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2014CVPR source code
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Application background Automatic detection of the common case of the dark spot algorithm, and use the spot as the fuzzy kernel (blur kernel) constraints. It to solve the dark fuzzy picture is very effective and spot this phenomenon not only in low light, in the ordinary fuzzy image will also appear, only need scene is presented with the surrounding environment with different colors of small objects. Readers can try to download the code on my personal homepage. There is an article is from a blurred image, we can not only estimate the camera jitter and can also send disinter scene depth (joint depth estimation and camera shake removal from single). This may sound like an impossible task, but in fact, the blur of images is the same as the camera shake and scene depth information. Readers can also think so, we can take the process of shooting the fuzzy image can be seen as a short video process, so that we are equivalent to have a multi angle three-dimensional (stereo)
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Pyramid word bag model Matlab code.Zip
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Application background This code is about Bags of Features: Spatial Pyramid; Matching for Recognizing Natural Scene Categories Beyond, the main is to calculate the SIFT code, and then the formation of the Pyramid of the bag, for image classification, video retrieval can be applied. Key Technology Thispaperpresentsamethodforrecognizingscenecat-egoriesbasedonapproximateglobalgeometriccorrespon-dence.Thistechniqueworksbypartitioningtheimageintoincreasingly? Nesub-regionsandcomputinghistogramsoflocalfeaturesfoundinsideeachsub-region.Theresult-ing "spatialpyramid" isasimpleandcomputationallyef, -cientextensionofanorderlessbag-of-featuresimagerep-resentation, cantlyimprovedperfor-manceonchallengingscenecategorizationtasks.Speci,, -cally, ourproposedmethodexceedsthestateoftheartontheCaltech-101databaseandachieveshighaccuracyonalargedatabaseof, fteennaturalscenecategories.Thespa-tialpyramidframeworkalsooffersinsightsintothesuccessofseveralrecentlypr
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Boundary Detection Using Pointwise Crisp article c
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Application background For image segmentation, edge detection, etc.; have shown that contrary, WeRecent trends it, is possible to achieve excellent boundary detection results toVery local information and low-dimensional feature spaces. This is achieved usingThe use of a novel statistical framework based on Pointwise mutual throughInformation. Key Technology Boundaries between semantically meaningful ob- DetectingIn visual scenes is an important component of many vision algo- jectsIn this paper we, propose a novel method for detecting such rithms.Based on a simple underlying principle: pixels belonging to boundariesSame object exhibit higher statistical dependencies than pixels be- theTo di erent objects. We show how to derive an anity measure longingOn this principle using pointwise mutual information and, we show basedThis measure is indeed a good predictor of whether or not two pixels thatOn the same object. Using this anity with spectral clu
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2014 CVPR Histogram Dirichlet-based Feature Transf
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Application background With the Dirichlet distribution of the new vector Fisher implementation, compared to the original vector Fisher training parameters to reduce, the origin of the three into one, improve the efficiency of training, but also improve the accuracy of training. Key Technology Development of image classifications such, as by recentLocal descriptors. In this paper we, propose a method SIFTEfficiently transform those histogram features for improving toClassification performance. The the (L1-normalized)Feature is regarded as a probability mass function histogram,Is modeled by Dirichlet distribution. Based on whichProbabilistic modeling we, induce the Dirichlet Fisher theFor transforming the histogram feature vector. The kernelWorks on the individual histogram feature to enhance methodDiscriminative power at a low computational theOn the other hand in, the bag-of-feature cost. (BoF) framework,Dirichlet mixture model can be extended to
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