MegaStitch: Robust Large Scale Image Stitching

Zarei, Ariyan; Gonzalez, Emmanuel; Merchant, Nirav; Pauli, Duke; Lyons, Eric; Barnard, Kobus

doi:10.36227/techrxiv.16376304.v2

Cited by 1 publication

(3 citation statements)

References 4 publications

(2 reference statements)

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“…PhytoOracle two-dimensional (2D) image processing workflow. (A) The 2D pre-processing steps include the conversion of binary (BIN) files (RGB, thermal, PSII chlorophyll fluorescence) to GeoTIFF files, correction of georeferencing information within each GeoTIFF metadata using Megastitch for RGB and thermal data, clipping corrected GeoTIFF images to plots using a GeoJSON file with plot boundary information, and generation of plot level orthomosaics (Zarei et al, 2022). file per image (see Code Availability Statement).…”

Section: D Object Detectionmentioning

confidence: 99%

“…The first container converted BIN files to GeoTIFF images with approximate GPS bounding coordinates calculated from barcode positioning information contained within the JSON metadata file generated by the FS. The second container deployed MegaStitch, which is a software for efficient image stitching of large-scale image datasets (Zarei et al, 2022). Megastitch was run in a non-distributed manner as all images are required for the global optimization stitching method, which generated geometrically corrected GeoTIFFs.…”

Section: Rgb Processing Pipelinementioning

confidence: 99%

“…Thermal calibration measurements were applied to each pixel, converting the DN value to Celsius. The second container deployed MegaStitch (Zarei et al, 2022) in a non-distributed manner, which generated geometrically corrected GeoTIFFs. The third container clipped GeoTIFFs to plot boundaries specified within a GeoJSON file.…”

Section: Thermal Processing Pipelinementioning

confidence: 99%

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PhytoOracle: Scalable, modular phenomics data processing pipelines

et al. 2023

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As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

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Section: D Object Detectionmentioning

confidence: 99%

Section: Rgb Processing Pipelinementioning

confidence: 99%