Search results for "segmentation"

showing 10 items of 674 documents

Computerunterstützte Diagnostik in der Thoraxradiologie - aktuelle Schwerpunkte und Techniken

2003

The proliferation of digital data sets and the increasing amount of images, e. g. through the use of multislice spiral CT or multiple follow-up examinations in the context of new therapies, are ideal prerequisites for computer-aided diagnosis (CAD) in chest radiology. Multiple studies have described the applications and advantages of computer assistance in performing different diagnostic tasks. More powerful computers will enable the introduction of these systems into the clinical routine and could provide an enormous increase in morphological and functional information. The commercial introduction of tools for detection and visualization of pulmonary nodules has already begun. This is one …

business.industryFeature extractionContext (language use)Image processingCADMachine learningcomputer.software_genreVisualizationMedicineRadiology Nuclear Medicine and imagingSegmentationArtificial intelligenceMedical diagnosisNuclear medicinebusinesscomputerLung cancer screeningRöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren
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Statistical methods for texture analysis applied to agronomical images

2008

For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an averag…

business.industryFeature extractionPattern recognitionImage processingImage segmentationTexture (music)Class (biology)Image (mathematics)Image textureCluster validity indexComputer visionArtificial intelligencebusinessMathematicsImage Processing: Machine Vision Applications
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Content based segmentation of patterned wafers

2004

We extend our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer in- spection is based on the comparison of the same area on two neigh- boring dies. The dissimilarities between the images are a result of defects in this area of one of the dies. The noise level can vary from one structure to the other, within the same image. Therefore, seg- mentation is required to create a mask and apply an optimal thresh- old in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. We show a method to anticipate these variation…

business.industryMachine visionComputer scienceFeature extractionWavelet transformScale-space segmentationImage processingImage segmentationAtomic and Molecular Physics and OpticsComputer Science ApplicationsSegmentationComputer visionArtificial intelligenceElectrical and Electronic EngineeringPhotomaskbusinessJournal of Electronic Imaging
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Perceptually weighted optical flow for motion-based segmentation in MPEG-4 paradigm

2000

In the MPEG-4 paradigm, the sequence must be described in terms of meaningful objects. This meaningful, high-level representation should emerge from low-level primitives such as optical flow and prediction error which are the basic elements of previous-generation video coders. The accuracy of the high-level models strongly depends on the robustness of the primitives used. It is shown how perceptual weighting in optical flow computation gives rise to better motion estimates which consistently improve motion-based segmentation compared to equivalent unweighted motion estimates.

business.industryMean squared prediction errorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flowcomputer.file_formatPerceptual weightingOptical flow computationRobustness (computer science)Motion estimationComputer Science::MultimediaMPEG-4Computer visionSegmentationArtificial intelligenceElectrical and Electronic EngineeringbusinesscomputerMathematicsElectronics Letters
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Unsupervised clustering method for pattern recognition in IIF images

2017

Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy…

business.industryPattern recognitionIIfBiologyIIF imageSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)K-meanIdentification (information)Fluorescence intensityStatistical classificationPattern recognitionPattern recognition (psychology)Autoimmune diseaseAutomatic segmentationArtificial intelligenceUnsupervised clusteringCluster analysisbusinessclustering
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Location and characterization of the stem-calyx area on oranges by computer vision

1996

Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem becomes a skeleton. These techniques are able to determine the presence or absence of a stem with certainty, to locate the stems from random views with more than 90% accuracy and from profile images with an accuracy ranging from 92.4% to 100% depending on the method used. Finally, determination of the length and cutting point of the stem is achieved with only 3.8% of failures. (C) 1996 Silsoe Research …

business.industrySegmentationComputer visionRangingArtificial intelligenceAquatic ScienceCurvature analysisbusinessLinear discriminant analysisAnalysis methodCalyxMathematicsCitrus fruit
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Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

2016

Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…

business.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficientBroyden–Fletcher–Goldfarb–Shanno algorithmSegmentationArtificial intelligenceHidden Markov random fieldbusinessHidden Markov modelcomputerMathematicsProceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence
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Automatic detection of cervical cells in Pap-smear images using polar transform and k-means segmentation

2016

We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background or surrounding objects that are not part of the candidate cell. For assessing the natural number of clusters, the silhouette method is used. In the segmented polar representation, a number of parameters can be conveniently observed and evaluated as fuzzy memberships to the non-cell class, out of …

business.industryk-means clustering02 engineering and technologyImage segmentationElectronic mail030218 nuclear medicine & medical imagingSilhouette03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringCluster (physics)Polar020201 artificial intelligence & image processingSegmentationComputer visionArtificial intelligencebusinessCluster analysisMathematics2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
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Emotions in segmentation

2004

Abstract This paper presents an empirical study into tourist segmentation based on consumption emotions evoked by the enjoyment of leisure and tourism services. The research was conducted with 400 tourists visiting attractions. Taking as a basis the bidimensional approach to emotions—pleasure and arousal dimensions—the results provide support for the suitability of emotions as a segmentation variable. Those experiencing greater pleasure and arousal showed an increased level of satisfaction as well as more favorable behavioral intentions, meaning loyalty and willingness to pay more. Among the recommendations directed towards service organizations is the incorporation of affective variables i…

business.industrymedia_common.quotation_subjectDevelopmentPleasureMarket researchEmpirical researchMarket segmentationWillingness to payTourism Leisure and Hospitality ManagementLoyaltyPsychologybusinessSocial psychologyConsumer behaviourTourismmedia_commonAnnals of Tourism Research
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An Efficient Method for the Visualization of Spectral Images Based on a Perception-Oriented Spectrum Segmentation

2010

We propose a new method for the visualization of spectral images. It involves a perception-based spectrum segmentation using an adaptable thresholding of the stretched CIE standard observer colormatching functions. This allows for an underlying removal of irrelevant channels, and, consequently, an alleviation of the computational burden of further processings. Principal Components Analysis is then used in each of the three segments to extract the Red, Green and Blue primaries for final visualization. A comparison framework using two different datasets shows the efficiency of the proposed method.

business.industrymedia_common.quotation_subjectMultispectral imageSpectrum (functional analysis)ThresholdingIndependent component analysisVisualizationPerceptionPrincipal component analysisSegmentationComputer visionArtificial intelligencebusinessMathematicsmedia_common
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