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In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of abandoned object detection in a cluttered environment using a camera moving along a straight track. The developed system compares captured images to a previously recorded reference video, thus requiring proper temporal alignment and geometric registration between the two signals. A real-time constraint is imposed onto the system to allow an effective surveillance capability in practical situations. In this paper, we propose to deal with the simultaneous detection of objects of different sizes using a multiresolution approach together with normalized cross-correlation and a voting step. In order to develop and properly assess the proposed method we designed a database recorded in a real surveillance scenario, consisting of an industrial plant containing a large number of pipes and rotating machines. Also, we have devised a systematic parameter tuning routine that allows the system to be adapted to different scenarios. We have validated it using the designed database. The obtained results are quite effective, achieving real-time, robust abandoned object detection in an industrial plant scenario. [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect anomalies in videos acquired by a camera mounted on a moving inspection robot. The proposed method is based on a spatio-temporal composition (STC) method, where a dense sampling is used to break the video into small 3D volumes that are used to calculate the probability of the spatio-temporal arrangements. This class of methods has been successfully used for surveillance videos obtained by static cameras. However, when applied to videos recorded by cameras on moving platforms, the STC gives a large number of false detections. In this work, we propose improvements to the present STC method that will alleviate this problem in two ways. First, a two-stage dictionary learning process is performed to allow a more reliable anomaly detection. Second, improved spatio-temporal features are employed. These modified features are extracted after an enhanced temporal filtering that performs a temporal regularization of the video sequence. The proposed approach gives very good results in the identification of anomalies without the need of background subtraction, motion estimation or tracking. The results are shown to be comparable or even superior to those of other state-of-the-art methods using bag-of-video words or other moving-camera surveillance methods. The system is accurate even with no prior knowledge of the type of event to be observed, being robust to cluttered environments, as illustrated by several practical examples. These results are obtained without compromising the performance of the algorithm in the static cameras case. [ABSTRACT FROM AUTHOR]

This study presents the application of a safe, cost effective, environmental friendly, and efficient technology for the removal of trivalent chromium ions from aqueous solutions, based on the valorisation of a renewable resource, Laminaria digitata seaweed. Insights into trivalent chromium speciation in solution and interaction with the active sites present in the surface of the brown algae were studied. Carboxyl and hydroxyl groups were identified as the major binding sites present in the surface of the biosorbent, in concentrations ( Qmax) of 2.06 ± 0.01 and 1.4 ± 0.7 mmol g −1, and with proton binding parameters ( pK) of 3.28 ± 0.01 and 11 ± 1, respectively. Trivalent chromium uptake at equilibrium conditions was well described at different acidic pH conditions and chromium concentrations, using a model which incorporates trivalent chromium hydrolysis reactions in the aqueous phase and its chemical interactions with the available active sites (carboxyl groups) present in the surface of biosorbent. The distribution profile of trivalent chromium species present in the solution as well as at the binding sites indicated that Cr 3+ and CrOH 2+ exhibit different affinities for the carboxyl groups present in the surface of the biomass according to the pH. A mass transfer kinetics model was applied to describe the kinetics at batch system, being possible to obtain the distribution of CrOH 2+ and Cr 3+ species in solution and at the binding sites. [ABSTRACT FROM AUTHOR]