20,760 research outputs found
Rotation and Scale Invariant Texture Classification
Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important
problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then
passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O (n*log n) complexity. The experimental results, based on different testing data sets for images from Brodatz album
with different orientations and scales, show that the implemented classification scheme outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.09 percent
Rotation invariant texture descriptors based on Gaussian Markov random fields for classification
Local Parameter Histograms (LPH) based on Gaussian–Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are discussed namely, Rotation Invariant LPH (RI-LPH) and the Isotropic LPH (I-LPH) descriptors. Extensive texture classification experiments using traditional GMRF features, LPH features, RI-LPH and I-LPH features are performed. Furthermore comparisons to the current state-of-the-art texture features are made. Classification results demonstrate that LPH, RI-LPH and I-LPH features achieve significantly better accuracies compared to the traditional GMRF features. RI-LPH descriptors give the highest classification rates and offer the best texture discriminative competency. RI-LPH and I-LPH features maintain higher accuracies in rotation invariant texture classification providing successful rotational invariance
Scale Selective Extended Local Binary Pattern for Texture Classification
In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-features. Then, we build a scale space using Gaussian filters and
calculate the histogram of multi-scale ELBPs for the image at each scale.
Finally, we select the maximum values from the corresponding bins of
multi-scale ELBP histograms at different scales as scale-invariant features. A
comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows
that the proposed SSELBP has high accuracy comparable to state-of-the-art
texture descriptors on gray-scale-, rotation-, and scale-invariant texture
classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 201
Gabor filters for rotation invariant texture classification
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Rotation and scale invariant texture classification using log polar wavelet energy signatures
Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures.The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So,the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n*log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a modified Mahalanobis classifier to classify a set of 12 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the implemented classification scheme using log- polar wavelet signatures outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.59 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features
Completed Local Ternary Pattern for Rotation Invariant Texture Classification
Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed
Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some
Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the
same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust
to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed
modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed
Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four
different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP
and CLBC descriptors
Overcomplete steerable pyramid filters and rotation invariance
A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated
Classification of wood defect images using local binary pattern variants
This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects
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