Search results for "Names"
showing 10 items of 6843 documents
The iterative object symmetry transform
2005
This paper introduces a new operator named the Iterated Object Transform that is computed by combining the Object Symmetry Transform with the morphological operator erosion. This new operator has been applied on both binary and gray levels images showing the ability to grasp the internal structure of a digital object. We present some experiments on real images in face analysis.
Machine Learning Methods for Spatial and Temporal Parameter Estimation
2020
Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…
Classification of Chitinozoa (Llandoverian, Canada) Using Image Analysis
1996
Chitinozoa (Llandoverian, Canada) were studied using image analysis. After digitalization of the objects, shape parameters were calculated. The boundary of each fossil was then traced by a vector centred at the centroid for Fast Fourier Transform (FFT). Results of the two methods were used as variables in a hierarchical cluster analysis in order to group the samples. These results show that Chitinozoa can be significantly classified in terms of taxa using independent shape parameters obtained by image analysis.
Noise Robustness Analysis of Point Cloud Descriptors
2013
In this paper, we investigate the effect of noise on 3D point cloud descriptors. Various types of point cloud descriptors have been introduced in the recent years due to advances in computing power, which makes processing point cloud data more feasible. Most of these descriptors describe the orientation difference between pairs of 3D points in the object and represent these differences in a histogram. Earlier studies dealt with the performances of different point cloud descriptors; however, no study has ever discussed the effect of noise on the descriptors performances. This paper presents a comparison of performance for nine different local and global descriptors amidst 10 varying levels o…
A measurement-based trajectory model for drifted motions towards a target zone
2016
Trajectory models have numerous applications in the area of wiewlwss communications. The aim of this paper is to develop an empirical trajectory model for drifted motions. Recently, a highly flexible trajectory model based on the primitives of Brownian fields (TramBrown) was proposed by A. Borhani and M. Patzold. This paper provides an empirical proof for TramBrown using global positioning system (GPS) data collected from real life user traces drifting to a particular target point or a zone. The recorded location coordinates of the mobile user are processed to compute the total travelling length and the angle-of-motion (AOM) along the drifted trajectory. It is shown that the probability den…
The influence of nimodipine on chromatolysis of motoneurons following axotomy with and without reinnervation: A quantitative image analysis
1996
Using a recently developed image analysis method to quantify the time course of chromatolysis in injured motorneurons we tested the effect of the calcium entry blocker nimodipine (1000 ppm in food pellets) on regenerating and degenerating motoneurons. Following facial-facial, hypoglossal-hypoglossal anastomosis with complete regeneration and following facial and hypoglossal nerve resection which causes a partial neuronal degeneration and postoperative survival times of 4 to 112 days, the texture of the Nissl substance of facial and hypoglossal motoneurons was analyzed on both sides of the brainstem in paraffin serial sections with a VIDASplus image analyzer. Monitoring alterations of the Ni…
A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks
2014
We consider a scenario where multiple Secondary Users SUs operate within a Cognitive Radio Network CRN which involves a set of channels, where each channel is associated with a Primary User PU. We investigate two channel access strategies for SU transmissions. In the first strategy, the SUs will send a packet directly without operating Carrier Sensing Medium Access/Collision Avoidance CSMA/CA whenever a PU is absent in the selected channel. In the second strategy, the SUs implement CSMA/CA to further reduce the probability of collisions among co-channel SUs. For each strategy, the channel selection problem is formulated and demonstrated to be a so-called "Potential" game, and a Bayesian Lea…
Towards interpretable classifiers with blind signal separation
2012
Blind signal separation (BSS) is a powerful tool to open-up complex signals into component sources that are often interpretable. However, BSS methods are generally unsupervised, therefore the assignment of class membership from the elements of the mixing matrix may be sub-optimal. This paper proposes a three-stage approach using Fisher information metric to define a natural metric for the data, from which a Euclidean approximation can then be used to drive BSS. Results with synthetic data models of real-world high-dimensional data show that the classification accuracy of the method is good for challenging problems, while retaining interpretability.
An algorithm for earthquakes clustering based on maximum likelihood
2007
In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…
Hospitalization for self-harm during the early months of the COVID-19 pandemic in France: A nationwide retrospective observational cohort study
2021
ABSTRACT Background Little is known to date about the impact of COVID-19 pandemic on self-harm. Methods The number of hospitalizations for self-harm (ICD-10 codes X60-X84) in France from 1st January to 31st August 2020 (including a two-month confinement) was compared to the same periods in 2017–2019. Statistical methods comprised Poisson regression, Cox regression and Student's t-test, plus Spearman's correlation test relating to spatial analysis of hospitalizations. Outcomes There were 53,583 self-harm hospitalizations in France during January to August 2020. Compared to the same period in 2019, this represents an overall 8·5% decrease (Relative Risk [95% Confidence Interval] = 0·91 [0·90–…