Hyperspectral image representation and processing with binary partition trees

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representatio

Multiscale approach of retinal blood vessels segmentation based on vessels segmentation with different scales

In this work, the authors developed retinal blood vessels segmentation approach using contrast limited adaptive histogram equalization, morphological filtering, k-means clustering, matched filtering for thin and thick vessels selection. The authors also applied matched filtering for thin vessels selection using the kernels which were built in order to determine the existence of line segments with different length and orientatio

Segmentation of Facade Images using Ultimate Opening

International audienceIn recent years, automatic reconstruction, modeling and interpretation of urban environment and building structures is an area which gained interest. Urban environment's modeling allows developing different applications. This analysis extracts and reconstructs windows, doors and ornaments to provide rich information of the buildings adding real- ism for visualization. Our goal is the automation of the fac ̧ades interpretation from images; especially to detection/extraction structural objects mainly windows. We propose connected-component (CC) segmentation to detect of facade structures. The segmentation is based on a morpho- logical operator named ultimate opening

3D vasculature segmentation using localized hybrid level-set method

Background: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. Methods: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Results: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. Conclusions: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does

Reverse-engineering of architectural buildings based on an hybrid modeling approach

We thank MENSI and REALVIZ companies for their helpful comments and the following people for providing us images from their works: Francesca De Domenico (Fig. 1), Kyung-Tae Kim (Fig. 9). The CMN (French national center of patrimony buildings) is also acknowledged for the opportunity given to demonstrate our approach on the Hotel de Sully in Paris. We thank Tudor Driscu for his help on the English translation.This article presents a set of theoretical reflections and technical demonstrations that constitute a new methodological base for the architectural surveying and representation using computer graphics techniques. The problem we treated relates to three distinct concerns: the surveying of architectural objects, the construction and the semantic enrichment of their geometrical models, and their handling for the extraction of dimensional information. A hybrid approach to 3D reconstruction is described. This new approach combines range-based modeling and image-based modeling techniques; it integrates the concept of architectural feature-based modeling. To develop this concept set up a first process of extraction and formalization of architectural knowledge based on the analysis of architectural treaties is carried on. Then, the identified features are used to produce a template shape library. Finally the problem of the overall model structure and organization is addressed

Standard quantification and measurement of damages through features characterization of surface imperfections on 3D models: an application on Architectural Heritages

Abstract Reverse Engineering techniques lead to easily obtain, even in case of wide and complex objects, high-resolution 3D models, suitably adoptable in the field of surface analysis and characterization. This research aims to propose innovative quantification and measuring approaches to diagnose and monitor damages affecting artefacts of different nature, from manufacturing to architectural heritage, performing non-destructive analyses with advanced surface metrology instruments and the potential integrations of the existing sectorial standards. General condition assessment is proposed to recognize and classify characterized pathologies by meaningful features in the form of surface imperfections, through the analysis of acquired point clouds. The method is applied to decay phenomena of an architectural artefact

Modified Canny Detector-based Active Contour for Segmentation

In the present work, an integrated modified canny detector and an active contour were proposed for automated medical image segmentation. Since the traditional canny detector (TCD) detects only the edge’s pixels, which are insufficient for labelling the image, a shape feature was extracted to select the initial region of interest ‘IROI’ as an initial mask for the active contour without edge (ACWE), using a proposed modified canny detector (MCD). This procedure overcomes the drawback of the manual initialization of the mask location and shape in the traditional ACWE, which is sensitive to the shape of region of region of interest (ROI). The proposed method solves this problem by selecting the initial location and shape of the IROI using the MCD. Also, a post-processing stage was applied for more cleaning and smoothing the ROI. A practical computational time is achieved as the proposed system requires less than 5 minutes, which is significantly less than the required time using the traditional ACWE. The results proved the ability of the proposed method for medical image segmentation with average dice 87.54%

Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modified Canny Detector-based Active Contour for Segmentation

In the present work, an integrated modified canny detector and an active contour were proposed for automated medical image segmentation. Since the traditional canny detector (TCD) detects only the edge’s pixels, which are insufficient for labelling the image, a shape feature was extracted to select the initial region of interest ‘IROI’ as an initial mask for the active contour without edge (ACWE), using a proposed modified canny detector (MCD). This procedure overcomes the drawback of the manual initialization of the mask location and shape in the traditional ACWE, which is sensitive to the shape of region of region of interest (ROI). The proposed method solves this problem by selecting the initial location and shape of the IROI using the MCD. Also, a post-processing stage was applied for more cleaning and smoothing the ROI. A practical computational time is achieved as the proposed system requires less than 5 minutes, which is significantly less than the required time using the traditional ACWE. The results proved the ability of the proposed method for medical image segmentation with average dice 87.54%