Nabil Ouerhani

• HES-SO Arc

Thermal error significantly impacts the machining precision of machine-tools. Thermal deformations in the machine-tool structure caused by the various machine heat sources is at the origin of this phenomenon. In order to ensure the expected quality of the parts, manufacturer have to run the machine-tools for hours before start producing in order to...

Next generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the key...

Musculo Skeletal Disorders (MSDs) is the most common disease in the workplaces causing disabilities and excessive costs to industries, particularly in EU countries. Most of MSDs prevention programs have focused on a combination of interventions including training to change individual behaviors (such as awkward postures). However, little evidence pr...

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on i...

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on i...

Due to real world physical constraints (e.g. walls), experimenting a virtual reality phenomenon implies transitional issues from one virtual environment (VE) to another. This paper proposes an experiment which studies the relevance of smooth and imperceptible transitions from a familiar and pleasurable virtual environment to a similar workplace as...

Internet of Things (IoT) seems a viable way to enable the Smart Cities of the future. iNUIT (Internet of Things for Urban Innovation) is a multi-year research program that aims to create an ecosystem that exploits the variety of data coming from multiple sensors and connected objects installed on the scale of a city, in order to meet specific needs...

This paper presents a real-world proven solution for dynamic street light control and management which relies on an open and flexible Internet of Things architecture. Substantial contribution is brought at the interoperability level using novel device connection concept based on model-driven communication agents to speed up the integration of senso...

This paper presents a novel approach towards dynamic street light control, which combines advanced Information and Communication Technologies (ICT) and citizens’ involvement and engagement. Our proposal is based on the Citizens’ involvement which would strongly increases the efficiency and performance of technological solutions in smart city contex...

In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. While several combination strategies are possible and the choice of a method influences the final saliency substantially, there is a real need for a performance comparison f...

In visual-based robot navigation, panoramic vision emerges as a very attractive candidate for solving the lo- calization task. Unfortunately, current systems rely on spe- cific feature selection processes that do not cover the re- quirements of general purpose robots. In order to fulfill new requirements of robot versatility and robustness to envir...

Saliency-based visual attention models provide visual saliency by combining the conspicuity maps relative to var-ious visual cues. Because the cues are of different nature, the maps to be combined show distinct dynamic ranges and a normalization scheme is therefore required. The normal-ization scheme used traditionally is an instantaneous peak-to-p...

In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. While several combination strategies are possible and the choice of a method influences the final saliency substantially, there is a real need for a performance comparison f...

Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene, on which higher level vision tasks, such as object recognition, can focus. The saliency-based model of visual attention represents one of the main attempts to simulate this visual mechanism on computers...

The aim of this study was to investigate how oculomotor behaviour depends on the availability of colour information in pictorial stimuli. Forty study participants viewed complex images in colour or grey-scale, while their eye movements were recorded. We found two major effects of colour. First, although colour increases the complexity of an image,...

This paper presents a robot self-localization method based on visual attention. This method takes advantage of the saliency-based model of attention to automatically learn configurations of salient visual landmarks along a robot path. During navigation, the visual attention algorithms detect a set of conspicuous visual features which are compared w...

Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene. The saliency-based model of visual attention is widely used to simulate this visual mechanism on computers. Though biologically inspired, this model has been only partially assessed in comparison with h...

This paper reports a landmark-based localization method relying on visual attention. In a learning phase, the multi-cue, multi-scale saliency-based model of visual attention is used to automatically acquire robust visual landmarks that are integrated into a topological map of the navigation environment. During navigation, the same visual attention...

Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment rep- resents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be usefu...

Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment represents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be useful...

The research described in this paper aims at assessing the contribution of depth to visual attention. It reports the measurement of depth induced human visual attention derived from fixation patterns and preliminary results of a quantitative comparison with visual attention as modeled by different versions of a computational model. More specificall...

Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer...

This paper reports a novel Multiscale Attention-based Pre-Segmentation method (MAPS) which is built around the multi-feature, multiscale, saliency-based model of visual attention. From the saliency map, provided by the attention algorithm, MAPS first derives the spatial locations of salient regions that will be considered further in the segmentatio...

Visual attention is the ability to rapidly detect the interest- ing parts of a given scene on which higher level computer vision tasks can focus. This paper reports a computational model of dynamic visual attention which combines static and dynamic features to detect salient locations in natural image sequences. Therefore, the model computes a map...

Visual attention is the ability to rapidly detect the visually salient parts of a given scene on which higher level vision tasks, such as object recognition, can focus. Found in biological vision, this mechanism represents a fundamental tool for computer vision. This paper reports the first real-time implementation of the complete visual attention...

Visual attention is the ability to rapidly detect the visually salient parts of a given scene. Inspired by biological vision, the saliency-based algorithm efficiently models the visual attention process. Due to its complexity, the saliency-based model of visual attention needs, for a real time implementation, higher computation resources than avail...

This paper reports a color image segmentation method based on a seeded region growing technique (SRG) and guided by a saliency-based visual attention algorithm. Inspired by biological vision the purely data-driven model of visual attention is built around the feature, con-spicuity and saliency maps. Using chromatic as well as unchromatic scene feat...

This paper reports an adaptive still color image compression method which produces automatically selected ROI with a higher reconstruction quality with respect to the rest of the input image. The ROI are generated on-the fly with a purely data-driven technique based on visual attention. Inspired from biological vision, the multicue visual attention...

The ”seeded region growing” (SRG) is a segmentation technique which performs an image segmentation with respect to a set of initial points, known as seeds. Given a set of seeds, SRG then grows the regions around each seed, based on the conventional region growing postulate of similarity of pixels within regions. The choice of the seeds is considere...

Visual attention is the ability to rapidly detect the interesting parts of a given scene. Inspired by biological vision, the principle of visual attention is used with a similar goal in computer vision. Several previous works deal with the computation of visual attention from images provided by standard video cameras, but little attention has been...

Visual Attention: From Bio-Inspired Modeling to Visual attention is the ability of a vision system, be it biological or artificial, to rapidly select the most salient and thus the most relevant data about the environment in which the system is operating. The main goal of this visual mechanism is to drastically reduce the amount of visual informatio...