scholarly journals Automatic detection of Aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency

2022 ◽  
Vol 93 ◽  
pp. 101754
Wesley L. Passos ◽  
Gabriel M. Araujo ◽  
Amaro A. de Lima ◽  
Sergio L. Netto ◽  
Eduardo A.B. da Silva
Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

2020 ◽  
Vol 34 (07) ◽  
pp. 10713-10720
Mingyu Ding ◽  
Zhe Wang ◽  
Bolei Zhou ◽  
Jianping Shi ◽  
Zhiwu Lu ◽  

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.

2021 ◽  
Wei-Yu Lee ◽  
Martin Dimitrievski ◽  
Ljubomir Jovanov ◽  
Wilfried Philips

2021 ◽  
Harry West ◽  
Nevil Quinn ◽  
Michael Horswell

<p>The North Atlantic Oscillation (NAO) is often cited as the primary atmospheric-oceanic circulation or teleconnection influencing regional climate in Great Britain. As our ability to predict the NAO several months in advance improves, it is important that we also continue to develop our spatial and temporal understanding of the rainfall signatures which the circulation produces.</p><p>We present a novel application of spatial statistics to explore variability in monthly NAO rainfall signatures using a 5km gridded monthly Standardised Precipitation Index (SPI) dataset. We first use the Getis-Ord Gi* statistic to map spatially significant hot and cold spots (clusters of high/wet and low/dry SPI values) in average monthly rainfall signatures under NAO Positive and Negative conditions over the period 1900-2015. We then look across the record and explore the temporal variability in these signatures, in other words how often a location is in a significant spatial hot/cold spot (high/low SPI) at a monthly scale under NAO Positive/Negative conditions.</p><p>The two phases of the NAO are typically more distinctive in the winter months, with stronger and more variable NAO Index values. The average monthly SPI analysis reveals a north-west/south-east ‘spatial divide’ in rainfall response. NAO Positive phases result in a southerly North Atlantic Jet Stream bringing warm and wet conditions from the tropics, increasing rainfall particularly in the north-western regions. However, under NAO Negative phases which result in a northerly Jet Stream, much drier conditions in the north-west prevail. Meanwhile in the south-eastern regions under both NAO phases a weaker and opposite wet/dry signal is observed. This north-west/south-east ‘spatial divide’ is marked by the location of spatially extensive hot/cold spots. The Getis-Ord Gi* result identifies that the spatial pattern we detect in average winter rainfall is statistically significant. Looking across the record, this NW/SE opposing response appears to have a relatively high degree of spatio-temporal consistency. This suggests that there is a high probability that NAO Positive and Negative phases will result in this NW/SE statistically significant spatial pattern.</p><p>Even though the phases of the NAO in the summer months are less distinctive they still produce rainfall responses which are evident in the monthly average SPI. However, the spatiality in wet/dry conditions is more homogenous across the country. In other words the ‘spatial divide’ observed in winter is diluted in summer. As a result, the occurrence of significant hot/cold spots is more variable in space and time.</p><p>Our analysis demonstrates a novel application of the Getis-Ord Gi* statistic which allows for spatially significant patterns in the monthly SPI data to be mapped for each NAO phase. In winter months particularly, this analysis reveals statistically significant opposing rainfall responses, which appear to have long-term spatio-temporal consistency. This is important because as winter NAO forecasting skill improves, the findings of our research enable a more spatially reliable estimate of the likely impacts of NAO-influenced rainfall distribution.</p>

Xiaobin Zhu ◽  
Zhuangzi Li ◽  
Xiao-Yu Zhang ◽  
Changsheng Li ◽  
Yaqi Liu ◽  

Video super-resolution is a challenging task, which has attracted great attention in research and industry communities. In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution. The RISTN can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames. Compared with existing recurrent convolutional network based approaches, RISTN is much deeper but more efficient. It consists of three major components: In the spatial component, a lightweight residual invertible block is designed to reduce information loss during feature transformation and provide robust feature representations. In the temporal component, a novel recurrent convolutional model with residual dense connections is proposed to construct deeper network and avoid feature degradation. In the reconstruction component, a new fusion method based on the sparse strategy is proposed to integrate the spatial and temporal features. Experiments on public benchmark datasets demonstrate that RISTN outperforms the state-ofthe-art methods.

2020 ◽  
Vol 10 (1) ◽  
Teja Curk ◽  
Ivan Pokrovsky ◽  
Nicolas Lecomte ◽  
Tomas Aarvak ◽  
David F. Brinker ◽  

Abstract Migratory species display a range of migration patterns between irruptive (facultative) to regular (obligate), as a response to different predictability of resources. In the Arctic, snow directly influences resource availability. The causes and consequences of different migration patterns of migratory species as a response to the snow conditions remains however unexplored. Birds migrating to the Arctic are expected to follow the spring snowmelt to optimise their arrival time and select for snow-free areas to maximise prey encounter en-route. Based on large-scale movement data, we compared the migration patterns of three top predator species of the tundra in relation to the spatio-temporal dynamics of snow cover. The snowy owl, an irruptive migrant, the rough-legged buzzard, with an intermediary migration pattern, and the peregrine falcon as a regular migrant, all followed, as expected, the spring snowmelt during their migrations. However, the owl stayed ahead, the buzzard stayed on, and the falcon stayed behind the spatio-temporal peak in snowmelt. Although none of the species avoided snow-covered areas, they presumably used snow presence as a cue to time their arrival at their breeding grounds. We show the importance of environmental cues for species with different migration patterns.

Yang Yu ◽  
Yasushi Makihara ◽  
Yasushi Yagi

AbstractWe address a method of pedestrian segmentation in a video in a spatio-temporally consistent way. For this purpose, given a bounding box sequence of each pedestrian obtained by a conventional pedestrian detector and tracker, we construct a spatio-temporal graph on a video and segment each pedestrian on the basis of a well-established graph-cut segmentation framework. More specifically, we consider three terms as an energy function for the graph-cut segmentation: (1) a data term, (2) a spatial pairwise term, and (3) a temporal pairwise term. To maintain better temporal consistency of segmentation even under relatively large motions, we introduce a transportation minimization framework that provides a temporal correspondence. Moreover, we introduce the edge-sticky superpixel to maintain the spatial consistency of object boundaries. In experiments, we demonstrate that the proposed method improves segmentation accuracy indices, such as the average and weighted intersection of union on TUD datasets and the PETS2009 dataset at both the instance level and semantic level.

2019 ◽  
Vol 66 (2) ◽  
pp. 155-163 ◽  
Alessandro Tedeschi ◽  
Michele Sorrenti ◽  
Michele Bottazzo ◽  
Mario Spagnesi ◽  
Ibon Telletxea ◽  

Abstract Diverse spatio-temporal aspects of avian migration rely on relatively rigid endogenous programs. However, flexibility in migratory behavior may allow effective coping with unpredictable variation in ecological conditions that can occur during migration. We aimed at characterizing inter- and intraindividual variation of migratory behavior in a forest-dwelling wader species, the Eurasian woodcock Scolopax rusticola, focusing on spatio-temporal consistency across repeated migration episodes. By satellite-tracking birds from their wintering sites along the Italian peninsula to their breeding areas, we disclosed a remarkable variability in migration distances, with some birds flying more than 6,000 km to Central Asian breeding grounds (up to 101°E). Prebreeding migration was faster and of shorter duration than postbreeding migration. Birds moving over longer distances migrated faster during prebreeding migration, and those breeding at northernmost latitudes left their wintering areas earlier. Moreover, birds making longer migrations departed earlier from their breeding sites. Breeding site fidelity was very high, whereas fidelity to wintering areas increased with age. Migration routes were significantly consistent, both among repeated migration episodes and between pre- and postbreeding migration. Prebreeding migration departure date was not significantly repeatable, whereas arrival date to the breeding areas was highly repeatable. Hence, interindividual variation in migratory behavior of woodcocks was mostly explained by the location of the breeding areas, and spatial consistency was relatively large through the entire annual cycle. Flexibility in prebreeding migration departure date may suggest that environmental effects have a larger influence on temporal than on spatial aspects of migratory behavior.

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