Image segmentation
Histogram-based approaches can also be quickly adapted to apply to multiple frames, while maintaining their single pass efficiency.The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable.The histogram can also be applied on a per-pixel basis where the resulting information is used to determine the most frequent color for the pixel location.[28][29] Spatial-taxons[30] are information granules,[31] consisting of a crisp pixel region, stationed at abstraction levels within a hierarchical nested scene architecture.When applying these concepts to actual images represented as arrays of numbers, we need to consider what happens when we reach an edge or border region.The method of Statistical Region Merging[38] (SRM) starts by building the graph of pixels using 4-connectedness with edges weighted by the absolute value of the intensity difference.The central idea is to evolve an initial curve towards the lowest potential of a cost function, where its definition reflects the task to be addressed.Lagrangian techniques are based on parameterizing the contour according to some sampling strategy and then evolving each element according to image and internal terms.In both cases, energy minimization is generally conducted using a steepest-gradient descent, whereby derivatives are computed using, e.g., finite differences.The level-set method was initially proposed to track moving interfaces by Dervieux and Thomasset[44][45] in 1979 and 1981 and was later reinvented by Osher and Sethian in 1988.The level-set method affords numerous advantages: it is implicit, is parameter-free, provides a direct way to estimate the geometric properties of the evolving structure, allows for change of topology, and is intrinsic.[50] An important generalization is the Mumford-Shah model[51] given by The functional value is the sum of the total length of the segmentation curveThe criterion for image segmentation using MRFs is restated as finding the labelling scheme which has maximum probability for a given set of features.A major issue with ICM is that, similar to gradient descent, it has a tendency to rest over local maxima and thus not obtain a globally optimal labeling scheme.Simulated annealing requires the input of temperature schedules which directly affects the speed of convergence of the system, as well as energy threshold for minimization to occur.Apart from likelihood estimates, graph-cut using maximum flow[60] and other highly constrained graph based methods[61][62] exist for solving MRFs.Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines, which represent the region boundaries.[63] Such a task may involve (i) registration of the training examples to a common pose, (ii) probabilistic representation of the variation of the registered samples, and (iii) statistical inference between the model and the image.There have been numerous research works in this area, out of which a few have now reached a state where they can be applied either with interactive manual intervention (usually with application to medical imaging) or fully automatically.The nesting structure that Witkin described is, however, specific for one-dimensional signals and does not trivially transfer to higher-dimensional images.Koenderink[66] proposed to study how iso-intensity contours evolve over scales and this approach was investigated in more detail by Lifshitz and Pizer.Gauch and Pizer[70] studied the complementary problem of ridges and valleys at multiple scales and developed a tool for interactive image segmentation based on multi-scale watersheds.[72] Vincken et al.[73] proposed a hyperstack for defining probabilistic relations between image structures at different scales.[77] Bijaoui and Rué[78] associate structures detected in scale-space above a minimum noise threshold into an object tree which spans multiple scales and corresponds to a kind of feature in the original signal.In one kind of segmentation, the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.In an alternative kind of semi-automatic segmentation, the algorithms return a spatial-taxon (i.e. foreground, object-group, object or object-part) selected by the user or designated via prior probabilities.The Eckhorn model provided a simple and effective tool for studying the visual cortex of small mammals, and was soon recognized as having significant application potential in image processing.The decoder structure utilizes transposed convolution layers for upsampling so that the end dimensions are close to that of the input image.[20] Related images such as a photo album or a sequence of video frames often contain semantically similar objects and scenes, therefore it is often beneficial to exploit such correlations.[84] The task of simultaneously segmenting scenes from related images or video frames is termed co-segmentation,[16] which is typically used in human action localization.
– blue : pulmonary arteries
– red : pulmonary veins (and also the abdominal wall )
– yellow : the mediastinum
– violet : the diaphragm
digital image processingcomputer visiondigital imagepixelsboundariescontoursedge detectionintensitytexturemedical imaging3D reconstructionsmarching cubesCT scanthoraxpulmonary arteriespulmonary veinsabdominal wallmediastinumdiaphragmContent-based image retrievalMachine visionvolume renderedcomputed tomographymagnetic resonance imagingObject detectionPedestrian detectionFace detectionFace recognitionFingerprint recognitionIris recognitionAirport securityVideo surveillanceVideo object co-segmentation and action localizationalgorithmsThresholding (image processing)thresholdingbalanced histogram thresholdingOtsu's methodk-means clusteringData clusteringK-means algorithmiterativepartition an imagealgorithmrandomlyheuristicK-means++distanceoptimalMean Shiftaprioririgid motion segmentationHuffman codingchain codelossy compressionminimum description lengthentropymultivariate normal distributionHistogramclustersrecursivelyvideo trackingGestaltRegion-growingStatistical Region Mergingmeasure of similarityHaralickintensitiesscatterlambda-connectednessSplit-and-merge segmentationquadtreepartial differential equationinverse problemsLagrangianWitkinTerzopoulossnakeslevel-set methodlevel-set data structuresfast marching methodPotts modelMumford-Shah modelgraduated non-convexityAmbrosio-Tortorelli approximationundirected graphSegmentation-based object categorizationrandom walkerminimum spanning tree-based segmentationMarkov random fieldscliquesmaximum a posteriori estimationBayes' theoremCliquelog likelihooditerated conditional modessimulated annealingexpectation–maximization algorithmconditional probabilitywatershed transformationactive shape modelsactive appearance modelsscale spacescale-space segmentationLivewiredomain knowledgeneural networkKohonen mapPulse-coupled neural networks (PCNNs)biomimeticimage processingconvolutional neural networkautoencoderObject co-segmentationco-segmentationhuman action localizationbounding boxMarkov Networksmultispectral segmentationImage-based meshingRange image segmentationVector quantizationImage quantizationColor quantizationObject-based image analysisList of manual image annotation tools