A higher-order active contour model of a `gas of circles' and its application to tree crown extraction

Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, e.g. medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques

A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences

International audienceIn this paper, we propose a Multilayer Markovian model for change detection in registered aerial image pairs with large time differences. A Three Layer Markov Random Field takes into account information from two different sets of features namely the Modified HOG (Histogram of Oriented Gradients) difference and the Gray-Level (GL) Difference. The third layer is the resultant combination of the two layers. Thus we integrate both the texture level as well as the pixel level information to generate the final result. The proposed model uses pairwise interaction retaining the sub-modularity condition for energy. Hence a global energy optimization can be achieved using a standard min-cut/ max flow algorithm ensuring homogeneity in the connected regions

Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model

This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observable image. Our approach consists of extending a recent iterative method of estimation, called Iterative Conditional Estimation (ICE) to a hierarchical markovian model. The idea resembles the Estimation-Max- imization (EM) algorithm as we recursively look at the Maximum a Posteriori (MAP) estimate of the label field given the estimated parameters then we look at the Maximum Likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. We propose unsupervised image classification algorithms using a monogrid or a hierarchical model. The only parameter supposed to be knowm is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images

A hierarchical Markov random field model and multi-temperature annealing for parallel image classification

In this report, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. First, we present a classical multiscale model which consists of a label pyramid and a whole observation field. The potential functions of coarser grids are derived by simple computations. The optimization problem is first solved at the higher scale by a parallel relaxation algorithm, then the next lower scale is initialized by a projection of the result. Second, we propose a hierarchical Markov Random Field model based on this classical model. We introduce new interactions between neighbor levels in the pyramid. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price. This model results in a relaxation algorithm with a new annealing scheme: The Multi-Temperature Annealing (MTA) scheme, which consists of associating higher temperatures to higher levels, in order to be less sensitive to local minima at coarser grids. The convergence to the global optimum is proved by a generalisation of the annealing theorem of Geman and Geman

Image classification using Markov random fields with two new relaxation methods : deterministic pseudo annealing and modified metropolis dynamics

In this paper, we present two relaxation techniques : deterministic pseudo-annealing (DPA) and modified metropolis dynamics (MMD) in order to do image classification using a Markov random field modelization. For the first algorithm (DPA), the a posteriori probability of a tentative labeling is generalized to continuous labeling. The merit function thus defined has the same maxima under constraints yielding probability vectors. Changing these constraints convexify the merit function. The algorithm solve this unambigous maximization problem and then tracks down the solution while the original constraints are restored yielding a good even if suboptimal solution to the original labeling assignment problem. As for the second method (MMD) it is a modified version of the metropolis algorithm : at each iteration the new state is chosen randomly but the decision to accept it is purely deterministic. This is of course also a suboptimal technique which gives faster results than stochastic relaxation. These two methods have been implemented on a connection machine CM2 and simulation results are shown with a synthetic noisy image and a SPOT image. These results are compared to those obtained with the metropolis algorithm, the Gibbs sampler and ICM (Iterated Conditional Mode)

Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images

In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of ground truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches

What Does Ecological Farming Mean for Farm Labour?

Summary: Ecological farming, such as organic and low‐input farming, is gaining popularity in the public discourse. One question is how this type of farming may impact farm labour from a socio‐economic point of view. The article first discusses how low‐input farming practices (i.e. with lower reliance on inputs derived from fossil fuels) may affect the economic returns to labour, measured as the farm’s revenue per hour of labour input, on data from the Farm Accountancy Data Network (FADN) in 2004‐‐2015 for four European countries. Returns to labour appear to be highest at the two extremes – very low‐input farms and highly intensive farms. Farms in the low‐input end of the spectrum are in the minority, while the overwhelming majority of farms are intensive and have internal economic incentives to intensify further. The article also analyses how working conditions differ between organic and conventional dairy farms in two European countries based on interviews with farmers in 2019. Results show that all dimensions of working conditions are affected by being an organic farm or not, but this is not the only factor. There are many influences on working conditions, such as the production context and workforce composition

What Does Ecological Farming Mean for Farm Labour?

Summary: Ecological farming, such as organic and low‐input farming, is gaining popularity in the public discourse. One question is how this type of farming may impact farm labour from a socio‐economic point of view. The article first discusses how low‐input farming practices (i.e. with lower reliance on inputs derived from fossil fuels) may affect the economic returns to labour, measured as the farm’s revenue per hour of labour input, on data from the Farm Accountancy Data Network (FADN) in 2004‐‐2015 for four European countries. Returns to labour appear to be highest at the two extremes – very low‐input farms and highly intensive farms. Farms in the low‐input end of the spectrum are in the minority, while the overwhelming majority of farms are intensive and have internal economic incentives to intensify further. The article also analyses how working conditions differ between organic and conventional dairy farms in two European countries based on interviews with farmers in 2019. Results show that all dimensions of working conditions are affected by being an organic farm or not, but this is not the only factor. There are many influences on working conditions, such as the production context and workforce composition