Search results for " image processing."

SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

2018

This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of class discovery using SOM-based clustering. It is then concluded that by eval…

CrowdVAS-Net: A Deep-CNN Based Framework to Detect Abnormal Crowd-Motion Behavior in Videos for Predicting Crowd Disaster

2019

With the increased occurrences of crowd disasters like human stampedes, crowd management and their safety during mass gathering events like concerts, congregation or political rally, etc., are vital tasks for the security personnel. In this paper, we propose a framework named as CrowdVAS-Net for crowd-motion analysis that considers velocity, acceleration and saliency features in the video frames of a moving crowd. CrowdVAS-Net relies on a deep convolutional neural network (DCNN) for extracting motion and appearance feature representations from the video frames that help us in classifying the crowd-motion behavior as abnormal or normal from a short video clip. These feature representations a…

Comprehensive Experimental Analysis of Handcrafted Descriptors for Face Recognition

2018

Over the past few decades, LBP descriptor, which shown its high robustness in extracting discriminative features from an image, has been successfully applied in diverse challenging computer vision applications including face recognition. The efficiency and usability of the LBP operator and its success in various real world applications has inspired the development of much new powerful LBP variants. Indeed, after the appearance of the LBP operator, several renowned extensions and modifications of LBP have been proposed in the literature to the point that it can be difficult to recognize their respective LBP-related strategies, strengths and weaknesses according to a given application, and th…

Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features

2018

International audience; Many fields of application could benefit from an accurate registration of measurements of different modalities over a known 3D model. However, aligning a 2D image to a 3D model is a challenging task and is even more complex when the two have a different modality. Most of the 2D/3D registration methods are based on either geometric or dense visual features. Both have their own advantages and their own drawbacks. We propose, in this paper, to mutually exploit the advantages of one feature type to reduce the drawbacks of the other one. For this, an hybrid registration framework has been designed to mutually align geometrical and dense visual features in order to obtain …

Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems

2017

Review of machine to machine communication in smart grid

2016

Machine to machine communication (M2M) is a communication architecture that enables heterogeneous devices to interact with each other without human intervention. Smart Grid (SG) is one of the many applications areas in the M2M communication. Smart Grid demands advanced communication infrastructure for two-way communications between devices deployed at various locations in energy generation, transmission, distribution and consumption. The billions of electronic devices connected to the Smart Grid pose a big challenge to grid communication. Therefore, a feasible solution to efficient M2M has to overcome challenges of energy efficiency of connected devices, interoperability, coverage area, int…

Estimation of the Velocity of a Walking Person in Indoor Environments from mmWave Signals

2018

The present work is motivated by the growing interest in using millimeter-wave (mmWave) bands in future wireless indoor communications. For a variety of wireless indoor applications, such as remote medical care, healthcare services, and human-machine interaction, it is of crucial importance to estimate the velocity of walking persons in indoor environments with high precision. In this paper, we present a novel procedure for estimating the velocity of a walking person in indoor environments by using mmWave signals. The indoor environment is considered to be equipped with a distributed $2\times 2$ multiple-input multiple-output (MIMO) system operating in the 60 GHz band. The proposed approach…

Improved locally adaptive least-squares detection of differences in images

2007

We introduce a method for change detection under nonuniform changes of intensity using an improved least-squares method. A locally adaptive normalizing window is correlated with the two images, and a morphological postprocessing is then applied to isolate objects that have been added or removed from the scene. We use a modification of the least-squares solution to get rid of clutter caused by intensity changes that do not satisfy the model assumed for the least-squares solution.

Supporting Autonomy in Agent Oriented Methodologies

2016

Designing a software solution for a complex systems is always a demanding task, it becomes much more complex if we consider to design a multi agent system where agents have to exhibit autonomy; which abstractions and which concepts to take into consideration when using a design methodology we would like to support autonomy? In this paper, we answer this question by studying and analyzing literature on the concept of agents in order to establish the basic set of concepts an agent oriented methodology has to deal with.

A Windowing strategy for Distributed Data Mining optimized through GPUs

2017

Abstract This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree; and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels; which is paid in terms of time performance. Our proposed enhancements…