Search results for "Feature transform"

showing 3 items of 23 documents

SIFT Texture Description for Understanding Breast Ultrasound Images

2014

Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.

medicine.diagnostic_testFeature transformbusiness.industryTexture DescriptorInformationSystems_INFORMATIONSTORAGEANDRETRIEVALComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformPattern recognitionTexture (geology)ComputingMethodologies_PATTERNRECOGNITIONmedicineDegree of similarityComputer visionArtificial intelligencebusinessBreast ultrasoundMathematics
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Image classification based on 2D feature motifs

2013

The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. In general, various features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the candidate feature set. In this paper, we study the capability of a special class of motifs previously introduced in the literature, i.e. 2D irredundant motifs, when they are exploited as feat…

pattern discoveryContextual image classificationProbabilistic latent semantic analysisExploitComputer sciencebusiness.industryScale-invariant feature transformPattern recognitioncomputer.software_genreDigital imageComputingMethodologies_PATTERNRECOGNITIONclassificationimage analysisVisual WordArtificial intelligenceData miningbusinessClassifier (UML)computerImage compression
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Visual saliency by keypoints distribution analysis

2011

In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual a…

saliency visual attentiontexture SIFTComputer sciencebusiness.industryFixation (visual)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVisual attentionScale-invariant feature transformPattern recognitionComputer visionTop-down and bottom-up designArtificial intelligencebusinessVisual saliency
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