52,680 research outputs found
Using basic image features for texture classification
Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
<i>In situ</i> observation of strain and phase transformation in plastically deformed 301 austenitic stainless steel
To inform the design of superior transformation-induced plasticity (TRIP) steels, it is important to understand what happens at the microstructural length scales. In this study, strain-induced martensitic transformation is studied by in situ digital image correlation (DIC) in a scanning electron microscope. Digital image correlation at submicron length scales enables mapping of transformation strains with high confidence. These are correlated with electron backscatter diffraction (EBSD) prior to and post deformation process to get a comprehensive understanding of the strain-induced transformation mechanism. The results are compared with mathematical models for enhanced prediction of strain-induced martensitic phase transformation
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