12 research outputs found
WILDLIFE MULTISPECIES REMOTE SENSING USING VISIBLE AND THERMAL INFRARED IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)
Wildlife aerial surveys require time and significant resources. Multispecies detection could reduce costs to a single census for species
that coexist spatially. Traditional methods are demanding for observers in terms of concentration and are not adapted to multispecies
censuses. The processing of multispectral aerial imagery acquired from an unmanned aerial vehicle (UAV) represents a potential
solution for multispecies detection. The method used in this study is based on a multicriteria object-based image analysis applied on
visible and thermal infrared imagery acquired from a UAV. This project aimed to detect American bison, fallow deer, gray wolves,
and elks located in separate enclosures with a known number of individuals. Results showed that all bison and elks were detected
without errors, while for deer and wolves, 0–2 individuals per flight line were mistaken with ground elements or undetected. This
approach also detected simultaneously and separately the four targeted species even in the presence of other untargeted ones. These
results confirm the potential of multispectral imagery acquired from UAV for wildlife census. Its operational application remains
limited to small areas related to the current regulations and available technology. Standardization of the workflow will help to reduce
time and expertise requirements for such technology
UAV-BASED POINT CLOUD GENERATION FOR OPEN-PIT MINE MODELLING
Along with the advancement of unmanned aerial vehicles (UAVs), improvement of high-resolution cameras and development of
vision-based mapping techniques, unmanned aerial imagery has become a matter of remarkable interest among researchers and
industries. These images have the potential to provide data with unprecedented spatial and temporal resolution for three-dimensional
(3D) modelling. In this paper, we present our theoretical and technical experiments regarding the development, implementation and
evaluation of a UAV-based photogrammetric system for precise 3D modelling. This system was preliminarily evaluated for the
application of gravel-pit surveying. The hardware of the system includes an electric powered helicopter, a 16-megapixels visible
camera and inertial navigation system. The software of the system consists of the in-house programs built for sensor calibration,
platform calibration, system integration and flight planning. It also includes the algorithms developed for structure from motion
(SfM) computation including sparse matching, motion estimation, bundle adjustment and dense matching
REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING
Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital
cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to
serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be
considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of
efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity
hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity
in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy
function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each
pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the
proposed framework could achieve high levels of accuracy and efficiency
ROBUST SPARSE MATCHING AND MOTION ESTIMATION USING GENETIC ALGORITHMS
In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters
from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational
efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of
initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search
with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being
performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum,
however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective
camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the
estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the
uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic
data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC,
MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach
perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency
Vibrational Signatures of Calcium Oxalate Polyhydrates
International audienceThe vibrational signatures of the calcium oxalate polyhydrates are investigated using a combination of Density Functional Theory-Dispersion corrected, Fourier Transform-Raman and-Infrared (IR) spectroscopies. Most vibrational bands were assigned and the theoretical predictions were compared with in-house and other experimental data, for both, IR and Raman spectroscopies. Such an approach allowed a more accurate analysis of vibrational spectra helping in the completion of the band assignments of the vibrational bands of the mono, di, and tri hydrate calcium oxalate (COM, COD, and COT). Particular attention has been paid to the degree of hydration of COD, the low Raman wavenumbers, and the presence of oxalic acid in natural calcium oxalate polyhydrates. The obtained results are expected to be supportive in the detection of the different polyhydrates in natural samples, such as in kidney stones