Research Paul Scheunders

Abstract

The main goal of this project is to develop a new data analysis approach for quantitative material characterization from spectral reflectance. If successful, the proposed approach will enable accurate estimation of material properties, in a non-destructive and non-contact manner, unlike many existing laboratory-based measurement techniques. Moreover, it is employable in situ, for the inspection of large infrastructures or quality control in production. The proposed approach combines a geometric description in which material properties are represented on curves or low-dimensional surfaces, and machine learning to relate these representations to actual estimates of material properties. The representation is made invariant to spectral variability, to make it applicable under variable environmental and acquisition conditions and in cross-sensor situations. The proposed methodology will be developed for a large group of material characterization tasks. More specifically, methods are developed for estimating the material composition (i.e., the mass fractions of the material components), detecting target material components, and estimating water content. Furthermore, three use cases of the developed methodologies will be elaborated, related to corrosion monitoring and characterization of building materials.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Bnoulkacem Zakaria

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

A material can be uniquely characterized by its optical reflectance spectrum. Hyperspectral cameras disperse the reflected sunlight into hundreds of consecutive small wavelength bands in the visible and near-infrared (VNIR, 400-1000 nm) and the shortwave infrared (SWIR, 1000-2500 nm) regions. The main objective of this project is to develop advanced innovative spectral analysis methods that relate optical reflectance to material properties. I will focus on 3 particular material properties, for which a framework will be developed, validated, and applied on specific case studies: 1. mineral composition estimation, with a case study in geological mining. 2. plant leaf biochemical parameter estimation, with a case study in multi-scale forest ecological functioning 3. water content estimation, with a case study in climate change effects on built heritage

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Koirala Bikram

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

With technological progress, small size, low cost multispectral and hyperspectral cameras become available that capture the light in up to hundreds of consecutive small wavelength bands in the visible and near infrared (VNIR, 400-1000 nm) and the shortwave infrared (SWIR, 1000-2500 nm) regions. These cameras can be installed on unmanned aerial vehicles, agricultural equipment or conveyer belts, or used in laboratory environments and even be used as handheld devices. Numerous applications can be considered for industrial inspection and quality control. As water has very strong absorption power, in particular in the SWIR range (e.g., absorption peaks around 1400 and 1900 nm), the optical reflectance properties of water-bearing materials are dominated by water. This can be disadvantageous when characterizing materials, as the material reflectance characteristics are largely hidden because of the water absorption. On the other hand, this gives opportunities to focus on water-related properties of a material, e.g., its water content, or specific material parameters that can be related to water content, e.g., leaf physiological parameters such as the Equivalent Water Thickness. The goal of this project is to study the characterization of water-bearing materials from optical reflectance imaging and to estimate and spatially resolve the water content and other relevant water-related material parameters. The main novelty and challenge is the development of a hyperspectral image analysis framework that is • invariant to environmental and acquisition conditions, • generically applicable to a large group of materials. These improvements will allow to upscale from point-based laboratory applications on benchmark datasets to spatially resolved real world in situ applications. The developed framework will be validated on two specific use cases: moisture content estimation in soils, and plant leaf parameter estimation.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Jambhali Ketaki Vinay

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The European Green Deal relies on healthy forests to remove carbon (C) from the atmosphere, stabilize the water cycle and provide sufficient biomass for the future bioeconomy. The Flanders Forest Living lab realizes a specific breakthrough in the assessment of these crucial ecosystem functions, at spatial scales ranging from the individual tree to the entire forest. The Living Lab is situated in an ICOS flux-tower observatory, that currently already provides a permanent assessment of ecosystem scale CO2-fluxes, evapotranspiration and respiration. To date however, no technique is available to study the function of individual trees, at daily resolution, across a forest. achieving this is the groundbreaking objective of this new infrastructure. Its specific equipment allows for crucial realistic simulation of the water-, energy- and carbon fluxes by advanced vegetation models at spatial scales matching those of satellite imagery products, thereby creating new possibilities for applications such as automated precision forestry management, fire prevention and worldwide carbon budget quantifications. The new infrastructure involves an UAV and a set of linked validation sensors. Observations are steered by artificial intelligence, in order to be able to adapt the flight pattern to the fluctuating source area of the flux-tower, and in order to proactively adapt to specific weather patterns and potentially interesting ground-sensor observations.

Researcher(s)

• Promoter: Janssens Ivan
• Co-promoter: Campioli Matteo
• Co-promoter: Gielen Bert
• Co-promoter: Latré Steven
• Co-promoter: Nijs Ivan
• Co-promoter: Roland Marilyn
• Co-promoter: Scheunders Paul
• Co-promoter: Vicca Sara

Research team(s)

• Plant and Ecosystems (PLECO) - Ecology in a time of change

Project type(s)

• Research Project

Abstract

CLIMPACTH scopes safeguarding our built heritage in the context of climate change by combining state-of-the-art expertise on material characterization, degradation processes, hygrothermal modelling, climate modelling and built heritage. Our built heritage is an invaluable collection, being of exceptional cultural, social and economic importance. At the same time, it is also fragile and vulnerable due to its longterm exposure to the environment. Therefore, the sustainable management of our built heritage requires the implementation of actions to reduce its vulnerability and to increase its resilience to the risks of climate change. Furthermore, as the public and private sectors are pushed to invest in near-zero energy buildings for economic, ecologic and climate action reasons, the pressure on our built heritage to follow the same path is increasing, posing an additional threat to its historical and cultural values. Caring about our built heritage also means caring about the collections kept within, as historic houses, museums and churches are often located within built heritage. At the international level, the EU-funded projects NOAH's ARK (2004-2007) and Climate for Culture (2009-2014) set the first monumental steps to study the direct and indirect effects of climate change on built heritage at the European level. In addition, the International Council on Monuments and Sites (ICOMOS) has recently established a working group on climate change and heritage to comprehensively scope the intersection between cultural heritage and climate change, identifying both the strengths and challenges in this regard. CLIMPACTH will further build on these research initiatives at the international level and maximize collaboration with research initiatives at the national level to address the needs to adopt a global management strategy following the preventive conservation approach. The processes underlying these risk factors can be understood as material degradation in response to their constant exposure to various climate factors. In addition to these research initiatives, it is imperative to advance our knowledge of the characterization of historic materials and projected climatic parameters to address the risk assessment in an overall management strategy. The former will be assessed through in-depth research on material characteristics and degradation processes, and on-site assessment and monitoring of building envelope conditions. This will allow us to comprehensively generalize the built heritage envelopes and their condition for hygrothermal modelling. The latter can be assessed by estimating the evolution of climate factors under climate change conditions and their uncertainty using an ensemble of climate projections at global, regional and urban scales. This will allow us to propagate the uncertainties in climate projection to the scale of heritage buildings using dedicated bias correction methods. It will also allow us to carry out an integrated assessment of the degradation of risks under climate change conditions. By examining the particular conditions of built heritage and its exposure to the outdoor climate, risk factors will be reassessed and assessment strategies will be developed, taking into account the impact of the exterior on the interior and prioritizing the main threats.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

A widely used non-destructive method for material inspection is computer vision using RGB cameras along with matching image analysis. This allows to spatially resolve inhomogeneities in the materials. Computer vision however is limited to the visual part of the electromagnetic spectrum (400-700 nm), while many of the chemical processes and mineral formations in materials have particular reflectance properties in the near infrared (NIR, 700-1000 nm) and the shortwave infrared (SWIR, 1000-2500 nm). Spectrometers, on the other hand, are able to resolve these properties in the spectral direction but can only provide point-based measurements. The main scientific objective of this project is to exploit and enhance the potential of hyperspectral imaging in the NIR and SWIR range for the characterization of heterogeneously mixed and compound materials. For this, we will develop hyperspectral image analysis methods with increasing levels of complexity, ranging from spectral indices, characteristic for minerals and compounds in the materials, over methods based on spectral libraries, to supervised spectral unmixing methods. The developed methods will be validated on data acquired from homemade mixtures (mineral mixtures, sands, powdered clays and mortar) and implemented on two real use cases, i.e. mineral detection and corrosion detection.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Q-INSPEX aims at the development of novel imaging and image processing protocols to non-invasively and quantitatively inspect objects and subjects. Core imaging technologies herein are X-ray, (near)-infrared, and TeraHertz imaging. These technologies are largely complementary to each other and can be used in different set-ups as (i) an R&D tool to measure specific characteristics of materials (e.g. food structures or polymers), (ii) as a quality control procedure implemented within an industrial setting (i.e. compatible with processing speeds) or (iii) in-field inspections of crops and infrastructure (e.g. corrosion). Furthermore, they can be applied in a wide variety of domains: additive manufacturing, composites, art objects, textiles, archaeology, crops, food, etc.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Janssens Koen
• Co-promoter: Scheunders Paul
• Co-promoter: Steenackers Gunther
• Co-promoter: Van der Snickt Geert
• Fellow: De Samber Björn
• Fellow: Levrau Elisabeth

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The Port of Antwerp-Bruges is seeking solutions to perform accurate and fast detection of oil incidents in the port area. The port is polluted on a regular basis by oil which has a serious environmental and economical impact. Amongst the economical impacts are the costs for clean-up of the oil, which are related to its size, volume, the type of product and the location. These costs variate year to year, but in average we speak about the budget of 700K up to one million euro per year. The port is seeking solutions for early detection of the spills and follow up the spill while cleaning. Drones are seen as the ideal platform and are part of the port's future strategy towards digitization. To make drone inspections operational in 2021 is the priority of the Port of Antwerp-Bruges Authority. For this proposal, the port has identified two partners in Belgium, VITO and the University of Antwerp which have the necessary expertise to develop an appropriate solution. The objectives of this project are (1) to develop a prototype workflow to detect oil spills from a drone and (2) demonstrate the technology in the Port of Antwerp-Bruges. Port of Antwerp-Bruges Authority will launch a tender in Q3 2021 to select a company which will provide drone inspections in the port area. Automated oil spills detection is a part of the inspection program. Therefore it is very important for the Port of Antwerp-Bruges to help to develop this technology and to bring it to a higher TRL level. The Antwerp Port authority is co-funding by opening up their infrastructure, introducing an artificial spill and providing continuous feedback on the results. The result will be a prototype workflow to detect oil spills from drone images at TRL 5-6, accompanied by a protocol for the camera settings and flight protocol. The innovation in this proposal is linked to the challenging application of the technology in a complex and harsh port environment and the combined use of SWIR and RGB imagery.

Researcher(s)

• Promoter: Vanlanduit Steve
• Co-promoter: Scheunders Paul

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Oil spills in ports pose a direct risk to the environment and marine life, along with operational risks (sailing through the discharges, fouling of ships, fire risk) and high costs for the port itself. This project aims at an automatic detection, determination of severity and centralisation of communication based on advanced LWIR (longwave infrared) and multispectral SWIR (shortwave infrared) imaging via drones. Automation saves time, costs and creates a safer and cleaner port environment. and cleaner port environment.

Researcher(s)

• Promoter: Vanlanduit Steve
• Co-promoter: Scheunders Paul

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The overall objective of the project is to demonstrate the technical feasibility and economic added value of using of hyperspectral cameras for corrosion inspection and quality control of corrosion solutions (corrosion cleaning, wet and dry coatings and chemical surface treatments). The project focuses on the entire corrosion value chain. On the one hand, these are owners of infrastructure (petrochemical, port, energy), a group of some 100 mainly large Flemish companies, and on the other hand companies offering corrosion protection services (about 150). protection services (approximately 150 SMEs). For the rollout of the results, it is important that also companies that are active in corrosion inspection and camera integrators are also actively involved. These two groups of Flemish SMEs each count about 20 companies that, compared to the previously mentioned companies, have a larger R&D capacity to be able to implement the technology.

Researcher(s)

• Promoter: Vanlanduit Steve
• Co-promoter: Scheunders Paul

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

In mineralogical studies, the use of hyperspectral images helps to detect different minerals in a faster and more accurate manner. However, it can be problematic to process such data, as the acquisition of sufficient ground truth information is challenging. Therefore, the main goal of this research is to develop unsupervised learning techniques (sparse subspace-based clustering algorithms) to distinguish different mineralogical features from hyperspectral images. As a first milestone for the proposed research topic, a new sparse subspace-based clustering algorithm was developed to cluster the mineralogical features into meaningful groups. The proposed algorithm is able to process highly mixed and complex data in a robust and fast manner. As a follow-up, spatial information was exploited in a newly developed clustering algorithm. Such information helps to take into account spatial structures of mineralogical samples. By including the spatial information, the precision of the proposed algorithm increased. In the period to come, we will explore the potential of deep clustering methods, based on state-of-the-art sparse autoencoder-based algorithms, or the concepts of deep image priors. As a result of the study, fast and robust deep learning-based clustering algorithms will be developed, to analyze complex hyperspectral images from mineralogical and geological features.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Rafiezadeh Shahi Kasra

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The main motivation of this project is a major opportunity that came up in the beginning of 2020, when 3 hyperspectral cameras and a spectrometer became available to our lab. This opportunity will enable us to generate high quality ground reference data for the validation of the nonlinear unmixing methods that are developed in the GEOMIX project. The objective of this support project is the acquisition, calibration and analysis of ground truth datasets for nonlinear spectral unmixing, through very well thought-out experiments in a controlled lab environment.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Detection and identification of illegal drugs is a major task of both police and customs authorities in order to prevent drug-dealing and consumption in our society. An accurate test is crucial to support this process. Within the AXES research group at UAntwerp, a new method was developed to achieve a fast and accurate detection of cocaine at low cost, using an electrochemical sensor. By using this method, the limitations and restrictions of existing tests can be tackled (i.e. interpretation sensitivity, false positives/negatives, and environmental influences). The developed technique is currently operational in a lab setting, but needs to be adjusted and translated to be effective on location. For this reason it is crucial in this phase of the research to focus on the development of a software to translate the scientific data into a simple readout for non-experts and on an appropriate valorization plan to bring the final product as close as possible to the market. Thus, we aim within the present project to (1) develop methods for data treatment and analysis which will strikingly improve accuracy of drug detection, (2) thoroughly test and verify the final portable prototype product with future end users, and (3) built a sound valorization plan. It should be emphasized that the scope of the data analysis is broader than only drugs detection. The developed methods for data treatment and analysis will also be used in future for the interpretation of the voltammetric fingerprints of other target molecules such as antibiotics.

Researcher(s)

• Promoter: De Wael Karolien
• Co-promoter: Scheunders Paul

Research team(s)

Project type(s)

• Research Project

Abstract

The main objective of the project is the creation of a powerful yet flexible hyperspectral unmixing methodology, based upon sound physical and mathematica! principles, validated on extensive artificial and real data sets, and made publicly available through publication and sharing of program code. The unmixing methodology is based upon the recently developed multilinear mixing model, and will extend this state-of-the-art method with several additional capabilities.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Our overall objective is to develop data fusion techniques that account for these spectral aspects and in this way enhance the spectral properties of a fused image or a derived product such as a land cover classification map.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The aim of the research is to extend our recently developed geometrical framework for spectral unmixing with recently gained insights on manifolds and the convex geometry of high-dimensional data sets. This framework will be employed for unsupervised non-linear unmixing of hyperspectral data by using geodesic distances on the data manifold.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Heylen Rob

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This project is part of the BELAIR project. This project aims at the development of a Belgian super test site, where targeted Earth observation data and in situ data are collected for the Belgian and international research community.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The theoretical and computer science-based expertise of Visionlab (UA) on data-driven spectral unmixing approaches and the applied biological science expertise of M3BIORES (KULeuven) on modeling and simulation-based unmixing approaches will be combined into a unique interdisciplinary framework for tackling the problem of nonlinear spectral mixtures. The shared goal is to obtain better insights into the causes of non-linear mixing and the error resulting from a linear mixing assumption, to mitigate its effects in existing unmixing applications, and to use the obtained knowledge to extract the maximum of information on the observables of the system under consideration. The developed methodology will be validated and applied to vegetation monitoring.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This project represents a formal research agreement between UA and on the other IBBT. UA provides IBBT research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Vegetation and in particular green and full grown trees are extremely important in urban environments, amongst others for their beneficial effects on the reduction of air pollution. CIties are however not ideal environments for these trees and their health condition should be monitored carefully. A large scale biomonitoring of urban vegetation cannot be done manually, and remote sensing, and hyperspectral imaging in particular, announces themselves as a perfect candidate for an automated procedure. The aim of this project is to develop a framework for biomonitoring of urban vegetation from canopy spectral reflectance as this is the information that can be obtained from hyperspectral remote sensing. A data-driven approach is developed by constructing a hyperspectral reflectance library of leaf and canopy reflectance spectra. From this library, we will (i) study the relation between leaf-level and canopy-level reflectance; (ii) study the spectral distinction between healthy and unhealthy trees, and (iii) study the spectral distinction between trees growing at sites with different loads of air pollution.

Researcher(s)

• Promoter: Scheunders Paul
• Co-promoter: Samson Roeland

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The main objective of this project is to explore and further develop the current semi-supervised and active learning techniques for the specific application of vegetation mapping. In particular, our aim is to tackle the problem of limited ground reference data by investigating the re-use of vegetation reference data. As a prototype problem, we envisage the classification of vegetation from hyperspectral images acquired at the same location on different occasions or at different locations containing similar vegetation types. The goal is then to design strategies for the re-use of reference samples obtained from one occasion or location to improve the classification at the other occasions or locations.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The goal of CHAMELEON is to design the next generation vision system that exploits the benefits of spectral information. The mission and novelty of CHAMELEON is: To enable and demonstrate flexible, but domain-specific hyperspectral imaging systems for relevant industrial applications. More specifically, Chameleon will promote direct usage of hyperspectral imaging by the industry by providing flexibility in terms of: • System exploration and simulation tools • Novel camera architectures, including parameterizable templates • Novel hyperspectral image processing methods and techniques • Mapping, data handling and interpretation strategies for real-time operation • Novel measurement strategies

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Sinusoidal analysis is an important technique used to analyse audio. However, when analysing polyphonic audio, the harmonic components of different notes tend to overlap and the resulting system matrix cannot be solved. Using additional information, mainly from a music score, should enable us to start from a better initial guess of the model, and determine the correct number of parameters beforehand. This should result in a much more robust and accurate analysis. The first part of this project is concentrated around the mathematical an technical problems involved, and will also consist of the development of the necessary software tools. The second part of the project focuses on applications, more specifically on source separation or elimination and on individual note editing in a polyphonic context.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Ganseman Joachim

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The increasing amount of image data available from airborne and satellite sensors demands new strategies to overcome the trade-off in spectral and spatial resolution. in this project, two different approaches to enhance the spatial information in hyperspectral image are tackled: state-of-the-art data fusion and advanced unmixing techniques.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The purpose of this research is in the first place to model the Adjacency effect (i.e. to distinguish the different components of the radiance) and to develop correction algorithms that can be applied to high resolution data sets. These can be hyperspectral as well as multispectral image data. At the start, the Adjacency effect will be studied on the separation between water and land, but the general purpose is to develop a general analysis method, leading to generic correction algorithms.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Geens Bert

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Medical imaging is getting more and more complex. Unfortunately the medical imaging community today by far is not optimally making use of color and multi-dimensional information. This research project will significantly improve this situation. In the technology working packages we will develop platform technology to better handle multi-dimensional and color data. The basic technology that will be developed in this project covers the entire imaging chain starting with acquisition device over image processing to visualization and finally (clinical) validation and standards. This technology will be use as a basis in other working packages and applied to specific clinical applications.

Researcher(s)

• Promoter: Scheunders Paul
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Sinusoidal analysis is an important technique used to analyse audio. However, when analysing polyphonic audio, the harmonic components of different notes tend to overlap and the resulting system matrix cannot be solved. Using additional information, mainly from a music score, should enable us to start from a better initial guess of the model, and determine the correct number of parameters beforehand. This should result in a much more robust and accurate analysis. The first part of this project is concentrated around the mathematical an technical problems involved, and will also consist of the development of the necessary software tools. The second part of the project focuses on applications, more specifically on source separation or elimination and on individual note editing in a polyphonic context.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Ganseman Joachim

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The project proposes to research the following ¿ Definition of a vision beyond Web2.0/Telco2.0/Enterprise2.0 starting from an in depth analysis of market and technology trends. ¿ Implementation of the vision under form of the definition of a WTEPlus framework. ¿ Research on a number of key functional elements of the framework: workflow and service assurance ¿ Development of a number of Proof of Concepts in the three application domains.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The goal of this project is to develop new statistical models for spatial classification procedures for multispectral and hyperspectral remote sensing data, in which spectral, polarimetric and spatial information is treated simultaneously. The methodology is based on the following principles: 1. the use of multiresolution techniques 2. the use of Bayesian principles 3.the use and the development of new multivariate Markov Random Field models

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This project will concentrate on a particular image processing and image analysis problems. This problem is about creating and analyzing large biomedical image datasets. When the main goal is analysis, or CT reconstrcution, there is a need for automatic construction of a so called mosaic images, aligning and combining all images, creating a large FOV (field-of-view) image.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Traditional image processing and analysis techniques have been developed for scalar images. However, with the evolution of imaging technology, often multivalued images are produced. In contrast to scalar images, where every pixel contains a single gray value, the pixels of these images contain vectorial information. Examples are color images, multispectral images and multimodal biomedical images. Image segmentation is an essential process for most subsequent image analysis tasks, because low-order pixel based information is converted into region based higher-order information, which allows to analyze the information more efficient. Image restoration is also an important preprocessing step, because homogeneous images, where the noise was removed, are easier to process with a segmentation algorithm. The goal of this project is to develop a new technique for segmentation of multivalued images. The algorithm will contain a preprocessing procedure to reduce the noise. The proposed technique will be based on the combination of the following three principles: ¿Interband approach: By processing the information of all bands simultaneously, both the spatial and the spectral information is used for the restoration and segmentation. This way, all the available information is fully exploited. ¿Multiresolution approach: Multiresolution techniques such as the wavelet transform decompose an image in several resolution scales. Restoration is useful in this representation, because the noise amplitude typically decreases at the lower resolution scales. For segmentation, a coarse segmentation on lower resolution scales can be used to obtain a finer segmentation on higher resolution scales, with a hierarchical procedure. ¿Model based approach: An a-priori model is assumed for the probability density function (pdf) of the pixels. The parameters of the model are estimated by means of the available images and this data can be used for restoration and segmentation. The second part of this project consist of the validation of the proposed techniques. This validation will be performed on two different research fields with realistic applications: ¿Multispectral images (earth observation). By means of cooperation of the Visionlab with the Teledetectie and atmospheric processes (TAP) department of the VITO, there are multispectrale and hyperspectrale datasets available with applications in forestry, vegetation monitoring, ground pollution with heavy metals, etc.... ¿Multimodal biomedical images. The visionlab has a cooperation with the research group 'Bio-imaging Lab' of the University of Antwerp, where magnetic imaging (MRI) is used for biomedical research.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Driesen Jef

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This project aims to develop a operation-oriented methodology to map, to monitor and to evaluate habitat characteristics, vegetation types and gradients in order to determine nature quality. While the proposed methodologies are generic enough to support any classification problem with remote sensing and applicable in many domains, our focus is a transferable platform for habitat status reporting. A major application, and for the test and validations of this project, is the detailed mapping of ecotopes that can assess the conservation status of Natura 2000 habitats. . In remote sensing, we most often have to deal with sensor limitations. No single sensor combines the optimal spectral, spatial and temporal resolutions. Mapping of ecotopes would be difficult without hyperspectral data. Spaceborne hyperspectral data, which give good large coverage, is always coarse in resolution. To improve spatial resolutions of spaceborne hyperspectral imagery, we propose the use of superresolution image reconstruction techniques which utilize the complementary information in multi-angle overlapping imagery. Conventional classification methods do not address spatial and structural dimensions adequately for habitat reporting. One objective of this project is to make optimal use of available sensors. Spectral information will be extended to include multi-scale information, spatial features (classification of segmented regions, textural/contextual features), and post-processing of classification results (clustering, rule-based learning). Structural analysis will target the composition of vegetation types that can be used for diagnosis of habitat quality as well as future change modeling. The innovative of this proposal is the combination of all these techniques to fully exploit the available data. Rather than just improving state of the art spatial features and superresolution techniques, this project will assess the implication of the improved features on the classification performance. Furthermore, the classification framework will be enhanced by introducing ensemble classifiers. The objective is to investigate the operational potential of ensemble classifiers in terms of stability, accuracy, ease of use, and computing costs. The developed algorithms and methodologies aimed for habitat status reporting will be integrated and tested by a very specific application: assessing the conservation status of Natura 2000 habitats in Special Areas for Conservation' (SACs). While this proposal is composed of heavy methodological elements, user's requirements will provide all the guidelines for developments and integrations as well as defining expected results. INBO and Alterra, both specifically involved in the implementation of the EU directive to report on Natura 2000 habitats, and the mapping of vegetation and habitat types in general, are ideal partners in the domain of habitat reporting. This cross-disciplinary proposal will be first to address operational issues with habitat status reporting using novel and advanced remote sensing methodologies. The end results will have significant implications on future methodologies for biodiversity and habitat monitoring. The outcome from the technical aspects will be beneficial to some core scientific issues and generic developments of remote sensing methods .

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Industrial vision applications in which colour evaluation forms the key problem, is a separate and rather difficult part in vision technology. An industrial accepted calibration method will help in solving a lot of practical problems than and will give progress to the use of colour vision applications. The Skr-matrix method, for which patent PCT/EP2005/003889 is taken by the Antwerp Innovation Centre and which is developed at the Lab for Industrial Vision, forms a powerful Colour Handling Method which can be used in all kinds image handling industries. Especially, we think about digital camera's, scanners, copiers, screens, IR-camera's...

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In ecology the description of patterns in a vegetation and the understanding of the process-pattern paradigm forms an essential part of vegetation and landscape studies. The description of these patterns is frequently hindered (hampered) by scale effects. The use of wavelets in the analysis of scale dependent patterns can offer a solution for this complex problem with applications in a.o. the validation of teledetection products, an essential part of studies in physics.

Researcher(s)

• Promoter: Ceulemans Reinhart
• Co-promoter: Scheunders Paul

Research team(s)

• Plant and Ecosystems (PLECO) - Ecology in a time of change

Project website

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In this project, improved statistical tests will be developed voor functional magnetic resonance imaging (FMRI) data analysis. Thereby, parametric tests, based on generalized likelihood ratio tests, as well as non-parametric tests (such as clustering) will be investigated. Finally, advanced methods for visualisation of fMRI detection results on high resolution MR images will be developed.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Traditional image processing and analysis techniques have been developed for scalar images. However, with the evolution of imaging technology, often multivalued images are produced. In contrast to scalar images, where every pixel contains a single gray value, the pixels of these images contain vectorial information. Examples are color images, multispectral images and multimodal biomedical images. Image segmentation is an essential process for most subsequent image analysis tasks, because low-order pixel based information is converted into region based higher-order information, which allows to analyze the information more efficient. Image restoration is also an important preprocessing step, because homogeneous images, where the noise was removed, are easier to process with a segmentation algorithm. The goal of this project is to develop a new technique for segmentation of multivalued images. The algorithm will contain a preprocessing procedure to reduce the noise. The proposed technique will be based on the combination of the following three principles: ¿Interband approach: By processing the information of all bands simultaneously, both the spatial and the spectral information is used for the restoration and segmentation. This way, all the available information is fully exploited. ¿Multiresolution approach: Multiresolution techniques such as the wavelet transform decompose an image in several resolution scales. Restoration is useful in this representation, because the noise amplitude typically decreases at the lower resolution scales. For segmentation, a coarse segmentation on lower resolution scales can be used to obtain a finer segmentation on higher resolution scales, with a hierarchical procedure. ¿Model based approach: An a-priori model is assumed for the probability density function (pdf) of the pixels. The parameters of the model are estimated by means of the available images and this data can be used for restoration and segmentation. The second part of this project consist of the validation of the proposed techniques. This validation will be performed on two different research fields with realistic applications: ¿Multispectral images (earth observation). By means of cooperation of the Visionlab with the Teledetectie and atmospheric processes (TAP) department of the VITO, there are multispectrale and hyperspectrale datasets available with applications in forestry, vegetation monitoring, ground pollution with heavy metals, etc.... ¿Multimodal biomedical images. The visionlab has a cooperation with the research group 'Bio-imaging Lab' of the University of Antwerp, where magnetic imaging (MRI) is used for biomedical research.

Researcher(s)

• Promoter: Scheunders Paul
• Fellow: Driesen Jef

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The topic of this project is a nalysis and segmentation of multispectral images with applications in remote sensing . We will develop multispectral versions of low-level image processing operators, multispectral edge detectors and techniques for multispectral texture characterisation. We will vaildate the techniques in remote-sensing applications.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Hyperwave belongs to the research domain of hyperspectral image processing. Hyperspectral sensors have a high spectral resolution of a few nanometers. Opposite to multispectral sensors, they can register small differences in spectral signature. The disadvantage is that they produce an enormous amount of data. It is impossible to process the data without reduction. The challenge is to extract the useful information from the raw data and to ignore redundant data. A previous project, Hypercrunch (SR/00/05) was a firs onset to this. However, the application, detection of stress in orchard, was very specific and extremely ambitious. With this project, we aim at further developing the algorithms and using them for totally different applications. The final goal is to build the algorithms into processing chains of hyperspectral data, in view of the operational exploitation of the hyperspectral APEX instrument, that start in 2005.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The goal of the project is to develop methods for classification en detection based on hyperspectral images. Unmixing of the spectra into there constituent pure spectra is necessary to make the classification. Therefore, unmixing technique are developed to make this possible. The classification and unmixing techniques developed will be applied to the problem of vegetation.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Van Dyck Dirk
• Co-promoter: Scheunders Paul
• Fellow: Tisson Greg

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This project aims at the optimisation of the methodology of the simultaneous determination of the electronic and atomic structure and the chemical composition and speciation of nano configurations in a single instrument. The required instrumentation is already available at EMAT (Electron Microscopy for Materials Research) within two different electron microscopes, but the optimal registration of the data and the interpretation of the results still require an integrated effort from various research areas. The above-mentioned nano-structural aspects are of utmost importance for the physical and chemical properties of many materials rendering this fundamental research project a gateway to several novel technological applications. The data sets that will be treated will be acquired using two high resolution transmission electron microscopes (HRTEM) (3000F ARP, CM30 UT), equipped with a field emission gun (FEG) and an electron energy loss spectrometer (EELS) with an energy filter (EF) and CCD detector. The first instrument also has a high resolution scanning transmission unit (STEM) and energy dispersive X-ray detector (EDX). Both instruments provide the possibility to work in nanoprobe, by which spectroscopic information of extremely small volumes can be obtained. The emphasis of the present project is on the optimisation of the acquiring and interpretation of EELS results in combination with other techniques. The fine structure of the spectra can give information on the chemical state and environment enabling the so-called speciation of the elements. In order to reach the extreme detail aimed for, the working conditions of both instruments have to be secured to minimise external influences. The possibility to record the information directly in a digital way via different detectors and CCD cameras implies a strongly enhanced and useful quantitative output. To make this project feasible and successful different domains of expertise available at the UA are brought together: HRTEM and EELS detection (EMAT) and interpretation with respect to chemical speciation (MiTAC) for materials science and the research of image and data improvement (Visielab). In the framework of this project three model systems will be investigated with the objective of optimising the performance of the various possibilities of the instrumentation. Apart from the structural characterisation, particular attention will be paid to the EELS methodology from which chemical as well as electronic information can be extracted. As a first system thin films of La1-xSr xMnO3 (CMR : Colossal Magnetic Resistance)-material in which various valency-states of Mn-ions can disintegrate on a nanometer scale will be examined. This demixing can only be visualised by the differences in the fine-structure of the EELS-spectra (ELNES) of the different states. A second system concerns diamond-like carbon films (DLC) produced by plasma enhanced chemical vapour deposition (PE-CVD). In these films one wants to discriminate the different types of carbon bonds depending on the plasma conditions. Again, especially ELNES will provide the necessary information. In the third model system nanoprecipitates in a known silicon or germanium matrix will be characterised. These precipitates are known from high resolution studies in EMAT but the structure and contents could up to now not unambiguously be determined because no chemical information, e.g. on the presence of oxygen, was available at the required nanoscale. Again the nanoprobe EELS-data should supply the required information for a thorough structure analysis including the speciation of the elements. In a later stage of the project the developed methods and acquired know-how will open possibilities for the study of nano-configurations and interfaces in materials which are more complex than the chosen model systems.

Researcher(s)

• Promoter: Schryvers Nick
• Co-promoter: Scheunders Paul
• Co-promoter: Van Espen Piet
• Co-promoter: Van Landuyt Joseph

Research team(s)

• Electron microscopy for materials research (EMAT)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In this project the aim is to develop new techniques for the visualization and classification of multispectral images. The ideas behind this come from the recent technological developments in the areas of remote sensing and multimodal medical imaging. The development will be done partly by extension of techniques, developed in the framework of color- and texture analysis, and partly by recent developments using non-linear projection techniques.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Broeckhove Jan
• Co-promoter: Arickx Frans
• Co-promoter: Scheunders Paul
• Co-promoter: Van Dyck Dirk

Research team(s)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Van Dyck Dirk
• Co-promoter: Scheunders Paul

Research team(s)

Project type(s)

• Research Project

Abstract

The aim is to expand existing clustering techniques by means of fuzzy techniques and genetic algorithms. These global clustering techniques are for instance suitable for segmentation of color images, with as result a better image quality than the existing techniques can yield.

Researcher(s)

• Promoter: Scheunders Paul

Research team(s)

Project type(s)

• Research Project