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Cellular Image Classification


BackgroundClinical Problems: A Case Study on Autoimmune DiseasesCellular Imaging: A Case Study on Indirect ImmunofluorescenceComputer-Aided DiagnosisExperimental Datasets in the BookThe ICPR2012 DatasetThe ICIP2013 Training DatasetStructure of the ChaptersReferencesFundamentalsOptical Systems for Cellular ImagingLaser Scanning Confocal MicroscopeFluorescent Probe and Laser Scanning Confocal Microscopy Imaging TechniquesFundamentals of Confocal Laser Scanning MicroscopyCharacteristics of Confocal Laser Scanning MicroscopeApplication of Confocal Laser Scanning MicroscopeMulti-photon Fluorescence ImagingTotal Internal Reflection Fluorescence MicroscopeThe Principle of Total Internal ReflectionTotal Internal Reflection Fluorescence Microscopy (TIRFM)The Application of TIRFMNear-Field Scanning Optical Microscopy Imaging TechnologyWorking Principle of the Near FieldThe Characteristics and Classification of Near-Field Scanning Optical MicroscopeThe Application of Near-Field Scanning Optical MicroscopeOptical Coherence Tomography TechnologyFeature ExtractionLow-Level FeaturesLocal Binary PatternsScale Invariant Feature TransformScale-Space Extrema DetectionKeypoint LocalizationOrientation AssignmentKeypoint DescriptorMid-Level FeaturesClassificationSupport Vector MachineNearest Neighbor ClassifierReferencesOptical Systems for Cellular ImagingIntroductionOptical TweezerIntroduction to Optical TweezersGradient and Scattering Force of Optical TweezersThree-Dimensional Optical TrapLow-Order Fiber Mode LF2iFiber Mode Coupling TheoryAnalysis of Field Distribution in Optical FiberSolution to LP2i ModeSelective Excitation ofLP2l ModeThe Twisting and Bending Characteristics ofLP2 ModeWhy LP21 Mode?Optical Tweezer Using Focused LP2i ModeFiber AxiconsCell ManipulationModeling of Optical Trapping ForceForce Analysis of Mie Particles in Optical TrapGaussian BeamSimulation of Light Force on Mie ParticleSummaryReferencesImage Representation with Bag-of-WordsIntroductionCodingVector QuantizationSoft Assignment CodingLocality-Constrained Linear CodingPoolingSummaryReferencesImage CodingIntroductionLinear Local Distance Coding MethodDistance VectorLocal Distance VectorThe Algorithm FrameworkExperiments and AnalysesExperiment SetupExperimental Results on the ICPR2012 DatasetExperimental Results on the ICIP2013 Training DatasetDiscussionSummaryReferencesEncoding Image FeaturesIntroductionEncoding Rotation Invariant Features of ImagesPairwise LTPs with Spatial Rotation InvariantEncoding the SIFT Features with BoW FrameworkExperiments and AnalysesExperiment SetupExperimental Results on the ICPR2012 DatasetExperimental Results on the ICIP2013 Training DatasetDiscussionSummaryReferencesDefining Feature Space for Image ClassificationIntroductionAdaptive Co-occurrence Differential Texton Space for ClassificationCo-occurrence Differential TextonAdaptive CoDT Feature SpaceHEp-2 Cell Image Representation in the Adaptive CoDT Feature SpaceExperiments and AnalysesExperiment SetupExperimental Results on the ICPR2012 DatasetExperimental Results on the ICIP2013 Training DatasetDiscussionSummaryReferencesMajor Techniques Developed in the BookDirections and Future WorkReferences
 
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