Automatic segmentation and analysis of ancient mosaic images can help archaeologists and experts build digital collections and automatically compare mosaics by means of image database indexing and content-based retrieval tools. However, ancient mosaics are characterized by low contrast colors and irregular tessella shape, orientation, and positioning, making automatic segmentation difficult. We propose a tessella-oriented strategy whose first step consists of isolating tessellas from their cemented network by computing the watershed transformation of a criterion image generated to exhibit the cement network as watershed crests. Then a simple k-means algorithm is used to classify tessellas and segment mosaic images with more accuracy than with a pixel-oriented strategy. Additionally, we propose a method to automatically obtain the main directional guidelines of mosaics by estimating tessella orientation. This is done by minimizing a contextual energy computed from gray-level means of neighboring tessellas and the orientation of their borders. Several examples of cartographies show the effectiveness of the method.
This work deals with unsupervised change detection in bi-date Synthetic Aperture Radar (SAR) images. Whatever the indicator of change used to compute the criterion image, e.g. log-ratio or Kullback-Leibler divergence between images, we have observed poor quality change maps for some events when using the Hidden Markov Chain (HMC) model we focus on in this work. The main reason comes from the stationary assumption involved in this model --and in most Markovian models such as Hidden Markov Random Fields--, which can not be justified in most observed scenes: changed areas are not necessarily stationary in the image. Besides the non-stationary Markov models proposed in the literature, the aim of this paper is to describe a pragmatic solution to tackle change detection stationarity by evaluating and comparing a 1D and a 2D window approaches. By moving the window through the criterion image, the process is able to produce a change map which can better exhibit non-stationary changes than the classical HMC applied directly on the whole criterion image. Special care is devoted to the estimation of the number of classes in each window, which can vary from one (no change) to three (positive change, negative change and no change) by using the corrected Akaike Information Criterion suited to small samples. The quality assessment of the proposed approaches is achieved with a pair of RADARSAT images bracketing the Mount Nyiragongo volcano eruption event in January 2002. The available ground truth confirms the effectiveness of the proposed approach compared to a classical HMC-based strategy.
This work deals with unsupervised change detection in bi-date Synthetic Aperture Radar (SAR) images. Whatever
the indicator of change used, e.g. log-ratio or Kullback-Leibler divergence, we have observed poor quality
change maps for some events when using the Hidden Markov Chain (HMC) model we focus on in this work.
The main reason comes from the stationary assumption involved in this model − and in most Markovian models
such as Hidden Markov Random Fields−, which can not be justified in most observed scenes: changed areas
are not necessarily stationary in the image. Besides the few non stationary Markov models proposed in the
literature, the aim of this paper is to describe a pragmatic solution to tackle stationarity by using a sliding
window strategy. In this algorithm, the criterion image is scanned pixel by pixel, and a classical HMC model is
applied only on neighboring pixels. By moving the window through the image, the process is able to produce
a change map which can better exhibit non stationary changes than the classical HMC applied directly on the
whole criterion image. Special care is devoted to the estimation of the number of classes in each window, which
can vary from one (no change) to three (positive change, negative change and no change) by using the corrected
Akaike Information Criterion (AICc) suited to small samples. The quality assessment of the proposed approach
is achieved with speckle-simulated images in which simulated changes is introduced. The windowed strategy is
also evaluated with a pair of RADARSAT images bracketing the Nyiragongo volcano eruption event in January
2002. The available ground truth confirms the effectiveness of the proposed approach compared to a classical
HMC-based strategy.
The efficiency of Markov models in the context of SAR image segmentation mainly relies on their spatial regularity constraint. However, a pixel may have a rather different visual aspect when it is located near a boundary or inside a large set of pixels of the same class. According to the classical hypothesis in Hidden Markov Chain (HMC) models, this fact can not be taken into consideration. This is the very reason of the recent Pairwise Markov Chains (PMC) model which relies on the hypothesis that the pairwise process (X,Y) is
Markovian and stationary, but not necessarily X. The main interest of the PMC model in SAR image segmentation is to not assume that the speckle is spatially uncorrelated. Hence, it is possible to take into
account the difference between two successive pixels that belong to the same region or that overlap a boundary. Both PMC and HMC parameters are learnt from a variant of the Iterative Conditional Estimation method. This allows to apply the Bayesian Maximum Posterior Marginal criterion for the restoration of X in
an unsupervised manner. We will compare the PMC model with respect to the HMC one for the unsupervised segmentation of SAR images, for both Gaussian distributions and Pearson system of distributions.
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