A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models

This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features

Detection of incomplete enclosures of rectangular shape in remotely sensed images

We develop an approach for detection of ruins of livestock enclosures in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a new rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfect enclosures, but also for broken ones with distorted angles, fragmented walls, or even a completely missing wall. However, it has zero value for spurious structures with less than three sides of a perceivable rectangle. Performance analysis using large imagery of an alpine environment is provided. We show how the detection performance can be improved by learning from only a few representative examples and a large number of negatives.Computer SciencesEuropean Prehistor

Flamingo detection using Marked Point Processes for estimating the size of populations

In this paper, we present a new technique to automatically detect and count breeding Greater flamingos (Phoenicopterus roseus) on aerial photographs of their colonies. We consider a stochastic approach based on marked point processes also called object processes. Here, the objects represent flamingos which are defined as ellipses. The Gibbs density associated with the marked point process of ellipses is defined w.r.t the Poisson measure. Thus, the issue is reduced to an energy minimization, where the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted in the image. The prior energy is defined as a sum of local energies for each object. For a given object o, we consider the set S(o) of objects in the current configuration which overlap o. An overlapping coefficient between two objects is defined by the intersection area normalised by the minimum size between the two objects. The local energy, associated to o, is then proportional to the maximum overlapping coefficient between o and any element of S(o). The data term is also defined by a sum local energies over each object in the configuration. The local energy is obtained from the computation of a radiometric distance between pixels in the ellipse, modeling the flamingo, and pixels in the neighborhood of this ellipse....Nous présentons dans cet article une nouvelle technique de détection de flamants roses sur des images aériennes. Nous considérons une approche stochastique fondée sur les processus ponctuels marqués, aussi appelés processus objets. Ici, les objets représentent les flamants, qui sont modélisés par des ellipses. La densité associée au processus ponctuel marqué d'ellipses est définie par rapport à une mesure de Poisson. Dans un cadre gibbsien, le problème se réduit à la minimisation d'une énergie, qui est constituée d'un terme de régularisation (densité a priori), qui introduit des contraintes sur les objets et leurs interactions; et un terme d'attache aux données, qui permet de localiser sur l'image les flamants à extraire. Nous échantillonnons le processus pour extraire la configuration d'objets minimisant l'énergie grâce à une nouvelle dynamique de Naissances et Morts multiples, amenant finalement à une estimation du nombre total de flamants présents sur l'image. Cette approche donne des comptes avec une bonne précision comparée aux comptes manuels. De plus, elle ne nécessite aucun traitement préalable ou intervention manuelle, ce qui réduit considérablement le temps d'obtention des comptes

Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires

National audiencePreviously, marked point process models have been successfully applied to solve problems of feature network extraction from aerial and satellite high resolution photographs. The main advantage of these models is that they take into account object geometry. In particular, objects in the image are represented by a set of geometric shapes which are governed by two types of energy: a data energy term that links objects to the processed image, and a regularizing energy term which introduces some prior knowledge about the observed scene. Furthermore, some parameters reflecting the importance of these energies are involved in the definition of this model. Thus, to ensure automatic object extraction, an estimation method of these parameters, based on the Stochastic Expectation-Maximization algorithm was studied and showed promising results on a simple example of a marked point process where the considered objects are disks. We propose to extend this study to extract more general geometric shapes, such as ellipses and rectangles. Hence, we deal with several applications, namely flamingo extraction, tree crown extraction, detection of boats in a seaport, building footprint detection and refugee tent detection. The main originality of this work consists in developing new energy components that allow to model boat alignment and building interactions within an unsupervised framework.Les modèles de processus ponctuels marqués ont été précédemment appliqués avec succès pour résoudre des problèmes d'extraction de réseaux de formes à partir d'images aériennes et satellitaires haute résolution. L'avantage de ces modèles est qu'ils prennent en compte la géométrie des objets à extraire. En particulier, les objets de l'image sont représentés par un ensemble de formes géométriques dont la disposition est gouvernée par deux types d'énergies : une énergie d'attache aux données qui lie les objets à l'image traitée, et une énergie de régularisation qui permet d'introduire des connaissances a priori sur le réseau d'objets. Par ailleurs, des paramètres qui traduisent l'influence de ces énergies sont introduits dans la définition de ce modèle. Afin d'assurer une extraction automatique des objets, une méthode d'estimation des paramètres en question, fondée sur une version stochastique de l'algorithme Espérance-Maximisation, a été étudiée et a conduit à des résultats prometteurs sur un exemple simple de processus ponctuels d'objets circulaires. Nous proposons de prolonger cette étude afin d'extraire des formes géométriques plus générales, i.e. des formes elliptiques et rectangulaires. Nous abordons ainsi plusieurs applications ; à savoir l'extraction de flamants roses, de houppiers d'arbres, de bateaux dans un port marîtime, de la trace au sol du bâti ainsi que la détection de tentes de réfugiés. L'originalité majeure de ce travail réside dans la définition des composantes de l'énergie qui permettent de modéliser l'alignement des navires à quai, ainsi que les interactions qui existent entre les bâtis tout en restant dans un cadre automatique

1 A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.

Abstract—This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a priori knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types. Index Terms—Image processing, Poisson point process, stochastic geometry, dense urban area, Digital Elevation Models, land register, building detection, MCMC, RJMCMC, simulated annealing

Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

Characterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets