Hanno Scharr

Hanno Scharr
Forschungszentrum Jülich · IAS-8: Data Analytics and Machine Learning

Dr. rer. nat. habil.

About

153
Publications
75,776
Reads
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5,246
Citations
Additional affiliations
August 2003 - present
Forschungszentrum Jülich
Position
  • Head of Department
August 2002 - August 2003
Intel
Position
  • Senior Researcher at Intel Research
January 2001 - July 2002
Heidelberg University
Position
  • PostDoc Position

Publications

Publications (153)
Preprint
Full-text available
In this work, we couple the functional-structural plant model CPlantBox to the Unreal Engine by exploiting the implemented raytracing pipeline to evaluate light influx on the plant surface. There are many approaches for photosynthesis computation and light evaluation, though they typically are limited by versatility, compute speed, or operate on mu...
Preprint
Full-text available
Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision foundation model, while giving...
Preprint
Full-text available
Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data, i.e., k-space data. This emph...
Conference Paper
Full-text available
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biol...
Preprint
Full-text available
We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products f...
Preprint
In the acquisition of Magnetic Resonance (MR) images shorter scan times lead to higher image noise. Therefore, automatic image denoising using deep learning methods is of high interest. MR images containing line-like structures such as roots or vessels yield special characteristics as they display connected structures and yield sparse information....
Preprint
Full-text available
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biol...
Preprint
Full-text available
Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challe...
Conference Paper
Full-text available
VRoot is an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns. We have conducted a laboratory user study to assess the performance of new users with our software in comparison to established software. We utilize a plant model to derive a synthetic root architecture, providing a bas...
Preprint
Full-text available
State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertainty quantification. While Bayesian neural networks provide a conceptually simple approach to serve tho...
Preprint
Full-text available
Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low tempor...
Preprint
Full-text available
This article describes an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns. We have conducted a laboratory user study to assess the performance of new users with our software in comparison to classical and established desktop software. We utilize a functional-structural plant mode...
Conference Paper
E-infrastructures deliver basic supercomputing and storage capabilities but can benefit from innovative higher-level services that enable use-cases in critical domains, such as environmental and agricultural science.This work describes methods to distribute virtual scenes to the GPU nodes of a modular supercomputer for data generation.High informat...
Article
Full-text available
The successful operation of airborne and space-based spectrometers in recent years holds the promise to map solar-induced fluorescence (SIF) accurately across the globe. Machine learning (ML) can play an important role in this effort, but its application to SIF retrieval methods is in part hindered by the need for time-consuming radiative transfer...
Article
Full-text available
In plant science it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data is currently limiting. To overcome this bottleneck, synthet...
Article
Full-text available
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell l...
Preprint
Full-text available
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell l...
Preprint
Full-text available
Reliable deep learning segmentation for microfluidic live-cell imaging requires comprehensive ground truth data. ObiWan-Microbi is a microservice platform combining the strength of state-of-the-art technologies into a unique integrated workflow for data management and efficient ground truth generation for instance segmentation, empowering collabora...
Chapter
Use of imaging data has become more profound with the advent of digital cameras, the internet, and automation. With advances of computer vision, actionable information can now be extracted from images to advance the study of plants and their phenotype. In this chapter we document the experience of advancing the state of the art in developing method...
Article
We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induc...
Preprint
Computer Vision problems deal with the semantic extraction of information from camera images. Especially for field crop images, the underlying problems are hard to label and even harder to learn, and the availability of high-quality training data is low. Deep neural networks do a good job of extracting the necessary models from training examples. H...
Article
Full-text available
Background: Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping...
Article
In this contribution we introduce an almost lossless affine 2D image transformation method. To this end we extend the theory of the well-known Chirp-z transform to allow for fully affine transformation of general $n$ -dimensional images. In addition we give a practical spatial and spectral zero-padding approach dramatically reducing losses of our...
Chapter
In phenotyping experiments plants are often germinated in high numbers, and in a manual transplantation step selected and moved to single pots. Selection is based on visually derived germination date, visual size, or health inspection. Such values are often inaccurate, as evaluating thousands of tiny seedlings is tiring. We address these issues by...
Article
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access dataset suitable for ML in image-based plant phenotyping suitable for machine learning, fuelling a true interdisciplinary symbiosis, increased awareness, and steep perf...
Article
Full-text available
Background Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such...
Article
Full-text available
Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs...
Article
Full-text available
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent releas...
Preprint
Full-text available
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent releas...
Article
Full-text available
In large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual...
Article
Full-text available
Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive imaging technique allowing to estimate the molecular self-diffusion tensors of water within surrounding tissue. Due to the low signal-to-noise ratio of magnetic resonance images, reconstructed tensor images usually require some sort of regularization in a post-processing step. P...
Article
We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phe...
Article
Full-text available
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional tra...
Article
Full-text available
We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camer...
Article
Full-text available
Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variabil...
Article
Full-text available
Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, pote...
Article
Full-text available
Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision task...
Article
Full-text available
Precise measurements of leaf vein traits are an important aspect of plant phenotyping for ecological and genetic research. Here we present a powerful and user-friendly image analysis tool, named phenoVein. It is dedicated to automated segmenting and analyzing leaf veins of images acquired with different imaging modalities (microscope, macro photogr...
Conference Paper
Full-text available
We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 µm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camer...
Article
Full-text available
We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise p...
Conference Paper
Full-text available
In this work we derive a novel framework rendering measured distributions into approximated distributions of their mean. This is achieved by exploiting constraints imposed by the Gauss-Markov theorem from estimation theory, being valid for mono-modal Gaussian distributions. It formulates the relation between the variance of measured samples and the...
Article
Full-text available
Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and performance) as a result of genotype differences (i.e., differences in the genetic code) and the environmental conditions to which a plant has been exposed [1]-[3]. According to the Food and Agriculture Organization of the United Nations, large-scale...
Article
Full-text available
Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure...
Conference Paper
Full-text available
Die Vielzahl an verfügbaren Mess-und Analysemethoden im Bereich der Phänotypisierung und verwandter Disziplinen der Pflanzenwissenschaften produziert einen stetig wachsenden, oft heterogenen Bestand an Experimentaldaten. Neben der Schwierigkeit eine hierfür geeignete Verwaltungs-und Datenzugriffsplattform bereitzustellen, besteht auch ein zentrales...
Article
Full-text available
In this paper, we present a novel multi-level procedure for finding and tracking leaves of a rosette plant, in our case up to 3 weeks old tobacco plants, during early growth from infrared-image sequences. This allows measuring important plant parameters, e.g. leaf growth rates, in an automatic and non-invasive manner. The procedure consists of thre...
Article
Full-text available
Background Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its appl...
Thesis
Full-text available
Text dt. Heidelberg, Universität, Diss., 2000. Computerdatei im Fernzugriff.
Technical Report
Full-text available
While image-based approaches to plant phenotyping are gaining momentum, benchmark data focusing on typical imaging situations and tasks in plant phenotyping are still lacking, making it difficult to compare existing methodologies. This report describes a benchmark dataset of raw and annotated images of plants. We describe the plant material, enviro...
Article
Parameter estimation in the presence of noisy measurements characterizes a wide range of computer vision problems. Thus, many of them can be formulated as errors-in-variables (EIV) problems. In this paper we provide a closed form likelihood function to EIV problems with arbitrary covariance structure. Previous approaches either do not offer a close...
Article
Full-text available
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a n...
Data
Full-text available
a b s t r a c t Several longer-term assembly studies on ex-arable land have found that species that arrive first at a disturbed site can play a key role in the further development of the community and that this priority effect influences aboveground productivity, species diversity and stability of the grassland communities that develop. Restoration...
Data
Full-text available
In the face of rapidly declining diversity interest in how plant diversity and ecosystem functioning interrelate and how this relationship may differ across various systems is high. We know that grasslands with more species and functional traits interacting can positively affect ecosystem functioning such as productivity or nutrient cycling. These...
Patent
Full-text available
Disclosed is a method and an apparatus for measuring the growth of leaf disks. The method comprises the following steps: a) Calibrating the capture system, b) capturing at least 2 images of a leaf disk, c) processing the image data, comprising i) segmenting the leaf disks by threshold segmentation, ii) multiple morphological erosion steps, iii) edg...
Chapter
Full-text available
We present a novel method for deriving a structural model of a plant root system from 3D Magnetic Resonance Imaging (MRI) data of soil grown plants and use it for plant root system analysis. The structural model allows calculation of physiologically relevant parameters. Roughly speaking, MRI images show local water content of the investigated sampl...