al registro de TC y RM con contraste Gd–EOB–DTPA así como al registro de imágenes de TC y MR para planificación de radioterapia intraoperatoria rectal. Adicionalmente, se ha desarrollado un algoritmo de apoyo de segmentación 3D basado en Level-Sets para la incorporación de la información de órgano en el registro. El algoritmo de segmentación se ha diseñado específicamente para la estimación volumétrica de hígado sano funcional y ha demostrado un buen funcionamiento en un conjunto de imágenes de TC abdominales. Los resultados muestran una mejora estadísticamente significativa de OF-MI comparada con la Información Mutua clásica en las medidas de calidad de los registros; tanto con datos simulados (p<0.001) como con datos reales en registro hepático de TC y RM con contraste Gd– EOB–DTPA y en registro para planificación de radioterapia rectal usando OF-MI multi-órgano (p<0.05). Adicionalmente, OF-MI presenta resultados más estables con menor dispersión que la Información Mutua y un comportamiento más robusto con respecto a cambios en la relación señal-ruido y a la variación de parámetros. La métrica OF-MI propuesta en esta tesis presenta siempre igual o mayor precisión que la clásica Información Mutua y consecuentemente puede ser una muy buena alternativa en aplicaciones donde la robustez del método y la facilidad en la elección de parámetros sean particularmente importantes. Abstract Pre-operative planning has become an essential task in complex surgeries and therapies, especially for those affecting soft tissue. One example where soft tissue preoperative planning is of high interest is liver surgery. It involves the accurate detection and identification of individual liver lesions and vessels as well as the proper functional liver segmentation and volume estimation. This process is very important because it determines whether the patient is a suitable candidate for surgical therapy and the type of procedure. Soft tissue radiation therapy is a second example where planning is required for both conventional external and intraoperative radiotherapy. It involves the segmentation of the tumor target and vulnerable organs and the estimation of the planned dose. Functional liver segmentations and volume estimations for surgery planning are commonly estimated from computed tomography (CT) images. Similarly, in radiation therapy planning, targets to be irradiated and healthy and vulnerable tissues to be protected from irradiation are commonly delineated on CT scans. However, developments in magnetic resonance imaging (MRI) technology are progressively offering advantages. For instance, the hepatic metastasis detection rate has been found to be significantly higher in Gd–EOB–DTPAenhanced MRI than in CT. Therefore, recent studies highlight the importance of combining the information from CT and MRI to achieve the highest level of accuracy in radiotherapy and to facilitate accurate liver lesion description. In order to improve those two soft tissue pre operative planning scenarios, an accurate nonrigid image registration algorithm is clearly required. However, the vast majority of commercial systems only provide rigid registration. Voxel intensity measures have been shown to be robust measures of image similarity, and among them, Mutual Information (MI) is always the first candidate in multimodal registrations. However, one of the main drawbacks of Mutual Information is the absence of spatial information and the assumption that statistical relationships between images are the same over the whole domain of the image. The hypothesis of the present thesis is that incorporating spatial organ information into the registration process may improve the registration robustness and quality, taking advantage of the clinical segmentations availability. In this work, a multimodal nonrigid 3D registration framework using a new Organ- Focused Mutual Information metric (OF-MI) is proposed, validated and compared to the classical formulation of the Mutual Information (MI). It allows improving registration results in problematic areas by adding regional information into the similitude criterion taking advantage of actual segmentations availability in standard clinical protocols and allowing the statistical dependence between the two modalities differ among organs or regions. The proposed method is applied to CT and T1 weighted delayed Gd–EOB–DTPA-enhanced MRI registration as well as to register CT and MRI images in rectal intraoperative radiotherapy planning. Additionally, a 3D support segmentation algorithm based on Level-Sets has been developed for the incorporation of the organ information into the registration. The segmentation algorithm has been specifically designed for the healthy and functional liver volume estimation demonstrating good performance in a set of abdominal CT studies. Results show a statistical significant improvement of registration quality measures with OF-MI compared to MI with both simulated data (p<0.001) and real data in liver applications registering CT and Gd–EOB–DTPA-enhanced MRI and in registration for rectal radiotherapy planning using multi-organ OF-MI (p<0.05). Additionally, OF-MI presents more stable results with smaller dispersion than MI and a more robust behavior with respect to SNR changes and parameters variation. The proposed OF-MI always presents equal or better accuracy than the classical MI and consequently can be a very convenient alternative within applications where the robustness of the method and the facility to choose the parameters are particularly important.

Assembling models of embryo development: Image analysis and the construction of digital atlases

Digital atlases of animal development provide a quantitative description of morphogenesis, opening the path toward processes modeling. Prototypic atlases offer a data integration framework where to gather information from cohorts of individuals with phenotypic variability. Relevant information for further theoretical reconstruction includes measurements in time and space for cell behaviors and gene expression. The latter as well as data integration in a prototypic model, rely on image processing strategies. Developing the tools to integrate and analyze biological multidimensional data are highly relevant for assessing chemical toxicity or performing drugs preclinical testing. This article surveys some of the most prominent efforts to assemble these prototypes, categorizes them according to salient criteria and discusses the key questions in the field and the future challenges toward the reconstruction of multiscale dynamics in model organisms.

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration

Seed image analysis and taxonomy of Diplotaxis DC. (Brassicaceae, Brassiceae)

The genus Diplotaxis, comprising 32 or 34 species, plus several additional infraspecific taxa, displays a considerable degree of heterogeneity in the morphology, molecular markers, chromosome numbers and geographical amplitude of the species. The taxonomic relationships within the genus Diplotaxis were investigated by phenetic characterisation of germplasm belonging to 27 taxa of the genus, because there is an increasing interest in Diplotaxis, since some of its species (D. tenuifolia, D. muralis) are gathered or cultivated for human consumption, whereas others are frequent arable weeds (D. erucoides) in many European vineyards. Using a computer-aided vision system, 33 morpho-colorimetric features of seeds were electronically measured. The data were used to implement a statistical classifier, which is able to discriminate the taxa within the genus Diplotaxis, in order to compare the resulting species grouping with the current infrageneric systematics of this genus. Despite the high heterogeneity of the samples, due to the great intra-population variability, the stepwise Linear Discriminant Analysis method, applied to distinguish the groups, was able to reach over 80% correct identification. The results obtained allowed us to confirm the current taxonomic position of most taxa and suggested the taxonomic position of others for reconsideration.

Hybrid Contribution of JPEG200 Video Files for Professional Production Centers Reliability and Non-Reliability Transmission File Based in Image Quality Needed

ATM, SDH or satellite have been used in the last century as the contribution network of Broadcasters. However the attractive price of IP networks is changing the infrastructure of these networks in the last decade. Nowadays, IP networks are widely used, but their characteristics do not offer the level of performance required to carry high quality video under certain circumstances. Data transmission is always subject to errors on line. In the case of streaming, correction is attempted at destination, while on transfer of files, retransmissions of information are conducted and a reliable copy of the file is obtained. In the latter case, reception time is penalized because of the low priority this type of traffic on the networks usually has. While in streaming, image quality is adapted to line speed, and line errors result in a decrease of quality at destination, in the file copy the difference between coding speed vs line speed and errors in transmission are reflected in an increase of transmission time. The way news or audiovisual programs are transferred from a remote office to the production centre depends on the time window and the type of line available; in many cases, it must be done in real time (streaming), with the resulting image degradation. The main purpose of this work is the workflow optimization and the image quality maximization, for that reason a transmission model for multimedia files adapted to JPEG2000, is described based on the combination of advantages of file transmission and those of streaming transmission, putting aside the disadvantages that these models have. The method is based on two patents and consists of the safe transfer of the headers and data considered to be vital for reproduction. Aside, the rest of the data is sent by streaming, being able to carry out recuperation operations and error concealment. Using this model, image quality is maximized according to the time window. In this paper, we will first give a briefest overview of the broadcasters requirements and the solutions with IP networks. We will then focus on a different solution for video file transfer. We will take the example of a broadcast center with mobile units (unidirectional video link) and regional headends (bidirectional link), and we will also present a video file transfer file method that satisfies the broadcaster requirements.

Optimización de la Exactitud Geométrica al Integrar Diversas Fuentes de Datos en Proyectos de Inventario Forestal Basados en Escaneo Laser Aerotransportado=Optimizing Geometric Accuracy in Data Source Integration for Airborne Laser Scanning-based Forest Inventory Projects

La mayoría de las aplicaciones forestales del escaneo laser aerotransportado (ALS, del inglés airborne laser scanning) requieren la integración y uso simultaneo de diversas fuentes de datos, con el propósito de conseguir diversos objetivos. Los proyectos basados en sensores remotos normalmente consisten en aumentar la escala de estudio progresivamente a lo largo de varias fases de fusión de datos: desde la información más detallada obtenida sobre un área limitada (la parcela de campo), hasta una respuesta general de la cubierta forestal detectada a distancia de forma más incierta pero cubriendo un área mucho más amplia (la extensión cubierta por el vuelo o el satélite). Todas las fuentes de datos necesitan en ultimo termino basarse en las tecnologías de sistemas de navegación global por satélite (GNSS, del inglés global navigation satellite systems), las cuales son especialmente erróneas al operar por debajo del dosel forestal. Otras etapas adicionales de procesamiento, como la ortorectificación, también pueden verse afectadas por la presencia de vegetación, deteriorando la exactitud de las coordenadas de referencia de las imágenes ópticas. Todos estos errores introducen ruido en los modelos, ya que los predictores se desplazan de la posición real donde se sitúa su variable respuesta. El grado por el que las estimaciones forestales se ven afectadas depende de la dispersión espacial de las variables involucradas, y también de la escala utilizada en cada caso. Esta tesis revisa las fuentes de error posicional que pueden afectar a los diversos datos de entrada involucrados en un proyecto de inventario forestal basado en teledetección ALS, y como las propiedades del dosel forestal en sí afecta a su magnitud, aconsejando en consecuencia métodos para su reducción. También se incluye una discusión sobre las formas más apropiadas de medir exactitud y precisión en cada caso, y como los errores de posicionamiento de hecho afectan a la calidad de las estimaciones, con vistas a una planificación eficiente de la adquisición de los datos. La optimización final en el posicionamiento GNSS y de la radiometría del sensor óptico permitió detectar la importancia de este ultimo en la predicción de la desidad relativa de un bosque monoespecífico de Pinus sylvestris L. ABSTRACT Most forestry applications of airborne laser scanning (ALS) require the integration and simultaneous use of various data sources, pursuing a variety of different objectives. Projects based on remotely-sensed data generally consist in upscaling data fusion stages: from the most detailed information obtained for a limited area (field plot) to a more uncertain forest response sensed over a larger extent (airborne and satellite swath). All data sources ultimately rely on global navigation satellite systems (GNSS), which are especially error-prone when operating under forest canopies. Other additional processing stages, such as orthorectification, may as well be affected by vegetation, hence deteriorating the accuracy of optical imagery’s reference coordinates. These errors introduce noise to the models, as predictors displace from their corresponding response. The degree to which forest estimations are affected depends on the spatial dispersion of the variables involved and the scale used. This thesis reviews the sources of positioning errors which may affect the different inputs involved in an ALS-assisted forest inventory project, and how the properties of the forest canopy itself affects their magnitude, advising on methods for diminishing them. It is also discussed how accuracy should be assessed, and how positioning errors actually affect forest estimation, toward a cost-efficient planning for data acquisition. The final optimization in positioning the GNSS and optical image allowed to detect the importance of the latter in predicting relative density in a monospecific Pinus sylvestris L. forest.