Publications by authors named "Mengqi Ji"

7 Publications

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Genome wide association study of the whiteness and colour related traits of flour and dough sheets in common wheat.

Sci Rep 2021 Apr 22;11(1):8790. Epub 2021 Apr 22.

State Key Laboratory of Crop Biology, Key Laboratory of Crop Biology of Shandong Province, Group of Wheat Quality and Molecular Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271000, Shandong, People's Republic of China.

Flour whiteness and colour are important factors that influence the quality of wheat flour and end-use products. In this study, a genome wide association study focusing on flour and dough sheet colour using a high density genetic map constructed with 90K single nucleotide polymorphism arrays in a panel of 205 elite winter wheat accessions was conducted in two different locations in 2 years. Eighty-six significant marker-trait associations (MTAs) were detected for flour whiteness and the brightness index (L* value), the redness index (a* value), and the yellowness index (b* value) of flour and dough sheets (P < 10) on homologous group 1, 2, 5 and 7, and chromosomes 3A, 3B, 4A, 6A and 6B. Four, three, eleven, eleven MTAs for the flour whiteness, L* value, a* value, b* value, and one MTA for the dough sheet L* value were identified in more than one environment. Based on MATs, some important new candidate genes were identified. Of these, two candidate genes, TraesCS5D01G004300 and Gsp-1D, for BS00000020_51 were found in wheat, relating to grain hardness. Other candidate genes were associated with proteins, the fatty acid biosynthetic process, the ketone body biosynthetic process, etc.
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http://dx.doi.org/10.1038/s41598-021-88241-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062544PMC
April 2021

Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior.

IEEE Trans Image Process 2021 2;30:3240-3251. Epub 2021 Mar 2.

Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.
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http://dx.doi.org/10.1109/TIP.2021.3059507DOI Listing
March 2021

A modular hierarchical array camera.

Light Sci Appl 2021 Feb 18;10(1):37. Epub 2021 Feb 18.

Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.

Array cameras removed the optical limitations of a single camera and paved the way for high-performance imaging via the combination of micro-cameras and computation to fuse multiple aperture images. However, existing solutions use dense arrays of cameras that require laborious calibration and lack flexibility and practicality. Inspired by the cognition function principle of the human brain, we develop an unstructured array camera system that adopts a hierarchical modular design with multiscale hybrid cameras composing different modules. Intelligent computations are designed to collaboratively operate along both intra- and intermodule pathways. This system can adaptively allocate imagery resources to dramatically reduce the hardware cost and possesses unprecedented flexibility, robustness, and versatility. Large scenes of real-world data were acquired to perform human-centric studies for the assessment of human behaviours at the individual level and crowd behaviours at the population level requiring high-resolution long-term monitoring of dynamic wide-area scenes.
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http://dx.doi.org/10.1038/s41377-021-00485-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892845PMC
February 2021

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

Patterns (N Y) 2020 Sep 3;1(6):100092. Epub 2020 Aug 3.

Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China.

The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.
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http://dx.doi.org/10.1016/j.patter.2020.100092DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396968PMC
September 2020

SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis.

IEEE Trans Pattern Anal Mach Intell 2020 May 25;PP. Epub 2020 May 25.

Multi-view stereopsis (MVS) tries to recover the 3D model from 2D images. As the observations become sparser, the significant 3D information loss makes the MVS problem more challenging. Instead of only focusing on densely sampled conditions, we investigate sparse-MVS with large baseline angles since sparser sampling is always more favorable inpractice. By investigating various observation sparsities, we show that the classical depth-fusion pipeline becomes powerless for thecase with larger baseline angle that worsens the photo-consistency check. As another line of solution, we present SurfaceNet+, a volumetric method to handle the 'incompleteness' and 'inaccuracy' problems induced by very sparse MVS setup. Specifically, the former problem is handled by a novel volume-wise view selection approach. It owns superiority in selecting valid views while discarding invalid occluded views by considering the geometric prior. Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with repeating pattern. The experiments demonstrate the tremendous performance gap between SurfaceNet+ and the state-of-the-art methods in terms of precision and recall. Under the extreme sparse-MVS settings in two datasets, where existing methods can only return very few points, SurfaceNet+ still works as well as in the dense MVS setting.
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http://dx.doi.org/10.1109/TPAMI.2020.2996798DOI Listing
May 2020

Local stereo matching with adaptive shape support window based cost aggregation.

Appl Opt 2014 Oct;53(29):6885-92

Cost aggregation is the most important step in a local stereo algorithm. In this work, a novel local stereo-matching algorithm with a cost-aggregation method based on adaptive shape support window (ASSW) is proposed. First, we compute the initial cost volume, which uses both absolute intensity difference and gradient similarity to measure dissimilarity. Second, we apply an ASSW-based cost-aggregation method to get the aggregated cost within the support window. There are two main parts: at first we construct a local support skeleton anchoring each pixel with four varying arm lengths decided on color similarity; as a result, the support window integral of multiple horizontal segments spanned by pixels in the neighboring vertical is established. Then we utilize extended implementation of guided filter to aggregate cost volume within the ASSW, which has better edge-preserving smoothing property than bilateral filter independent of the filtering kernel size. In this way, the number of bad pixels located in the incorrect depth regions can be effectively reduced through finding optimal support windows with an arbitrary shape and size adaptively. Finally, the initial disparity value of each pixel is selected using winner takes all optimization and post processing symmetrically, considering both the reference and the target image, is adopted. The experimental results demonstrate that the proposed algorithm achieves outstanding matching performance compared with other existing local algorithms on the Middlebury stereo benchmark, especially in depth discontinuities and piecewise smooth regions.
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http://dx.doi.org/10.1364/AO.53.006885DOI Listing
October 2014
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