Attribute Controlled Reconstruction and Adaptive Mathematical Morphology

ISBN : 978-3-642-38293-2International audienceIn this paper we present a reconstruction method controlled by the evolution of attributes. The process begins from a marker, propagated over increasing quasi-flat zones. The evolution of several increasing and non-increasing attributes is studied in order to select the appropriate region. Additionally, the combination of attributes can be used in a straightforward way. To demonstrate the performance of our method, three applications are presented. Firstly, our method successfully segments connected objects in range images. Secondly, input-adaptive structuring elements (SE) are defined computing the controlled propagation for each pixel on a pilot image. Finally, input-adaptive SE are used to assess shape features on the image. Our approach is multi-scale and auto-dual. Compared with other methods, it is based on a given attribute but does not require a size parameter in order to determine appropriate regions. It is useful to extract objects of a given shape. Additionally, our reconstruction is a connected operator since quasi-flat zones do not create new contours on the image

Urban accessibility diagnosis from mobile laser scanning data

International audienceIn this paper we present an approach for automatic analysis of urban acessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning. Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards. Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases

Inner-Cheeger Opening and Applications

International audienceThe aim of this paper is to study an optimal opening in the sense of minimize the relationship perimeter over area. We analyze theoretical properties of this opening by means of classical results from variational calculus. Firstly, we explore the optimal radius as attribute in morphological attribute filtering for grey scale images. Secondly, an application of this optimal opening that yields a decomposition into meaningful parts in the case of binary image is explored. We provide different examples of 2D, 3D images and mesh-points datasets

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture

Discriminating value crops from weeds is an important task in precision agriculture. In this paper, we propose a novel image processing pipeline based on attribute morphology for both the segmentation and classification tasks. The commonly used approaches for vegetation segmentation often rely on thresholding techniques which reach their decisions globally. By contrast, the proposed method works with connected components obtained by image threshold decomposition, which are naturally nested in a hierarchical structure called the max-tree, and various attributes calculated from these regions. Image segmentation is performed by attribute filtering, preserving or discarding the regions based on their attribute value and allowing for the decision to be reached locally. This segmentation method naturally selects a collection of foreground regions rather than pixels, and the same data structure used for segmentation can be further reused to provide the features for classification, which is realised in our experiments by a support vector machine (SVM). We apply our methods to normalised difference vegetation index (NDVI) images, and demonstrate the performance of the pipeline on a dataset collected by the authors in an onion field, as well as a publicly available dataset for sugar beets. The results show that the proposed segmentation approach can segment the fine details of plant regions locally, in contrast to the state-of-the-art thresholding methods, while providing discriminative features which enable efficient and competitive classification rates for crop/weed discrimination

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information