Fast Structure from Motion for Sequential and Wide Area Motion Imagery

Aliakbarpour, Hadi; Palaniappan, Kannappan; Seetharaman, Guna

doi:10.1109/iccvw.2015.142

Cited by 31 publications

(16 citation statements)

References 45 publications

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“…Computing homographies for even small crop fields can be very challenging, and both approaches have additional difficulties when compared with mosaicking man‐made built environments (AliAkbarpour et al, 2015, 2017). First, built environments have many unique, sharp‐edged features that remain static during image capture and are minimally repeated over the landscape of interest, enabling the use of edge‐detection algorithms.…”

Section: Figurementioning

confidence: 99%

Robust mosaicking of maize fields from aerial imagery

et al. 2020

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Premise Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. Methods We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two‐dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame‐to‐frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two‐dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. Results The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics. Discussion Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management.

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Section: Figurementioning

confidence: 99%

Robust mosaicking of maize fields from aerial imagery

et al. 2020

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“…While approaches to estimating global rotation of each camera are well established [33]- [35], computing global translation priors is challenging, but recent studies have shown promising results [36], [37]. Moreover, incorporating extra information, such as sparse depth [30], vanishing points [38], GPS [39]- [42] and inertial data, has proven useful with the latter two common for photogrammetry and SLAM.…”

Section: Related Workmentioning

confidence: 99%