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opencc-by-4.0Jun 2020View details →

Monitoring spatio-temporal snow depth in the Chilean Andes using spaceborne tri-stereo photogrammetry

Data provided for submitted work: Thomas E. Shaw, César Deschamps-Berger, Simon Gascoin, James McPhee Monitoring spatial and temporal differences in Andean snow depth  derived from satellite tri-stereo photogrammetry --------- Contents:     'SDmap_2017_3m.tif' = Filtered DEM difference for September 4th 2017.     'SDmap_2019_3m.tif' = Filtered DEM difference for September 2nd 2019.     'SDmap_gapfilled_2017_3m.tif' = Filtered DEM difference for September 4th 2017, gap-filled by random forest model. Random forest model initialised by 100 runs, using all available snow depths in 2017 and topographic indices as predictors (Shaw et al., 2020).     'SDmap_gapfilled_2019_3m.tif' = As above, but for the filtered DEM difference of September 2nd 2019.      'Slope_3m.tif' = Slope angle (°) derived from the snow-free DEM (6th January 2018).     'TPI_3m.tif' = Topographic position index (TPI - Revuelto et al., 2014) derived from the snow-free DEM (6th January 2018) based upon a 60 m search distance.     'Aspect_3m.tif' = Aspect (°) derived from the snow-free DEM (6th January 2018).     'Exposure_SX_3m.tif' = Exposure parameter (SX) based upon Winstral et al. (2002) derived from the snow-free DEM (6th January 2018) and dominant ERA5 10 m wind direction for 2017 and 2019 winter average (45°).     'SkyViewFraction_3m.tif' = Sky view fraction derived from the snow-free DEM (6th January 2018). --------- NOTE: Products of the Pléiades DEM processing are provided here, though publication of the raw Pléiades DEMs are restricted by the regulations of the CNES agreement for grant PNTS‐2018‐ 4. --------- Cited works: Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., & Vicente-Serrano, S. M. (2014). Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: Intra- and inter-annual persistence. The Cryosphere, 8(5), 1989–2006. https://doi.org/10.5194/tc-8-1989-2014 Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., & McPhee, J. (2020). Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing Water Resources Research. Water Resources Research, 56, 1–23. https://doi.org/10.1029/2019WR024880 Winstral, A., Elder, K., & Davis, R. E. (2002). Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters. Journal of Hydrometeorology, 3(5), 524–538. https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2

opencc-zeroAug 2020View details →

Stereo video files used for 3d tracking horsefly trajectories

Of all hypotheses advanced for why zebras have stripes, avoidance of biting fly attack receives by far the most support, yet the mechanisms by which stripes thwart landings are not yet understood. A logical and popular hypothesis is that stripes interfere with optic flow patterns needed by flying insects to execute controlled landings. This could occur through disrupting the radial symmetry of optic flow via the aperture effect (i.e. generation of false motion cues by straight edges), or through spatiotemporal aliasing (i.e. misregistration of repeated features) of evenly spaced stripes. By recording and reconstructing tabanid fly behaviour around horses wearing differently patterned rugs, we could tease out these hypotheses using realistic target stimuli. We found that flies avoided landing on, flew faster near, and did not approach as close to striped and checked rugs compared to grey. Our observations that flies avoided checked patterns in a similar way to stripes refutes the hypothesis that stripes disrupt optic flow via the aperture effect, which critically demands parallel striped patterns. Our data narrow the menu of fly-equid visual interactions that form the basis for the extraordinary coloration of zebras.

opencc-zeroDec 2015View details →

Data from: A novel stereo-video method to investigate fish-habitat relationships

Habitat complexity is known to influence the structure of fish assemblages. A number of techniques have previously been used to measure complexity, including quantitative in situ methods, which can be time-consuming and labour-intensive, and more rapid semi-quantitative visual scoring methods. This study investigated the utility of a novel method for estimating complexity, whereby habitat height was measured using stereo-photogrammetry from diver-operated stereo-video, traditionally used to sample fish assemblages. This 'stereo-height' method was compared to established in situ and visual scoring techniques and found to produce similar estimates of complexity. To determine how relevant the proposed method is for assessing ecological relationships, it was then used in conjunction with visual scoring of relief and point-intercept samples of benthic composition to model fish–habitat associations in the Pilbara region of Western Australia. Visual scores of relief were marginally stronger predictors of fish assemblage parameters and functional groups than the stereo-height measurements, providing support for the visual scoring approach. The only exception was for corallivorous fishes, which were more strongly correlated with stereo-height measurements. This study has presented a method for assessing habitat complexity using video imagery that is both comparable to traditional in situ techniques and useful for investigating fish–habitat relationships. We suggest that future studies interested in collecting habitat complexity data from new or existing stereo-video samples use both the stereo-height and visual scoring methods presented here. Together these methods enable studies to rapidly and effectively assess fish–habitat relationships across a range of habitats without the need for in situ methods or solely relying on field observers trained in visual scoring techniques.

opencc-by-4.0Nov 2023View details →

Experimental dataset: stereo-DIC experiment on uniaxially loaded, S-Shaped, high density polyethylene test sample

Stereo-DIC experiments were performed on uniaxially loaded, s-shaped, high-density polyethylene test sample. 100 stationary images of unloaded test sample were taken for evaluation of DIC noise floor. Stereo calibration image dataset, involving a calibration target with rectangular grid (12 by 9 and pitch of 10 mm), is also made available. Tensile load from the test bench load-cell sampled at each moment an image is captured is available.

opencc-zeroJan 2024View details →

Morphology of nares associated with stereo-olfaction in baleen whales

The sensory mechanisms used by baleen whales (Mysticeti) for locating ephemeral, dense prey patches in vast marine habitats are poorly understood. Baleen whales have a functional olfactory system with paired rather than single blowholes (nares), potentially enabling stereo-olfaction. Dimethyl sulfide (DMS) is an odorous gas emitted by phytoplankton in response to grazing by zooplankton. Some seabirds use DMS to locate prey, but this ability has not been demonstrated in whales. For all 15 extant species of baleen whale, nares morphometrics (imagery from unoccupied aerial systems, UAS) was related to published trophic level indices using Bayesian phylogenetic mixed modelling. A significant negative relationship was found between nares-width and whale trophic level (β = -0.07, Lower 95% CI = -0.12, Upper 95% CI = -0.02), corresponding with a 36% increase in nares-width from highest to lowest trophic level. Thus, species with nasal morphology best suited to stereo-olfaction are more zooplanktivorous. These findings provide evidence that some baleen whale species may be able to localise odorants e.g., DMS. Our results helps direct future behavioural trials of olfaction in baleen whales, by highlighting the most appropriate species to study. This is a research priority, given the potential for DMS-mediated plastic ingestion by whales.

opencc-by-4.0Nov 2024View details →

Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Stereo Vision

Link to the video as supplementary material for the paper-《Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Stereo Vision》.

opencc-by-4.0Nov 2024View details →

The Light Field & Stereo (LFS) Image Dataset

This dataset contains a collection of monocular light field raw images, stereo-paired images, and calibration files for both the light field camera and the stereo camera. It also includes rectified stereo and depth images as computed using the SGM stereo method, and these are further reprojected to the extrinsically and intrinsically calibrated light field camera frame. This dataset is intended to aid research in computational photography, computer vision, image processing, and related fields. The varied, static images have been captured indoors, aiming to minimize the size of the dataset while maximizing its variability.

opencc-zeroMay 2022View details →

Validating the use of stereo-video cameras to conduct remote measurements of sea turtles

Stereo-Video Camera Systems (SVCSs) are a promising tool to remotely measure body size of wild animals without the need for animal handling. Here, we assessed the accuracy of SVCSs for measuring straight carapace length (SCL) of sea turtles. To achieve this, we hand captured and measured 63 juvenile, sub-adult, and adult sea turtles across three species, greens, Chelonia mydas (n = 52), loggerheads, Caretta caretta (n = 8), and Kemp's ridley, Lepidochelys kempii (n = 3) in the waters off Eleuthera, The Bahamas and Crystal River, Florida, U.S.A. between May - November 2019. Upon release, we filmed these individuals with the SVCS. We performed photogrammetric analysis to extract stereo SCL measurements (eSCL), which were then compared to the (manual) capture measurements (mSCL). mSCL ranged from 25.9 – 89.2 cm, while eSCL ranged from 24.7 – 91.4 cm. Mean percent bias of eSCL ranged from -0.61% (± 0.11 SE) to -4.46% (± 0.31 SE) across all species and locations. We statistically analyzed potential drivers of measurement error, including distance of the turtle to the SVCS, turtle angle, image quality, turtle size, capture location, and species.Using a linear mixed effects model, we found that the distance between the turtle and the SVCS was the primary factor influencing measurement error. Our research suggests that stereo-video technology enables high-quality measurements of sea turtle body size collected in situ without the need for hand-capturing individuals. This study contributes to the growing knowledge base that SVCS are accurate for body size measurements independent of taxonomic clade.

opencc-by-4.0Apr 2024View details →

Mixed-mode fracture: Combination of Arcan fixture and stereo-DIC

data and codes used for article titled "Mixed-mode fracture: Combination of Arcan fixture and stereo-DIC"

opencc-by-sa-4.0Jul 2024View details →

Dublin dataset for stereo and MVS

The Dublin stereo and MVS dataset is based on the original Dublin dataset (check https://geo.nyu.edu/?f%5Bdct_isPartOf_sm%5D%5B%5D=2015+Dublin+LiDAR). Samples were adapted to be compatible with the format required by stereo and MVS networks. Images, ground truth, reference DSM and camera parameters are inlcuded. For more information in detail, please check our article: https://www.mdpi.com/2072-4292/17/1/1

Text-fig. 17. SMNK MP 3, probable female snout of Hippopotamodon erymanthius from Pikermi. A) stereo occlusal view of snout, B) close-up stereo occlusal view of right upper cheek tooth row (scales 5 cm and 10 mm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 17. SMNK MP 3, probable female snout of Hippopotamodon erymanthius from Pikermi. A) stereo occlusal view of snout, B) close-up stereo occlusal view of right upper cheek tooth row (scales 5 cm and 10 mm).

Text-fig. 11. SMNK Ma1 200, mandibular symphysis of Hippopotamodon erymanthius with all incisors and canines from Mahmutgazi, Turkey. A) stereo occlusal view, B) stereo ventral view, C) left lateral view, D) right lateral view (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 11. SMNK Ma1 200, mandibular symphysis of Hippopotamodon erymanthius with all incisors and canines from Mahmutgazi, Turkey. A) stereo occlusal view, B) stereo ventral view, C) left lateral view, D) right lateral view (scale 5 cm).

Text-fig. 2. SMNK Ma1 MP8, right maxilla of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) stereo occlusal view, B) stereo dorsal view (scale 10 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 2. SMNK Ma1 MP8, right maxilla of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) stereo occlusal view, B) stereo dorsal view (scale 10 cm).

Text-fig. 13. SMNK Ma1 Gips 13, right mandible of Hippopotamodon erymanthius from Mahmutgazi, Turkey, with worn p/3–m/3. A) lingual view, B) stereo occlusal view, C) buccal view. This mandible may represent the same individual as the maxilla Ma1 MP 8 (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 13. SMNK Ma1 Gips 13, right mandible of Hippopotamodon erymanthius from Mahmutgazi, Turkey, with worn p/3–m/3. A) lingual view, B) stereo occlusal view, C) buccal view. This mandible may represent the same individual as the maxilla Ma1 MP 8 (scale 5 cm).

Text-fig. 12. Left mandible of Hippopotamodon erymanthius, SMNK Ma1 Gips 2, from Mahmutgazi, Turkey, containing lightly worn p/2–m/3. A) buccal view, B) stereo occlusal view, C) lingual view (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 12. Left mandible of Hippopotamodon erymanthius, SMNK Ma1 Gips 2, from Mahmutgazi, Turkey, containing lightly worn p/2–m/3. A) buccal view, B) stereo occlusal view, C) lingual view (scale 5 cm).

Text-fig. 6. Juvenile specimens of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) Ma1 MP 5, left maxilla containing D3/–D4/ and M1/ (A1 – stereo occlusal, A2 – lingual, A3 – buccal views), B) Ma Nr 196, right mandible containing d/2–d/4 and m/1 (B1 – stereo occlusal, B2 – lingual, B3 – buccal views) (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 6. Juvenile specimens of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) Ma1 MP 5, left maxilla containing D3/–D4/ and M1/ (A1 – stereo occlusal, A2 – lingual, A3 – buccal views), B) Ma Nr 196, right mandible containing d/2–d/4 and m/1 (B1 – stereo occlusal, B2 – lingual, B3 – buccal views) (scale 5 cm).

Text-fig. 14. SMNK Ma1 Gips 12, proximal left radio-ulna of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) medial, B) stereo cranial, C) lateral views (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 14. SMNK Ma1 Gips 12, proximal left radio-ulna of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) medial, B) stereo cranial, C) lateral views (scale 5 cm).

Text-fig. 8. Stereo occlusal views of upper premolars of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Nr 180, left P3/, B) SMNK Ma1 Nr 190, left P4/ (scale 10 mm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 8. Stereo occlusal views of upper premolars of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Nr 180, left P3/, B) SMNK Ma1 Nr 190, left P4/ (scale 10 mm).

Text-fig. 4. SMNK Ma1 Gips 6, mandible of Hippopotamodon erymanthius lacking the ascending rami, from Mahmutgazi, Turkey. A) stereo occlusal view, B) right lateral view (scale 10 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 4. SMNK Ma1 Gips 6, mandible of Hippopotamodon erymanthius lacking the ascending rami, from Mahmutgazi, Turkey. A) stereo occlusal view, B) right lateral view (scale 10 cm).

Text-fig. 10. The lower incisor-canine battery of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Gips 6 (A1 – oblique stereo anterior view, A2 – anterior view); B) SMNK Ma1 Gips 17, oblique stereo anterior view (scales 10 mm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey

Text-fig. 10. The lower incisor-canine battery of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Gips 6 (A1 – oblique stereo anterior view, A2 – anterior view); B) SMNK Ma1 Gips 17, oblique stereo anterior view (scales 10 mm).