Search results for "optical flow"

showing 8 items of 48 documents

Optical flow estimation from multichannel spherical image decomposition

2011

The problem of optical flow estimation is largely discussed in computer vision domain for perspective images. It was also proven that, in terms of optical flow analysis from these images, we have difficulty distinguishing between some motion fields obtained with little camera motion. The omnidirectional cameras provided images with large filed of view. These images contain global information about motion and allow to remove the ambiguity present in perspective case. Nevertheless, these images contain significant radial distortions that is necessary to take into account when treating these images to estimate the motion. In this paper, we shall describe new way to compute efficient optical fl…

business.industryPerspective (graphical)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flowPhysics::OpticsMotion (geometry)Spherical imageImage (mathematics)WaveletComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligenceDecomposition method (constraint satisfaction)businessOmnidirectional antennaSoftwareMathematicsComputer Vision and Image Understanding
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Restoration and Enhancement of Historical Stereo Photos

2021

Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it …

image denoisingComputer sciencemedia_common.quotation_subjectNoise reductionComputer applications to medicine. Medical informaticsR858-859.7ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flow02 engineering and technologyimage restorationArticleoptical flowgradient filteringPhotography0202 electrical engineering electronic engineering information engineeringRedundancy (engineering)historical photosContrast (vision)Radiology Nuclear Medicine and imagingComputer visionimage enhancementElectrical and Electronic EngineeringTR1-1050stereo matchingImage restorationmedia_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniguided supersamplingImage fusionSettore INF/01 - Informaticabusiness.industry020206 networking & telecommunicationsSupersamplingQA75.5-76.95stacked medianComputer Graphics and Computer-Aided DesignTransmission (telecommunications)Electronic computers. Computer science020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessimage denoising image restoration image enhancement stereo matching optical flow gradient filtering stacked median guided supersampling historical photosJournal of Imaging
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Primary ciliary dyskinesia assessment by means of optical flow analysis of phase-contrast microscopy images

2014

Primary ciliary dyskinesia implies cilia with defective or total absence of motility, which may result in sinusitis, chronic bronchitis, bronchiectasis and male infertility. Diagnosis can be difficult and is based on an abnormal ciliary beat frequency (CBF) and beat pattern. In this paper, we present a method to determine CBF of isolated cells through the analysis of phase-contrast microscopy images, estimating cilia motion by means of an optical flow algorithm. After having analyzed 28 image sequences (14 with a normal beat pattern and 14 with a dyskinetic pattern), the normal group presented a CBF of 5.2 +/- 1.6 Hz, while the dyskinetic patients presented a 1.9 +/- 0.9 Hz CBF. The cutoff …

medicine.medical_specialtyChronic bronchitisPhase contrast microscopyOptical flowBeat (acoustics)Health InformaticsSensitivity and SpecificityPattern Recognition Automatedlaw.inventionTECNOLOGIA ELECTRONICAPrimary ciliary dyskinesialawOphthalmologyImage Interpretation Computer-AssistedMicroscopymedicineHumansMicroscopy Phase-ContrastRadiology Nuclear Medicine and imagingPrimary ciliary dyskinesiaMicroscopy VideoBronchiectasisRadiological and Ultrasound Technologybusiness.industryCiliumOptical flowActive contoursReproducibility of ResultsAnatomyImage Enhancementmedicine.diseaseComputer Graphics and Computer-Aided DesignCell TrackingSubtraction TechniqueFISICA APLICADABeat frequencyComputer Vision and Pattern RecognitionbusinessMATEMATICA APLICADAAlgorithmsFourier-Mellin transformCiliary Motility Disorders
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A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

2021

Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow veloci…

mmWave radarTK7800-8360Computer Networks and CommunicationsComputer scienceOptical flowComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlaw.inventionoptical flowData acquisitionautonomous systemslawComputer visionElectrical and Electronic EngineeringRadarvelocity estimationbusiness.industryFrame (networking)Object (computer science)Object detectionVDP::Teknologi: 500Hardware and ArchitectureControl and Systems EngineeringSignal ProcessingExtremely high frequencyRobotArtificial intelligenceElectronicsbusinessmonocular cameraElectronics
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Optical flow estimation from multichannel spherical image decomposition

2011

International audience; The problem of optical flow estimation is largely discussed in computer vision domain for perspective images. It was also proven that, in terms of optical flow analysis from these images, we have difficulty distinguishing between some motion fields obtained with little camera motion. The omnidirectional cameras provided images with large filed of view. These images contain global information about motion and allow to remove the ambiguity present in perspective case. Nevertheless, these images contain significant radial distortions that is necessary to take into account when treating these images to estimate the motion. In this paper, we shall describe new way to comp…

optical flowComputer Science::Computer Vision and Pattern Recognition[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Physics::OpticsOmnidirectional imagesspherical wavelets
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An Advanced Numerical Method for Recovering Image Velocity Vectors Field

2005

optical flowgraphical displaySettore MAT/08 - Analisi Numerica
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A Comparative Study of Block Matching Optical Flow Algorithms

2017

TEM Journal; Vol 6, No 4, 2017. ISSN 2217-8309

optical flowmotion estimationblock matchinglcsh:Tmotion estimation.lcsh:Llcsh:Technologycomparative studylcsh:Education
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Automatic dynamic texture segmentation using local descriptors and optical flow

2012

A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of orie…

ta113business.industrySegmentation-based object categorizationComputer scienceTexture DescriptorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flowScale-space segmentationPattern recognitionImage segmentationComputer Graphics and Computer-Aided DesignImage textureMotion fieldRegion growingComputer Science::Computer Vision and Pattern RecognitionHistogramComputer visionSegmentationArtificial intelligencebusinessSoftwareIEEE Transactions on Image Processing
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