17 research outputs found
Long-range UAV Thermal Geo-localization with Satellite Imagery
Onboard sensors, such as cameras and thermal sensors, have emerged as
effective alternatives to Global Positioning System (GPS) for geo-localization
in Unmanned Aerial Vehicle (UAV) navigation. Since GPS can suffer from signal
loss and spoofing problems, researchers have explored camera-based techniques
such as Visual Geo-localization (VG) using satellite RGB imagery. Additionally,
thermal geo-localization (TG) has become crucial for long-range UAV flights in
low-illumination environments. This paper proposes a novel thermal
geo-localization framework using satellite RGB imagery, which includes multiple
domain adaptation methods to address the limited availability of paired thermal
and satellite images. The experimental results demonstrate the effectiveness of
the proposed approach in achieving reliable thermal geo-localization
performance, even in thermal images with indistinct self-similar features. We
evaluate our approach on real data collected onboard a UAV. We also release the
code and \textit{Boson-nighttime}, a dataset of paired satellite-thermal and
unpaired satellite images for thermal geo-localization with satellite imagery.
To the best of our knowledge, this work is the first to propose a thermal
geo-localization method using satellite RGB imagery in long-range flights.Comment: 8 pages, 6 figures, IROS 202
Model-Based Underwater 6D Pose Estimation from RGB
Object pose estimation underwater allows an autonomous system to perform
tracking and intervention tasks. Nonetheless, underwater target pose estimation
is remarkably challenging due to, among many factors, limited visibility, light
scattering, cluttered environments, and constantly varying water conditions. An
approach is to employ sonar or laser sensing to acquire 3D data, but besides
being costly, the resulting data is normally noisy. For this reason, the
community has focused on extracting pose estimates from RGB input. However, the
literature is scarce and exhibits low detection accuracy. In this work, we
propose an approach consisting of a 2D object detection and a 6D pose
estimation that reliably obtains object poses in different underwater
scenarios. To test our pipeline, we collect and make available a dataset of 4
objects in 10 different real scenes with annotations for object detection and
pose estimation. We test our proposal in real and synthetic settings and
compare its performance with similar end-to-end methodologies for 6D object
pose estimation. Our dataset contains some challenging objects with symmetrical
shapes and poor texture. Regardless of such object characteristics, our
proposed method outperforms stat-of-the-art pose accuracy by ~8%. We finally
demonstrate the reliability of our pose estimation pipeline by doing
experiments with an underwater manipulation in a reaching task.Comment: Under RA-L Submissio
Automated detection of lupus white matter lesions in MRI
Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration
Automated methods on magnetic resonance brain imaging in multiple sclerosis
In this thesis, we have focused on the image pre-processing in order to enhance the image information. The main aspects of this enhancement rely on removing any image noise and correcting any intensity bias induced by the scanner. Besides, we also contributed with a new technique based on a multispectral, adaptive, region growing algorithm in order to segment the brain from the rest of the head.
We include, as a pre-processing step, the image registration process, in which we proposed a novel pipeline by using information from multiple modalities to improve the results of this process.
Furthermore, we have also studied the current techniques for the detection and segmentation of WML, proposing a new method based on a previous proposal. Therefore, we presented a tool able to automatically detect and segment WML of Multiple sclerosis and Lupus patients.En aquesta tesi ens centrem, per una part, en el pre-processat de la imatge per tal d'eliminar el soroll i corregir les inhomogeneïtats en les intensitats, ambdós errors introduïts per l'escàner. A més hem contribuït també amb una nova tècnica basada en un algoritme de “región growing” per tal de segmentar el cervell de dins de tota la imatge del cap.
Incloem com a pre-processat el registre d'imatges, on hem proposat una “pipeline" mitjançant la informació de múltiples modalitats per tal de millorar els resultats d'aquest procés.
Per altra banda, hem estudiat també les tècniques actuals de detecció i segmentació de lesions en la matèria blanca, proposant un mètode nou basat en anteriors propostes. Així doncs, presentem una eina automàtica capaç de detectar i segmentar lesions en la matèria blanca de pacients d'Esclerosi Múltiple i Lupus
Automated methods on magnetic resonance brain imaging in multiple sclerosis
In this thesis, we have focused on the image pre-processing in order to enhance the image information. The main aspects of this enhancement rely on removing any image noise and correcting any intensity bias induced by the scanner. Besides, we also contributed with a new technique based on a multispectral, adaptive, region growing algorithm in order to segment the brain from the rest of the head.
We include, as a pre-processing step, the image registration process, in which we proposed a novel pipeline by using information from multiple modalities to improve the results of this process.
Furthermore, we have also studied the current techniques for the detection and segmentation of WML, proposing a new method based on a previous proposal. Therefore, we presented a tool able to automatically detect and segment WML of Multiple sclerosis and Lupus patients.En aquesta tesi ens centrem, per una part, en el pre-processat de la imatge per tal d'eliminar el soroll i corregir les inhomogeneïtats en les intensitats, ambdós errors introduïts per l'escàner. A més hem contribuït també amb una nova tècnica basada en un algoritme de “región growing” per tal de segmentar el cervell de dins de tota la imatge del cap.
Incloem com a pre-processat el registre d'imatges, on hem proposat una “pipeline" mitjançant la informació de múltiples modalitats per tal de millorar els resultats d'aquest procés.
Per altra banda, hem estudiat també les tècniques actuals de detecció i segmentació de lesions en la matèria blanca, proposant un mètode nou basat en anteriors propostes. Així doncs, presentem una eina automàtica capaç de detectar i segmentar lesions en la matèria blanca de pacients d'Esclerosi Múltiple i Lupus
Reconeixement de cd-roms musicals mitjançant visió per computador
Dissenyar, implementar i testejar un sistema per classificar imatges: disseny d’un sistema que primer aprèn com són les imatges d’una classe a partir d’un conjunt d’imatges d’entrenament i després és capaç de classificar noves imatges assignant-les-hi l’ etiqueta corresponent a una de les classes “apreses”. Concretament s’analitzen caràtules de cd-roms, les quals s’han de reconèixer per després reproduir automàticament la música del seu àlbum associa
Underwater Object Classification and Detection: first results and open challenges
This work reviews the problem of object detection in underwater environments.
We analyse and quantify the shortcomings of conventional state-of-the-art
(SOTA) algorithms in the computer vision community when applied to this
challenging environment, as well as providing insights and general guidelines
for future research efforts. First, we assessed if pretraining with the
conventional ImageNet is beneficial when the object detector needs to be
applied to environments that may be characterised by a different feature
distribution. We then investigate whether two-stage detectors yields to better
performance with respect to single-stage detectors, in terms of accuracy,
intersection of union (IoU), floating operation per second (FLOPS), and
inference time. Finally, we assessed the generalisation capability of each
model to a lower quality dataset to simulate performance on a real scenario, in
which harsher conditions ought to be expected. Our experimental results provide
evidence that underwater object detection requires searching for "ad-hoc"
architectures than merely training SOTA architectures on new data, and that
pretraining is not beneficial
Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury
Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments.status: publishe
Automatic quantification of computed tomography features in acute traumatic brain injury
Automated detection of Lupus white matter lesions in MRI
Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative and quantitative results in terms of precision and sensitivity of lesion detection (True Positive Rate (62%) and Positive Prediction Value (80%) respectively) as well as segmentation accuracy (Dice Similarity Coefficient (72%)). Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration