Search

Opening and closings are examined in depth. The notion of structural opening, which constitutes a basis for the class of all openings (and likewise for closings) is introduced. A second important class of openings is constituted by the adjunctional openings. Annular openings, inf-overfilters and incidence structures are also explained.

The use of substrates using or biochar as a component is a viable alternative in the production of forest seedlings, due to improvements in physical-chemical characteristics. In view of the above, the objective of this study was to evaluate the effect of particle size and the proportion of biochar bark from cariacar brasiliense on the production of Eucalyptus urophylla seedlings. The biochar was used in four particle sizes (> 0.5; 0.5-1.0; 1.0-2.0; 2.0-4.0 mm), mixed with the commercial substrate @bioplant (Sphagnum peat, coconut fiber, rice husks, pine husks and vermiculite) in five proportions: 0, 25, 50, 75%, 100% (v / v). Biodegradable as physical properties, particle density, bulk density and porosity of the tested substrates. The physiological development of the changes, on the other hand, was analyzed using the variables: germination percentage, emergence speed index, height, neck diameter, height / neck diameter ratio, articulated root length, dry root mass and dry mass for 120 days, correlating with the technical and chemical characteristics of the biochar used. The addition of biochar in smaller proportions (0,5; 0,5-1,0; 1,0-2,0; 2,0-4,0 mm), misturados ao substrato comercial @bioplant (turfa de Sphagnum, fibra de Coco, casca de arroz, casca de pinus e vermiculita) em cinco proporções: 0, 25, 50, 75%, 100% (v/v). Avaliou-se as propriedades físicas, densidade de partícula, densidade aparente e porosidade dos substratos testados. Já o desenvolvimento fisiológico da muda foi analisado através das variáveis: porcentagem de germinação, índice de velocidade de emergência, altura, diâmetro de colo, relação altura / diâmetro de colo, comprimento de raiz pivotante, massa seca da raiz e massa seca aérea aos 120 dias, correlacionando com as características físicas e químicas do biochar utilizado. A adição do biochar em menores proporções (< 25%) com o substrato comercial, confere ao substrato melhorias nos atributos físico-químicos, como o aumento da porosidade. Conclui-se que os melhores resultados fisiológicos para a espécie em estudo foram obtidos utilizando granulometrias de biochar entre 1 e 4 mm independente da proporção e biochar aplicada.&nbsp

In order to extract geometric information from images, suitable operators must be constructed. After a discussion of convexity and geodesic distance, the important notion of metric dilation is introduced, followed by that of distance transforms. Sections are then devoted to geodesic and conditional operators, granulometries, connectivity and skeletons. A final section considers discrete metric spaces.

The mathematical morphology of grey-level images has many special features, which are the subject of this chapter. A general procedure for proceeding from binary to grey-level morphology was presented in an earlier chapter. Here, each aspect of the morphological analysis of grey-level images is examined in depth.

Opening and closings are examined in depth. The notion of structural opening, which constitutes a basis for the class of all openings (and likewise for closings) is introduced. A second important class of openings is constituted by the adjunctional openings. Annular openings, inf-overfilters and incidence structures are also explained.

[EN] Background and objective Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become the inputs to shallow and deep Gaussian process classifiers which achieve an accurate prediction of cancer. Results We have used a real and unique dataset. The dataset is composed of 60 Whole Slide Images. For a five fold cross validation, shallow and deep Gaussian Processes obtain area under ROC curve values higher than 0.98. They outperform current state of the art patch based shallow classifiers and are very competitive to the best performing deep learning method. Models were also compared on 17 Whole Slide test Images using the FROC curve. With the cost of one false positive, the best performing method, the one layer Gaussian process, identifies 83.87% (sensitivity) of all annotated cancer in the Whole Slide Image. This result corroborates the quality of the extracted features, no more than a layer is needed to achieve excellent generalization results. Conclusion Two new descriptors to extract morphological features from histological images have been proposed. They collect very relevant information for cancer detection. From these descriptors, shallow and deep Gaussian Processes are capable of extracting the complex structure of prostate histological images. The new space/descriptor/classifier paradigm outperforms state-of-art shallow classifiers. Furthermore, despite being much simpler, it is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external database, This work was supported by the Ministerio de Economia y Competitividad through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA Corporation

Prosopis alba (algarrobo) flours are traditional food resources from Argentina. The aim of this work was to determine the effect of particle sizes of Prosopis flour on its chemical composition and functional properties. Flours were obtained by mechanical methods (F1 to F4, >840, 840 to 500, 500 to 149 and

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out., This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE., In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD., En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.

In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.

International audience; In the framework of the Silent Wall ANR project, the CMM and the US2B are associated in order to characterize and to model fibrous media studying 3D images acquired with an X-Ray microtomograph used by the US2B. The device can make 3D images of maximal size 2304 3 voxels with resolutions in the range of 2-15 μm. Using mathematical morphology, measurements on the 3D X-Ray CT images are performed to characterize materials. For example, measuring the covariance on these images of an acoustic insulating material made of wooden fibers highlights the transverse isotropy of the fibers orientations in the xOy planes perpendicular to the compression Oz axis. Moreover, it is possible to extract other morphological properties, such as the size distribution either of the fibers or of the pores by estimating the morphological opening granulometry of the considered medium. Finally the morphological tortuosity of the fibrous and porous networks are estimated from geodesic propagations. The tortuosity is a parameter which can help to link physical, acoustic, and morphological properties of the material. Moreover the effects of the boundary layer, where viscous and thermal damping of the sound take place, are studied from the point of view of the tortuosity.

The Max-Tree designed for morphological attribute filtering in image processing, is a data structure in which the nodes represent connected components for all threshold levels in a data set. Attribute filters compute some attribute describing the shape or size of each connected component and then decide which components to keep or to discard. In this paper, we augment the basic Max-Tree data structure such that interactive volumetric filtering and visualization becomes possible. We introduce extensions that allow 1) direct, splatting-based, volume rendering; 2) representation of the Max-Tree on graphics hardware; and 3) fast active cell selection for isosurface generation. In all three cases, we can use the Max-Tree representation for visualization directly, without needing to reconstruct the volumetric data explicitly. We show that both filtering and visualization can be performed at interactive frame rates, ranging between 2.4 and 32 frames per seconds. In contrast, a standard texture-based volume visualization method manages only between 0.5 and 1.8 frames per second. For isovalue browsing, the experimental results show that the performance is comparable to the performance of an interval tree, where our method has the advantage that both filter threshold browsing and isolevel browsing are fast. It is shown that the methods using graphics hardware can be extended to other connected filters.

In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain.

In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain.

A new theoretical development is presented for handling the over-segmentation problem in partitioning-based connected openings. The definition we propose treats singletons generated with the earlier method, as elements of a larger connected component. Unlike the existing formalism, this new method allows detection of filamentous structures linking larger objects. This is demonstrated in attribute filtering on neuron images.

In this paper we describe a rotation-invariant multi-scale morphological method for texture analysis. Compared with existing methods our method has three advantages. First, it can be implemented efficiently. Furthermore, our method can be used for the computation of size and strict shape attributes, which we use for the computation of 2-D size-shape pattern spectra. Finally, our method is rotation-invariant. Although the latter can also be approximated by morphological methods by using structuring elements at different angles, this tends to be computationally intensive.

This paper describes a visual processing technique for automatic frog (Xenopus Laevis sp.) localization and identification. The problem of frog identification is to process and classify an unknown frog image to determine the identity which is recorded previously on an image database. The frog skin pattern (i.e. texture) provides a unique feature for identification. Hence, the study investigates three different kind of features (i.e. Gabor filters, granulometry, threshold set compactness) to extract texture information. The classifier is built on nearest neighbor principle; it assigns the query feature to the database feature which has the minimum distance. Hence, the study investigates different distance measures and compares their performance. The detailed results show that the most successful feature and distance measure is granulometry and weighted L1 norm for the frog identification using skin texture features. Publisher's Version

International audience; This paper presents a fast, one-scan algorithm for 1-D morphological opening on 2-D support. The algorithm is further extended to compute the pattern spectrum during a single image scan. The structuring element (SE) can be oriented under arbitrary angle that makes it possible to perform different orientation-involved image analysis, such as the local angle extraction, directional granulometry, etc. The algorithm processes an image in constant time regardless the SE orientation and size in one scan, with minimal latency and very low memory requirements. For pattern spectra, the C-implementation yields an experimental speed-up of 27× compared to other suitable solutions. Aforementioned properties allow for efficient implementation on hardware platforms such as GPU or FPGA that opens a new opportunity of parallel computation, and consequently, further speed-up.

In this work we study the framework of mathematical morphology on simplicial complex spaces. Simplicial complexes are a versatile and widely used structure to represent multidimensional data, such as meshes, that are tridimensional complexes, or graphs, that can be interpreted as bidimensional complexes. Mathematical morphology is one of the most powerful frameworks for image processing, including the processing of digital structures, and is heavily used for many applications. However, mathematical morphology operators on simplicial complex spaces is not a concept fully developped in the literature. In this work, we review some classical operators from simplicial complexes under the light of mathematical morphology, to show that they are morphology operators. We define some basic lattices and operators acting on these lattices: dilations, erosions, openings, closings and alternating sequential filters, including their extension to weighted simplexes. However, the main contributions of this work are what we called dimensional operators, small, versatile operators that can be used to define new operators on simplicial complexes, while mantaining properties from mathematical morphology. These operators can also be used to express virtually any operator from the literature. We illustrate all the defined operators and compare the alternating sequential filters against filters defined in the literature, where our filters show better results for removal of small, intense, noise from binary images, Dans ce travail, nous étudions le cadre de la morphologie mathématique sur les complexes simpliciaux. Complexes simpliciaux sont une structure versatile et largement utilisée pour représenter des données multidimensionnelles, telles que des maillages, qui sont des complexes tridimensionnels, ou des graphes, qui peuvent être interprétées comme des complexes bidimensionnels. La morphologie mathématique est l'un des cadres les plus puissants pour le traitement de l'image, y compris le traitement des structures numériques, et est largement utilisé pour de nombreuses applications. Toutefois, les opérateurs de morphologie mathématique sur des espaces complexes simpliciaux n'est pas un concept entièrement développé dans la littérature. Dans ce travail, nous passons en revue certains opérateurs classiques des complexes simpliciaux sous la lumière de la morphologie mathématique, de montrer qu'ils sont des opérateurs de morphologie. Nous définissons certains treillis de base et les opérateurs agissant sur ces treillis: dilatations, érosions, ouvertures, fermetures et filtres alternés séquentiels, et aussi leur extension à simplexes pondérés. Cependant, les principales contributions de ce travail sont ce que nous appelions les opérateurs dimensionnels, petites et polyvalents opérateurs qui peuvent être utilisés pour définir de nouveaux opérateurs sur les complexes simpliciaux, qui garde les propriétés de la morphologie mathématique. Ces opérateurs peuvent également être utilisés pour exprimer pratiquement n'importe quel opérateur dans la littérature. Nous illustrons les opérateurs définis et nous comparons les filtres alternés séquentiels contre filtres définis dans la littérature, où nos filtres présentent de meilleurs résultats pour l'enlèvement du petit, intense bruit des images binaires

ISBN du volume : 978-146732533-2; International audience; This paper deals with a dedicated hardware architecture for 1-D morphological opening and pattern spectrum. These operators allow extraction and measurement of 1-D features in images that is a commonly used technique in image analysis and texture classification. The architecture is based on a recently proposed opening algorithm and makes it possible to obtain arbitrary-oriented opening and granulometry at the same time. Respecting a sequential data access, several instances with different orientation can run in parallel on a single input dataflow, increasing thus the performance (experimentally 414 Mpx/s per opening). It opens applicability of traditionally costly operators in embedded, industrial applications.

The eolian field complex and the emerged beach of Sonabia (Eastern of Cantabria, Bay of Biscay) are studied from the morphologic and sedimentological (texture and the bioclastic component) point of view. This carbonate fraction is an important index that allows to deduce the relative age of several eolian systems. The main eolian systems distiguished are: fore dune belt, irregular eolian complex, isolated transverse dune, blowouts and barjanic and longitudinal dune field. Coalescing barjanic and longitudinal dunes is due to the decomposition of wind structure. Eolian sands are finer, better sorted and more negative skeweed than the adjoining beach sands. The general negative skewness is due to the polymodal populations concentrated in short size intervals. Barjanic and longitudinal dunes so as the isolated transverse one are transgresive dunes placed in the early present century and blowouts are activated recently, whereas the others are posterior to the flandrian transgression. Se estudian los campos eólicos de la playa de Sonabia (E de Cantabria, Golfo de Vizcaya), desde el punto de vista de su clasificación y caracterización morfológica y sedimentología (textura y contenido carbonatado bioclástico como parámetro indicador de la edad relativa de las dunas), incluyendo la playa inter y supramareal. Se han diferenciado: cordones dunares, campo irregular de posplaya, duna transversal aislada, depresiones de deflación «

The eolian field complex and the emerged beach of Sonabia (Eastern of Cantabria, Bay of Biscay) are studied from the morphologic and sedimentological (texture and the bioclastic component) point of view. This carbonate fraction is an important index that allows to deduce the relative age of several eolian systems. The main eolian systems distiguished are: fore dune belt, irregular eolian complex, isolated transverse dune, blowouts and barjanic and longitudinal dune field. Coalescing barjanic and longitudinal dunes is due to the decomposition of wind structure. Eolian sands are finer, better sorted and more negative skeweed than the adjoining beach sands. The general negative skewness is due to the polymodal populations concentrated in short size intervals. Barjanic and longitudinal dunes so as the isolated transverse one are transgresive dunes placed in the early present century and blowouts are activated recently, whereas the others are posterior to the flandrian transgression.Se estudian los campos eólicos de la playa de Sonabia (E de Cantabria, Golfo de Vizcaya), desde el punto de vista de su clasificación y caracterización morfológica y sedimentología (textura y contenido carbonatado bioclástico como parámetro indicador de la edad relativa de las dunas), incluyendo la playa inter y supramareal. Se han diferenciado: cordones dunares, campo irregular de posplaya, duna transversal aislada, depresiones de deflación «

Connected filters are edge-preserving morphological operators, which rely on a notion of connectivity. This is usually the standard 4 and 8-connectivity, which is often too rigid since it cannot model generalized groupings such as object clusters or partitions. In the set-theoretical framework of connectivity, these groupings are modeled by the more general second-generation connectivity. In this paper, we present both an extension of this theory, and provide an efficient algorithm based on the Max-Tree to compute attribute filters based on these connectivities. We first look into the drawbacks of the existing framework that separates clustering and partitioning and is directly dependent on the properties of a preselected operator. We then propose a new type of second-generation connectivity termed mask-based connectivity which eliminates all previous dependencies and extends the ways the image domain can be connected. A previously developed Dual-Input Max-Tree algorithm for area openings is adapted for the wider class of attribute filters on images characterized by second-generation connectivity. CPU-times for the new algorithm are comparable to the original algorithm, typically deviating less than 10 percent either way.

In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimensional spaces, similar to scale spaces, which will be called attribute-spaces. Though some properties of connected filters are lost, granulometries can be defined under certain conditions, and pattern spectra in most cases. The advantage of this approach is that regions can be split into constituent parts before filtering more naturally than by using partitioning connectivities. Furthermore, the approach allows dealing with overlap, which is impossible in connectivity. A theoretical comparison to hyperconnectivity suggests the new concept is different. The theoretical results are illustrated by several examples. These show how attribute-space connected filters merge the ability of filtering based on local structure using classical, structuring-element-based filters to the object-attribute-based filtering of connected filters, and how this differs from similar attempts using second-generation connectivity. 2006 Elsevier B.V. All rights reserved.

A variant of morphological attribute filters is developed, in which the attribute on which filtering is based, is no longer a scalar, as is usual, but a vector. This leads to new granulometries and associated pattern spectra. When the vector-attribute used is a shape descriptor, the resulting granulometries filter an image based on a shape or shape family instead of one or more scalar values.

In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimension spaces, similar to scale spaces which will be called attribute spaces. Though some properties of connected filters are lost, granulometries can be defined under certain conditions, and pattern spectra in most cases. The advantage of this approach is that regions can be split into constituent parts before filtering more naturally than by using partitioning connectivities.

In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimension spaces, similar to scale spaces which will be called attribute spaces. Though some properties of connected filters are lost, granulometries can be defined under certain conditions, and pattern spectra in most cases. The advantage of this approach is that regions can be split into constituent parts before filtering more naturally than by using partitioning connectivities.

A new theoretical development is presented for handling the over-segmentation problem in partitioning-based connected openings. The definition we propose treats singletons generated with the earlier method, as elements of a larger connected component. Unlike the existing formalism, this new method allows detection of filamentous structures linking larger objects. This is demonstrated in attribute filtering on neuron images.

The work presented in this paper introduces a novel method for second-order connected attribute filtering using Max-Trees. The proposed scheme is generated in a recursive manner from two images, the original and a modified copy by an either extensive or an anti-extensive operator. The tree structure is shaped by the component hierarchy of the modified image while the node attributes are based on the connected components of the original image. Attribute filtering of second-order connected sets proceeds as in conventional Max-Trees with no further computational overhead.

In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimension spaces, similar to scale spaces which will be called attribute spaces. Though some properties of connected filters are lost, granulometries can be defined under certain conditions, and pattern spectra in most cases. The advantage of this approach is that regions can be split into constituent parts before filtering more naturally than by using partitioning connectivities.

A new theoretical development is presented for handling the over-segmentation problem in partitioning-based connected openings. The definition we propose treats singletons generated with the earlier method, as elements of a larger connected component. Unlike the existing formalism, this new method allows detection of filamentous structures linking larger objects. This is demonstrated in attribute filtering on neuron images.