Hanno Scharr

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

Dr. rer. nat. habil.

About

126
Publications
59,247
Reads
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4,285
Citations
Citations since 2016
22 Research Items
2633 Citations
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20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
Additional affiliations
August 2003 - present
Forschungszentrum Jülich
Position
  • Head of Department
August 2002 - August 2003
Intel Corp.
Position
  • Senior Researcher at Intel Research
January 2001 - July 2002
Universität Heidelberg
Position
  • PostDoc Position

Publications

Publications (126)
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...
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
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
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 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 currently witness an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected based on complex automated procedures, and are then post-processed to extract phenotyping related information. In this article we show that image compression used in such procedures may compromise phenotypi...
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...
Article
Full-text available
On-going automation in plant phenotyping has led to an increasing amount of measurement data, which is often managed by specialized, rarely interconnected systems with custom hard- And software. Experiment and analysis scenarios across different systems and the setup of new systems quickly get expensive and tedious. Therefore, we propose a distribu...
Article
In this paper, we examine the Ambrosio-Tortorelli (AT) functional [1] for image segmentation from an estimation theoretical point of view. Instead of considering a single point estimate, i.e. the maximum-a-posteriori (MAP) estimate, we adopt a wider estimation theoretical view-point, meaning we consider images to be random variables and investigate...
Conference Paper
Full-text available
High-angular resolution diffusion imaging (HARDI) is a magnetic resonance technique estimating the direction of self-diffusion of water molecules in biological tissue. HARDI encodes at each pixel (voxel) the orientation distribution function (ODF) of water diffusion molecules, i.e. the probability distribution function of finding a water molecule w...
Article
Full-text available
Root systems play an essential role in ensuring plant productivity. Experiments conducted in controlled environments and simulation models suggest that root geometry and responses of root architecture to environmental factors should be studied as a priority. However, compared with aboveground plant organs, roots are not easily accessible by noninva...
Article
Full-text available
We present a framework for Bayesian estimation in kernel feature space with implicit statistical inference in a high or even infinite dimensional feature space. Like in kernel PCA, this space is related to the input space by a nonlinear map consisting of all entities of interest. Inference is performed by means of a Gaussian model in the feature sp...
Conference Paper
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. The structural model allows calculation of physiologically relevant parameters. Roughly speaking, MRI images show local water content of the investigated sample. The small, local amounts of water in ro...
Article
Full-text available
This contribution aims to give a basic introduction to diffusion-like methods. There are many different methods commonly used for regularization tasks. Some of them will be briefly introduced and their connection to diffusion shown. In addition to this we will go into some detail for diffusion-like methods in a narrower sense, i.e. methods based on...
Conference Paper
Full-text available
This paper presents a novel approach for Ambrosio-Tortorelli (AT) image segmentation, or, more exactly, joint image regularization and edge-map reconstruction.We interpret the AT functional, an approximation of the Mumford-Shah (MS) functional, as the energy of a posterior probability density function (PDF) of the image and smooth edge indicator. P...
Article
Full-text available
The capacity for fast-growth recovery after de-submergence is important for establishment of riparian species in a water-level-fluctuation zone. Recovery patterns of two wetland plants, Alternanthera philoxeroides and Hemarthria altissima, showing 'escape' and 'quiescence' responses, respectively, during submergence were investigated. Leaf and root...
Conference Paper
Full-text available
In this paper we extend a standard affine optical flow model to 4D and present how affine parameters can be used for estimation of 3D object structure, 3D motion and rotation using a 1D camera grid. Local changes of the projected motion vector field are modelled not only on the image plane as usual for affine optical flow, but also in camera displ...
Article
Full-text available
We extend estimation of range flow to handle brightness changes in image data caused by inhomogeneous illumination. Standard range flow computes 3D velocity fields using both range and intensity image sequences. Toward this end, range flow estimation combines a depth change model with a brightness constancy model. However, local brightness is gener...
Conference Paper
Full-text available
Relations between deterministic (e.g. variational or PDE based methods) and Bayesian inference have been known for a long time. However, a classification of deterministic approaches into those methods which can be handled within a Bayesian framework and those with no such statistical counterpart is still missing in literature. After providing such...