Search results for "GEP"

showing 10 items of 1017 documents

A real-time webcam based Eye Ball Tracking System using MATLAB

2015

Eye Ball Tracking System is a device which is intended to assist patients that cannot perform any voluntary tasks related to daily life. Patients who only can control their eyes can still communicate with the real-world using the assistive devices like one proposed in this paper. This device provides a human computer interface in order to take decisions based on their eye movement. A real time data stream is captured via webcam that transfers data serially to MATLAB. Then a sequential image processing scheme segments the iris of the eye and calculates the centroid, thereby generating control signal with the help of a reference axis. The control signals are then used to manipulate the positi…

Scheme (programming language)Computer sciencebusiness.industryInterface (computing)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONEye movementCentroidImage processingMicrocontrollerComputer visionReal-time dataArtificial intelligenceMATLABbusinesscomputercomputer.programming_language2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging (SIITME)
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Use of balanced detection in single-pixel imaging

2016

We introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel architecture. With this novel detection scheme we are able to recover a real-time video stream in presence of ambient light.

Scheme (programming language)EngineeringLight intensitybusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONinitComputer visionArtificial intelligencebusinesscomputerSingle pixelcomputer.programming_languageImaging and Applied Optics 2016
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Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

2020

Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…

SegTHOR0209 industrial biotechnologyGeneral Computer ScienceComputer scienceFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyConvolutional neural network020901 industrial engineering & automationPyramid0202 electrical engineering electronic engineering information engineeringMedical imagingGeneral Materials ScienceSegmentationPyramid (image processing)3D deep learning modelsPixelbusiness.industryDeep learningGeneral EngineeringPattern recognition3D-atrous spatial pyramid pooling (ASPP)Feature (computer vision)3D volumetric segmentation020201 artificial intelligence & image processinglcsh:Electrical engineering. Electronics. Nuclear engineeringArtificial intelligencebusinesslcsh:TK1-9971IEEE Access
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Chaotic multiagent system approach for MRF-based image segmentation

2005

In this paper, we propose a new chaotic approach for image segmentation based on multiagent system (MAS). We consider a set of segmentation agents organized around a coordinator agent. Each segmentation agent performs iterated conditional modes (ICM) starting from its own initial image created using a chaotic mapping. The coordinator agent diversifies the initial images using a crossover and a chaotic mutation operators. The efficiency of our chaotic MAS approach is shown through some experimental results.

Segmentation-based object categorizationbusiness.industryComputer scienceMulti-agent systemCrossoverComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONChaoticScale-space segmentationImage segmentationComputingMethodologies_ARTIFICIALINTELLIGENCENonlinear Sciences::Chaotic DynamicsComputer Science::Multiagent SystemsComputerSystemsOrganization_MISCELLANEOUSComputer Science::Computer Vision and Pattern RecognitionIterated conditional modesSegmentationComputer visionArtificial intelligencebusinessISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005.
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Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

2016

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…

Segmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technologyImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficient0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligenceHidden Markov random fieldHidden Markov modelbusinesscomputerMathematicsProceedings of the 5th International Conference on Pattern Recognition Applications and Methods
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Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments

2021

Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user’s requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to …

Seguretat viàriasimulatorsChemical technologyhybrid gravito-inertial cues; motion cueing algorithms; motion platform; Virtual Reality (VR); simulators; road environmentsAccelerationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONroad environmentsTP1-1185BiochemistryVirtual Reality (VR)Atomic and Molecular Physics and OpticsArticleAnalytical ChemistryAutomationUser-Computer Interfacemotion platformhybrid gravito-inertial cuesComputer SimulationElectrical and Electronic EngineeringCuesInstrumentationmotion cueing algorithmsSensors
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A New SOM Initialization Algorithm for Nonvectorial Data

2008

Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms…

Self-organizing mapComputer sciencebusiness.industryQuantization (signal processing)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONInitializationMedian SOM initialization pairwise dataPattern recognitionMatrix (mathematics)ComputingMethodologies_PATTERNRECOGNITIONArtificial intelligenceCluster analysisbusinessAlgorithm
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Analysis of Multi-Choice Questionnaires through Self-Organizing Maps

1998

This paper describes how Self-Organizing Maps can be used to analyse multi-choice gallups. In this method, the use of a single SOM for all available data is replaced with the use of multiple SOMs trained with subsets of gallup questions. The subgroupings located from these maps are then used to train a new concluding SOM that is more readable than any single SOM analysis would be.

Self-organizing mapComputingMethodologies_PATTERNRECOGNITIONInformation retrievalComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
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Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering

2011

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…

Self-organizing mapGround truthPixelSettore INF/01 - Informaticabusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionFuzzy logicComputer visionSegmentationArtificial intelligenceCluster analysisbusinessHill climbingRetinal Vessels Self-Organizing Map Fuzzy C-Means.
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Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering

2011

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…

Self-organizing mapGround truthSettore INF/01 - InformaticaPixelbusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONk-means clusteringScale-space segmentationPattern recognitionRetinal vessels Self-Organizing Map K-MeansSegmentationComputer visionArtificial intelligenceCluster analysisbusinessHill climbing
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