Search results for "image processing"

A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders

2016

Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, which compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms that follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extra…

A label compression method for online multi-label classification

2018

Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally…

A comprehensive survey of multi-view video summarization

2021

[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-proce…

Enabling Retransmissions for Achieving Reliable Multicast Communications in WSNs

2016

To ensure end-to-end reliable multicast or broadcast transmissions in IEEE 802.15.4 based wireless sensor networks WSNs) is a challenging task since no retransmission and acknowledgment mechanisms are defined in such WSNs. In this paper, we propose three retransmission enabled multicast transmission schemes in order to achieve reliable packet transmissions in such networks. Different from the legacy CSMA/CA principle, these schemes allow a sending or forwarding node to retransmit a packet if necessary and enable implicit or/and explicit acknowledgment for multicast services. Simulations are performed in order to assess the performance of these schemes in terms of number of retransmissions, …

Deep 3D Convolution Neural Network for Alzheimer’s Detection

2020

One of the most well-known and complex applications of artificial intelligence (AI) is Alzheimer’s detection, which lies in the field of medical imaging. The complexity in this task lies in the three-dimensional structure of the MRI scan images. In this paper, we propose to use 3D Convolutional Neural Networks (3D-CNN) for Alzheimer’s detection. 3D-CNNs have been a popular choice for this task. The novelty in our paper lies in the fact that we use a deeper 3D-CNN consisting of 10 layers. Also, with effectively training our model consisting of Batch Normalization layers that provide a regularizing effect, we don’t have to use any transfer learning. We also use the simple data augmentation te…

Dimension enrichment with factual data during the design of multidimensional models: application to bird biodiversity

2015

20 pages; International audience; Data warehouses (DW) and OLAP systems are technologies allowing the on-line analysis of huge volume of data according to decision-makers’ needs. Designing DW involves taking into account functional requirements and data sources (mixed design methodology) [1]. But, for complex applications, existing automatic design methodologies seem inefficient. In some cases, decision-makers need querying, as a dimension, data which have been defined as facts by actual automatic mixed approachs. Therefore, in this paper, we offer a new mixed refinement methodology relevant to constellation multidimensional schema. The proposed methodolgy allows to decision-makers to enric…

Efficient High-Order Iterative Methods for Solving Nonlinear Systems and Their Application on Heat Conduction Problems

2017

[EN] For solving nonlinear systems of big size, such as those obtained by applying finite differences for approximating the solution of diffusion problem and heat conduction equations, three-step iterative methods with eighth-order local convergence are presented. The computational efficiency of the new methods is compared with those of some known ones, obtaining good conclusions, due to the particular structure of the iterative expression of the proposed methods. Numerical comparisons are made with the same existing methods, on standard nonlinear systems and a nonlinear one-dimensional heat conduction equation by transforming it in a nonlinear system by using finite differences. From these…

2018

Previous studies have reported that bright ceilings appear higher than dark ceilings, irrespective of the other colorimetric properties of the ceiling color (hue, saturation) and irrespective of the luminance of the remaining room surfaces (walls, floor). In the present study, we expand these findings to width and depth estimates. We presented stereoscopic full-scale room simulations on a head-mounted display and varied the luminance of the side walls, rear wall, and ceiling independently of each other. Participants judged the width and depth of the simulated rooms. Our results show that the perceived spatial layout of a given room is significantly influenced by the luminance of the direct …

2019

In this paper, we present a method for automated estimation of a human face given a skull remain. Our proposed method is based on three statistical models. A volumetric (tetrahedral) skull model encoding the variations of different skulls, a surface head model encoding the head variations, and a dense statistic of facial soft tissue thickness (FSTT). All data are automatically derived from computed tomography (CT) head scans and optical face scans. In order to obtain a proper dense FSTT statistic, we register a skull model to each skull extracted from a CT scan and determine the FSTT value for each vertex of the skull model towards the associated extracted skin surface. The FSTT values at p…

On Spatio-Temporal Saliency Detection in Videos using Multilinear PCA

2016

International audience; Visual saliency is an attention mechanism which helps to focus on regions of interest instead of processing the whole image or video data. Detecting salient objects in still images has been widely addressed in literature with several formulations and methods. However, visual saliency detection in videos has attracted little attention, although motion information is an important aspect of visual perception. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In this paper, we extend a recent saliency detection approach based on principal component analysis (PCA) which have shwon good results wh…