The Experts below are selected from a list of 25542 Experts worldwide ranked by ideXlab platform
Matti Pietikäinen - One of the best experts on this subject based on the ideXlab platform.
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globally Rotation Invariant multi scale co occurrence local binary pattern
Image and Vision Computing, 2015Co-Authors: Linlin Shen, Guoying Zhao, Matti PietikäinenAbstract:This paper proposes a globally Rotation Invariant multi-scale co-occurrence local binary pattern (MCLBP) feature for texture-relevant tasks. In MCLBP, we arrange all co-occurrence patterns into groups according to properties of the co-patterns, and design three encoding functions (Sum, Moment, and Fourier Pooling) to extract features from each group. The MCLBP can effectively capture the correlation information between different scales and is also globally Rotation Invariant (GRI). The MCLBP is substantially different from most existing LBP variants including the LBP, the CLBP, and the MSJ-LBP that achieves Rotation invariance by locally Rotation Invariant (LRI) encoding. We fully evaluate the properties of the MCLBP and compare it with some powerful features on five challenging databases. Extensive experiments demonstrate the effectiveness of the MCLBP compared to the state-of-the-art LBP variants including the CLBP and the LBPHF. Meanwhile, the dimension and computational cost of the MCLBP is also lower than that of the CLBP_S/M/C and LBPHF_S_M. This paper proposes a globally Rotation Invariant multi-scale co-occurrence of LBPs (MCLBP).The proposed MCLBP can effectively capture the correlation between the LBPs in different scales.Three globally Rotation Invariant encoding methods are introduced for MCLBP.The proposed MCLBP performs very well on texture, material, and medical cell classification.
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Rotation Invariant image and video description with local binary pattern features
IEEE Transactions on Image Processing, 2012Co-Authors: Guoying Zhao, Timo Ahonen, Jiri Matas, Matti PietikäinenAbstract:In this paper, we propose a novel approach to compute Rotation-Invariant features from histograms of local nonInvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two Rotation-Invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel Rotation-Invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of Rotation-Invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not Rotation Invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform nonInvariant and earlier versions of the Rotation-Invariant LBP in the Rotation-Invariant texture classification. In experiments on two dynamic-texture databases with Rotations or view variations, the proposed video features can effectively deal with Rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-Invariant recognition of DTs.
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Rotation Invariant image description with local binary pattern histogram fourier features
Scandinavian Conference on Image Analysis, 2009Co-Authors: Timo Ahonen, Jiři Matas, Chu He, Matti PietikäinenAbstract:In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel Rotation Invariant image descriptor computed from discrete Fourier transforms of local binary pattern (LBP) histograms. Unlike most other histogram based Invariant texture descriptors which normalize Rotation locally, the proposed Invariants are constructed globally for the whole region to be described. In addition to being Rotation Invariant, the LBP-HF features retain the highly discriminative nature of LBP histograms. In the experiments, it is shown that these features outperform non-Invariant and earlier version of Rotation Invariant LBP and the MR8 descriptor in texture classification, material categorization and face recognition tests.
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multiresolution gray scale and Rotation Invariant texture classification with local binary patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and Rotation Invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and Rotation Invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns.
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a generalized local binary pattern operator for multiresolution gray scale and Rotation Invariant texture classification
International Conference on Advances in Pattern Recognition, 2001Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:This paper presents generalizations to the gray scale and Rotation Invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and Rotation Invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of Rotation invariance, where the classifier is trained at one particular Rotation angle and tested with samples from other Rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with Rotation Invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for Rotation Invariant texture analysis.
Timo Ojala - One of the best experts on this subject based on the ideXlab platform.
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multiresolution gray scale and Rotation Invariant texture classification with local binary patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and Rotation Invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and Rotation Invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns.
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a generalized local binary pattern operator for multiresolution gray scale and Rotation Invariant texture classification
International Conference on Advances in Pattern Recognition, 2001Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:This paper presents generalizations to the gray scale and Rotation Invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and Rotation Invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of Rotation invariance, where the classifier is trained at one particular Rotation angle and tested with samples from other Rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with Rotation Invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for Rotation Invariant texture analysis.
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gray scale and Rotation Invariant texture classification with local binary patterns
European Conference on Computer Vision, 2000Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:This paper presents a theoretically very simple yet efficient approach for gray scale and Rotation Invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The proposed approach is very robust in terms of gray scale variations, since the operators are by definition Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operators can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in two true problems of Rotation invariance, where the classifier is trained at one particular Rotation angle and tested with samples from other Rotation angles, demonstrate that good discrimination can be achieved with the statistics of simple Rotation Invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with Rotation Invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for Rotation Invariant texture analysis.
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Rotation Invariant texture classification using feature distributions
Pattern Recognition, 2000Co-Authors: Matti Pietikäinen, Timo OjalaAbstract:Abstract A distribution-based classification approach and a set of recently developed texture measures are applied to Rotation-Invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven Rotation angles is used in experiments. The results show much better performance for our approach than for the CSAR features. A detailed analysis of the confusion matrices and the Rotation angles of misclassified samples produces several interesting observations about the classification problem and the features used in this study.
Topi Maenpaa - One of the best experts on this subject based on the ideXlab platform.
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multiresolution gray scale and Rotation Invariant texture classification with local binary patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and Rotation Invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and Rotation Invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns.
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a generalized local binary pattern operator for multiresolution gray scale and Rotation Invariant texture classification
International Conference on Advances in Pattern Recognition, 2001Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:This paper presents generalizations to the gray scale and Rotation Invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and Rotation Invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of Rotation invariance, where the classifier is trained at one particular Rotation angle and tested with samples from other Rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple Rotation Invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with Rotation Invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for Rotation Invariant texture analysis.
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gray scale and Rotation Invariant texture classification with local binary patterns
European Conference on Computer Vision, 2000Co-Authors: Timo Ojala, Matti Pietikäinen, Topi MaenpaaAbstract:This paper presents a theoretically very simple yet efficient approach for gray scale and Rotation Invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The proposed approach is very robust in terms of gray scale variations, since the operators are by definition Invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operators can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in two true problems of Rotation invariance, where the classifier is trained at one particular Rotation angle and tested with samples from other Rotation angles, demonstrate that good discrimination can be achieved with the statistics of simple Rotation Invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with Rotation Invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for Rotation Invariant texture analysis.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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pairwise Rotation Invariant co occurrence local binary pattern
European Conference on Computer Vision, 2012Co-Authors: Rong Xiao, Jun Guo, Lei ZhangAbstract:In this work, we introduce a novel pairwise Rotation Invariant co-occurrence local binary pattern (PRI-CoLBP) feature which incorporates two types of context - spatial co-occurrence and orientation co-occurrence. Different from traditional Rotation Invariant local features, pairwise Rotation Invariant co-occurrence features preserve relative angle between the orientations of individual features. The relative angle depicts the local curvature information, which is discriminative and Rotation Invariant. Experimental results on the CUReT, Brodatz, KTH-TIPS texture dataset, Flickr Material dataset, and Oxford 102 Flower dataset further demonstrate the superior performance of the proposed feature on texture classification, material recognition and flower recognition tasks.
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monogenic lbp a new approach for Rotation Invariant texture classification
International Conference on Image Processing, 2010Co-Authors: Lin Zhang, Lei Zhang, Zhenhua Guo, David ZhangAbstract:Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of Rotation Invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two Rotation Invariant measures: the local phase and the local surface type computed by the 1st-order and 2ndorder Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.
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Rotation Invariant texture classification using lbp variance lbpv with global matching
Pattern Recognition, 2010Co-Authors: Zhenhua Guo, Lei Zhang, David ZhangAbstract:Local or global Rotation Invariant feature extraction has been widely used in texture classification. Local Invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally Rotation Invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally Rotation Invariant LBP method.
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Rotation Invariant image classification based on mpeg 7 homogeneous texture descriptor
Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing, 2007Co-Authors: Lei Zhang, Bo YuanAbstract:Image classification based on texture features plays an important role in content-based image retrieval. A novel method for Rotation Invariant image classification is proposed based on MPEG-7 homogeneous texture descriptor (HTD). To compute HTD, Gabor transform is first performed by filtering image using a bank of orientation and scale selective band-pass filters called Gabor wavelets. For constructing the feature vector, the mean energy and standard deviation are calculated separately on each filtered and original image. Then the summations of energy in different direction are calculated and the direction with the maximum energy is chosen as the dominant orientation, which is used to shift the feature vector circularly in order to keep Rotation Invariant. The classification is done by SVM (support vector machine). The method is tested on Brodatz and UIUCTex datasets. The experiments demonstrate the method is effective and efficient for Rotation Invariant texture classification.
Jerome Mars - One of the best experts on this subject based on the ideXlab platform.
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shift 2d Rotation Invariant sparse coding for multivariate signals
IEEE Transactions on Signal Processing, 2012Co-Authors: Quentin Barthelemy, Anthony Larue, Aurelien Mayoue, David Mercier, Jerome MarsAbstract:Classical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide Rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first introduced as a tool to easily solve our problem and to preserve the data structure. Within this multivariate framework, we then present sparse coding methods: multivariate orthogonal matching pursuit (M-OMP), which provides sparse approximation for multivariate signals, and multivariate DLA (M-DLA), which empirically learns the characteristic patterns (or features) that are associated to a multivariate signals set, and combines shift-invariance and online learning. Once the multivariate dictionary is learned, any signal of this considered set can be approximated sparsely. This multivariate framework is introduced to simply present the 2D Rotation Invariant (2DRI) case. By studying 2D motions that are acquired in bivariate real signals, we want the decompositions to be independent of the orientation of the movement execution in the 2D space. The methods are thus specified for the 2DRI case to be robust to any Rotation: 2DRI-OMP and 2DRI-DLA. Shift and Rotation Invariant cases induce a compact learned dictionary and provide robust decomposition. As validation, our methods are applied to 2D handwritten data to extract the elementary features of this signals set, and to provide Rotation Invariant decomposition.
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multivariate dictionary learning and shift 2d Rotation Invariant sparse coding
IEEE Signal Processing Workshop on Statistical Signal Processing, 2011Co-Authors: Quentin Barthelemy, Anthony Larue, Aurelien Mayoue, David Mercier, Jerome MarsAbstract:In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D Rotation-Invariant case. Shift and Rotation-Invariant cases induce a compact learned dictionary. Our methods are applied to 2D handwritten data in order to extract the elementary features of this signals set.
