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Many problems of image processing lead to the minimization of an energy, which is a function of one or several given images, with respect to a binary or multi-label image. When this energy is made of unary data terms and of pairwise regularization terms, and when the pairwise regularization term is a metric, the multi-label energy can be minimized quite rapidly, using the so-called α-expansion algorithm. α-expansion consists in decomposing the multi-label optimization into a series of binary sub-problems called move. Depending on the chosen decomposition, a different condition on the regularization term applies. The metric condition for α-expansion move is rather restrictive. In many cases, the statistical model of the problem leads to an energy which is not a metric. Based on the enlightening article [1], we derive another condition for β-jump move. Finally, we propose an alternated scheme which can be used even if the energy fulfills neither the α-expansion nor β-jump condition. The proposed scheme applies to a much larger class of regularization functions, compared to α-expansion. This opens many possibilities of improvements on diverse image processing problems. We illustrate the advantages of the proposed optimization scheme on the image noise reduction problem.

Land-Cover databases (LC-DB) are very useful for environmental purposes, but need to be semantically detailed to provide robust and instructive spatial indicators. Moreover, remote sensed data allow to cover large areas with high temporal resolution. Such multi-temporal data are very useful input to discriminate LC classes. Nevertheless, automatic fusion method need to be developed to provide high quality LC-DB. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those fusion methods allow to take advantage of multitemporal data. Those methods are compared, and assessed thanks to a very high resolution LC-DB.

Analysis (MCDA) is a popular decision tool as it permits to summarize the benefits and the risks of a drug in a single utility score, accounting for the preferences of the decision-makers. However, the utility score is often derived using a linear model which might lead to counter-intuitive conclusions, for example drugs with no benefit or extreme risk could be recommended. Moreover, it assumes that the relative importance of benefits against risks is constant for all levels of benefit or risk, which might not hold for all drugs. Further methodological developments are required to overcome these issues. METHODS: . We propose Scale Loss Score (SLoS) as a new tool for the BR assessment, which offers the same advantages as MCDA but has, in addition, desirable properties permitting to avoid recommendations of non-effective or extremely unsafe treatments, and to tolerate larger increases in risk for a given increase in benefit when the amount of benefit is small than when it is high. RESULTS: . We present an application to a real case study on telithromycin in Community Acquired Pneumonia and Acute Bacterial Sinusitis, and we investigated the patterns of behavior of SLoS, as compared to MCDA, in a comprehensive simulation study. CONCLUSIONS: . Scale Loss Score (SLoS) is a novel, simple and valuable tool for BR assessment.OBJECTIVES: Survival network meta-analyses (NMAs) are a key component of many health technology assessment submissions in oncology when head-tohead evidence is not available for all comparators. Traditional NMA methods of survival outcomes using hazard ratios (HRs) assume proportional effects of treatment over time. More recent fractional polynomial (FP) techniques do not rely on this proportional hazards (PH) assumption. Currently, no formal Decision Support Unit guidance exists on performing survival NMA. The objectives of this work were to examine PH and NMA methods in National Institute for Health and Care Excellence (NICE) submissions and their associated acceptability, and to explore the comparability of results from FP and HR NMAs in the presence of non-PH. METHODS: We comprehensively identified and extracted information relating to NICE appraisal consultations for oncology products since June 2016. Information extracted included testing of PH, NMA methods, and Evidence Review Group (ERG) and committee comments. A targeted literature review of studies comparing FP and HR NMAs also was conducted. RESULTS: Since June 2016, 60% (47/78) of oncology submissions included survival NMAs or indirect comparisons. Of those, 47% utilised only HR-based NMAs, one (2%) used only an FP-based NMA, 13% used FP and HR, 38% used other approaches. The remaining submissions included limited evidence on comparable populations or availability of head-to-head evidence for all comparators. Tests of PH were reported in 81%. Of those reporting non-PH, approximately half only performed HR based approaches. Where there was evidence of non-PH, and the initial submission reported only HR NMAs, the ERG requested FP analysis. W...

Stereoscopic reconstruction is important to automatic vision systems. As an intermediate step, estimating this reconstruction is not enough for good performance of the whole system, and its uncertainty must be characterized. Several methods propose uncertainty indexes based on specific data features, thus incomplete, while others are based on learning. We propose a simple index, named ambiguity index, taking into account both data and regularization, and derived directly from the optimization process. Exploiting properties of dynamic programming, this index is related to the posterior variance of the solution when the Semi-Global Matching (SGM) algorithm is used for stereo reconstruction. To illustrate its interest, improvements in refining stereo reconstruction are shown on the KITTI datasets when the index is used.