With a continuous increase in the number of Earth Observation satellites, leading to the development of satellite image time series (SITS), the number of algorithms for land cover analysis and monitoring has greatly expanded. This paper offers a new perspective in dynamic classification for SITS. Four similarity measures (correlation coefficient, Kullback-Leibler divergence, conditional information, and normalized compression distance) based on consecutive image pairs from the data are employed. These measures employ linear dependences, statistical measures, and spatial relationships to compute radiometric, spectral, and texture changes that offer a description for the multitemporal behavior of the SITS. During this process, the original SITS is converted to a change map time series (CMTS), which removes the static information from the data set. The CMTS is analyzed using a latent Dirichlet allocation (LDA) model capable of discovering classes with semantic meaning based on the latent information hidden in the scene. This statistical method was originally used for text classification, thus requiring a word, document, corpus analogy with the elements inside the image. The experimental results were computed using 11 Landsat images over the city of Bucharest and surrounding areas. The LDA model enables us to discover a wide range of scene evolution classes based on the various dynamic behaviors of the land cover. The results are compared with the Corinne Land Cover map. However, this is not a validation method but one that adds static knowledge about the general usage of the analyzed area. In order to help the interpretation of the results, we use several studies on forms of relief, weather forecast, and very high resolution images that can explain the wide range of structures responsible for influencing the dynamic inside the resolution cell.

Percutaneous declotting of a thrombosed fistula or graft is standard of care and is a safe procedure. Subclinical pulmonary embolism (PE) during this procedure occurs commonly, but symptomatic PE is extremely rare. The authors report a case of declotting-associated massive PE with cardiopulmonary arrest and successful resuscitation. The patient developed a new right-axis deviation and right-bundle branch block. Diagnosis of PE was confirmed with a computed tomography (CT) angiogram, and the patient received tissue plasminogen activator (tPA) and heparin. She required norepinephrine and dobutamine temporarily and was subsequently extubated successfully. Massive PE is a very rare complication of this procedure. Given the grave outcome, the clinical signs and symptoms should be recognized immediately and treatment instituted early.

Pedestrian detection represents one of the most important components of engineering devices that use automated vision to help decision systems take quick and accurate actions. Such systems are defined and customized to be useful for different needs, such as monitoring and aided surveillance, or increasing safety features in automotive industry. Given the large spectrum of applications that use pedestrian detection, demand has increased in recent years for the development of feasible solutions which can be integrated in devices such as smartphones or action cameras.This paper focuses on finding probabilistic features that highlight the human body characteristics regardless of contextual information in images. Adjacent pixels are often spatially correlated, which means that they are likely to have similar values. We view the image as a collection of random variables indexed by certain locations, called sites. The state of a site ξ is conditionally independent of all variables in the random field, except the neighbouring systemwhere ∆ is a positive integer and d 2 (ξ , η) is the squared Euclidean distance between ξ and η. The neighbouring system strictly depends on a collection of cliques C = ∑ ω(δ ) k=1 C k , where ω(∆) is the number of cliques for each local specification.Energy function: An unpublished manuscript [2] describes how to interpret the local property of a Markov random field in terms of energy and potential, claiming that the probability at a site ξ is given by:where V C is the potential function, and ϕ ∈ F. To get the probability that at a site ξ the state is γ, we need to define a potential function V C (γ) in the neighbouring system, here denoted by a collection of cliques C. To be able to do this, we refer to the auto-binomial model that was introduced by Besag [1] to describe types of spatial processes, examining some stochastic models that occur in the texture of various physical materials. Potential function: The potential at a certain state is given by:

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