81 research outputs found
Application of Multi-Sensor Fusion Technology in Target Detection and Recognition
Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems
Mineral identification using data-mining in hyperspectral infrared imagery
Les applications de lâimagerie infrarouge dans le domaine de la gĂ©ologie sont principalement des applications hyperspectrales. Elles permettent entre autre lâidentification minĂ©rale, la cartographie, ainsi que lâestimation de la portĂ©e. Le plus souvent, ces acquisitions sont rĂ©alisĂ©es in-situ soit Ă lâaide de capteurs aĂ©roportĂ©s, soit Ă lâaide de dispositifs portatifs. La dĂ©couverte de minĂ©raux indicateurs a permis dâamĂ©liorer grandement lâexploration minĂ©rale. Ceci est en partie dĂ» Ă lâutilisation dâinstruments portatifs. Dans ce contexte le dĂ©veloppement de systĂšmes automatisĂ©s permettrait dâaugmenter Ă la fois la qualitĂ© de lâexploration et la prĂ©cision de la dĂ©tection des indicateurs. Câest dans ce cadre que sâinscrit le travail menĂ© dans ce doctorat. Le sujet consistait en lâutilisation de mĂ©thodes dâapprentissage automatique appliquĂ©es Ă lâanalyse (au traitement) dâimages hyperspectrales prises dans les longueurs dâonde infrarouge. Lâobjectif recherchĂ© Ă©tant lâidentification de grains minĂ©raux de petites tailles utilisĂ©s comme indicateurs minĂ©ral -ogiques. Une application potentielle de cette recherche serait le dĂ©veloppement dâun outil logiciel dâassistance pour lâanalyse des Ă©chantillons lors de lâexploration minĂ©rale. Les expĂ©riences ont Ă©tĂ© menĂ©es en laboratoire dans la gamme relative Ă lâinfrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m Ă 11.8 m. Ces essais ont permis de proposer une mĂ©thode pour calculer lâannulation du continuum. La mĂ©thode utilisĂ©e lors de ces essais utilise la factorisation matricielle non nĂ©gative (NMF). En utlisant une factorisation du premier ordre on peut dĂ©duire le rayonnement de pĂ©nĂ©tration, lequel peut ensuite ĂȘtre comparĂ© et analysĂ© par rapport Ă dâautres mĂ©thodes plus communes. Lâanalyse des rĂ©sultats spectraux en comparaison avec plusieurs bibliothĂšques existantes de donnĂ©es a permis de mettre en Ă©vidence la suppression du continuum. Les expĂ©rience ayant menĂ©s Ă ce rĂ©sultat ont Ă©tĂ© conduites en utilisant une plaque Infragold ainsi quâun objectif macro LWIR. Lâidentification automatique de grains de diffĂ©rents matĂ©riaux tels que la pyrope, lâolivine et le quartz a commencĂ©. Lors dâune phase de comparaison entre des approches supervisĂ©es et non supervisĂ©es, cette derniĂšre sâest montrĂ©e plus appropriĂ© en raison du comportement indĂ©pendant par rapport Ă lâĂ©tape dâentraĂźnement. Afin de confirmer la qualitĂ© de ces rĂ©sultats quatre expĂ©riences ont Ă©tĂ© menĂ©es. Lors dâune premiĂšre expĂ©rience deux algorithmes ont Ă©tĂ© Ă©valuĂ©s pour application de regroupements en utilisant lâapproche FCC (False Colour Composite). Cet essai a permis dâobserver une vitesse de convergence, jusquâa vingt fois plus rapide, ainsi quâune efficacitĂ© significativement accrue concernant lâidentification en comparaison des rĂ©sultats de la littĂ©rature. Cependant des essais effectuĂ©s sur des donnĂ©es LWIR ont montrĂ© un manque de prĂ©diction de la surface du grain lorsque les grains Ă©taient irrĂ©guliers avec prĂ©sence dâagrĂ©gats minĂ©raux. La seconde expĂ©rience a consistĂ©, en une analyse quantitaive comparative entre deux bases de donnĂ©es de Ground Truth (GT), nommĂ©e rigid-GT et observed-GT (rigide-GT: Ă©tiquet manuel de la rĂ©gion, observĂ©e-GT:Ă©tiquetage manuel les pixels). La prĂ©cision des rĂ©sultats Ă©tait 1.5 fois meilleur lorsque lâon a utlisĂ© la base de donnĂ©es observed-GT que rigid-GT. Pour les deux derniĂšres epxĂ©rience, des donnĂ©es venant dâun MEB (Microscope Ălectronique Ă Balayage) ainsi que dâun microscopie Ă fluorescence (XRF) ont Ă©tĂ© ajoutĂ©es. Ces donnĂ©es ont permis dâintroduire des informations relatives tant aux agrĂ©gats minĂ©raux quâĂ la surface des grains. Les rĂ©sultats ont Ă©tĂ© comparĂ©s par des techniques dâidentification automatique des minĂ©raux, utilisant ArcGIS. Cette derniĂšre a montrĂ© une performance prometteuse quand Ă lâidentification automatique et Ă aussi Ă©tĂ© utilisĂ©e pour la GT de validation. Dans lâensemble, les quatre mĂ©thodes de cette thĂšse reprĂ©sentent des mĂ©thodologies bĂ©nĂ©fiques pour lâidentification des minĂ©raux. Ces mĂ©thodes prĂ©sentent lâavantage dâĂȘtre non-destructives, relativement prĂ©cises et dâavoir un faible coĂ»t en temps calcul ce qui pourrait les qualifier pour ĂȘtre utilisĂ©e dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7ÎŒm to 11.8ÎŒm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grainâs surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grainâs surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field
Dynamic Hyperspectral and Polarized Endoscopic Imaging
The health of rich, developed nations has seen drastic improvement
in the last two centuries. For it to continue improving at a similar
rate new or improved diagnostic and treatment technologies are required,
especially for those diseases such as cancer which are forecast
to constitute the majority of disease burden in the future. Optical
techniques such as microscopy have long played their part in the diagnostic
process. However there are several new biophotonic modalities
that aim to exploit various interactions between light and tissue to provide
enhanced diagnostic information. Many of these show promise in
a laboratory setting but few have progressed to a clinical setting. We
have designed and constructed a
flexible, multi-modal, multi-spectral
laparoscopic imaging system that could be used to demonstrate several
different techniques in a clinical setting.
The core of this system is a dynamic hyperspectral illumination system
based around a supercontinuum laser and Digital Micromirror
Device that can provide specified excitation light in the visible and
near infra-red ranges. This is a powerful tool for spectroscopic techniques
as it is not limited to interrogating a fixed range of wavelengths
and can switch between excitation bands instantaneously. The excitation
spectra can be customised to match particular
fluorophores or
absorption features, introducing new possibilities for spectral imaging.
A standard 10 mm diameter rigid endoscope was incorporated into
the system to reduce cost and demonstrate compatibility with existing
equipment. The polarization properties of two commercial endoscopes
were characterised and found to be unsuited to current polarization
imaging techniques as birefringent materials used in their construction introduce complex, spatially dependent transformations of the polarization
state. Preliminary exemplar data from phantoms and ex vivo
tissue was collected and the feasibility and accuracy of different analysis
techniques demonstrated including multiple class classification algorithms.
Finally, a novel visualisation method was implemented in
order to display the complex hyperspectral data sets in a meaningful
and intuitive way to the user
Automated Remote Sensing Image Interpretation with Limited Labeled Training Data
Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science.
Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month
Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System
Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting
High Performance Processing Algorithms of Satellite Measurements for Retrieval of Trace Gases and Cloud Properties
The latest generation of Earth observation satellites measure hyperspectral data using radiative transfer models (RTMs) to retrieve atmospheric composition and monitor the climate change. This dissertation focuses on the development of high performance processing algorithms of satellite measurements for retrieval of trace gases and cloud properties. The higher performance is assessed by developing new acceleration techniques for hyperspectral RTMs for future remote sensing applications
Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion
Laboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of their
research. The discussed topics ranging from computer vision and optical
metrology to network security and machine learning.
This volume provides a comprehensive and up-to-date overview of the research program of the IES
Laboratory and the Fraunhofer IOSB
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