Automatic detection of passable roads after floods in remote sensed and social media data

This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches

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The studies of the factors of formation SO2-binding complex of white and red grape cultivars from different soil and climatic regions of the Crimea were carried out. The formation of the SO2-binding complex of grapes is associated with a combination of endogenous and exogenous factors. It was found that the most significant factors (α&0.05) in the formation of SO2-binding complex are the cultivar, soil-climatic region of growth, harvest year, and concentration of sugar. Revealed that in the case of white grape cultivars – the cultivar (α = 0.0002) and the soil-climatic region of growth (α = 0.0003) had a significant effect on the accumulation of aldehydes. In red grape cultivars the accumulation of SO2-binding components (ketoacids and aldehydes) was determined by the grape cultivar (α&0.045); α-ketoglutaric acid – soil-climatic region of growth (α = 0.014). The relationship between the mass concentrations of aldehydes and sugars in red grape cultivars has been established

Study of the influence of the process of freezing milk on the safety of its properties of cheese suitability

The article presents the results of studies of the effect of freezing on the change in the physicochemical, microbiological and technological properties of goat milk and the preservation of its qualities of cheese suitability. A statistically significant dependence of the composition of milk on the duration of storage in a frozen state was revealed. There was no significant effect of freezing and defrosting modes on the quality indicators of milk. It has been established that changes in the technological properties of frozen goat milk after defrosting, such as the duration of coagulation and the ability to syneresis, are insignificant in comparison with defrosted cow's milk

Biologization of grape growing technologies to obtain safe and high-quality products

Viticulture is impossible without the use of chemical plant protection agents. Their use follows to ecological destabilization of ampelocenoses and constitutes real danger to human health. Last years there is a tendency to biologize the protection technologies that the relevance of studies of the influence of biopreparations on the quality of graps and wines. The article presents the results studies of fungicide action of the preparations Biocomposite-correct and Biocomposite-protect. Their high anti-mycotic activity (97-100%) against Trichothecium roseum and Botrytis cinerea has been established. The high efficiency in the control of grey rot on grapes cultivar Merlot showed a two-fold application of the Biocomposite-protect– the damage to grapes decreased by 2 times in comparison with the control and amounted to 0.3%. Using of biofungicides did not affect the of mush/must fermentation. The test wines were distinguished by a higher (by 13-28%) concentration of phenolic substances. The use of Biocomposite-protect followed to an increase of anthocyanins concentration (by 1.5 times) and the total dry extract (by 12%) in wines; decrease – of titratable acids; Biocomposite-correct – decrease the total dry extract (by 10%). Using of biological preparations for grape protection did not influence the organoleptic quality of wines

Deep learning and hand-crafted feature based approaches for polyp detection in medical videos

Video analysis including classification, segmentation or tagging is one of the most challenging but also interesting topics multimedia research currently try to tackle. This is often related to videos from surveillance cameras or social media. In the last years, also medical institutions produce more and more video and image content. Some areas of medical image analysis, like radiology or brain scans, are well covered, but there is a much broader potential of medical multimedia content analysis. For example, in colonoscopy, 20% of polyps are missed or incompletely removed on average. Thus, automatic detection to support medical experts can be useful. In this paper, we present and evaluate several machine learning-based approaches for real-time polyp detection for live colonoscopy. We propose pixel-wise localization and frame-wise detection methods which include both handcrafted and deep learning based approaches. The experimental results demonstrate the capability of analyzing multimedia content in real clinical settings, the possible improvements in the work flow and the potential improved detection rates for medical experts

Medico multimedia task at MediaEval 2018

The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics

A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years