This paper investigates novel LBP-guided active contour approaches to texture segmentation. The localbinary pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for...
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This paper investigates novel LBP-guided active contour approaches to texture segmentation. The localbinary pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, two LBP-guided active contours have been formulated, namely the scalar-LBP active contour (s-LAC) and the vector-LBP active contour (v-LAC). These active contours combine the advantages of both the LBP texture representation and the vector-valued active contour without edges model, and result in high quality texture segmentation. s-LAC avoids the iterative calculation of active contour equation terms derived from textural feature vectors and enables efficient, high quality texture segmentation. v-LAC evolves utilizing regional information encoded by means of LBP feature vectors. It involves more complex computations than s-LAC but it can achieve higher segmentation quality. The computational cost involved in the application of v-LAC can be reduced if it is preceded by the application of s-LAC. The experimental evaluation of the proposed approaches demonstrates their segmentation performance on a variety of standard images of natural textures and scenes. (C) 2008 Elsevier B.V. All rights reserved.
The ever-increasing number of digital images in the medical domain, has amplified the need for automated search and retrieval tools. Furthermore, medical experts generally focus on specific anatomical structures to id...
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ISBN:
(纸本)9781424420025
The ever-increasing number of digital images in the medical domain, has amplified the need for automated search and retrieval tools. Furthermore, medical experts generally focus on specific anatomical structures to identify the cause of a pathology. For such cases, automated tools that can retrieve relevant slice(s) from a patient's image volume can assist the expert in diagnosis. Accordingly, in this paper we introduce a new search and retrieval work for finding relevant slices in brain MR (magnetic resonance) volumes. The features explored in this framework are based on intensity, texture, and their extended versions complemented with spatial context. Experiments on real data revealed that texture information outperformed its intensity counterpart, incorporating spatial context in the features substantially improved the accuracy, and finally texture features with spatial context provided fast and highly accurate retrieval of relevant slices.
Estimating the age exactly and then producing the younger and older images of the person is important in security systems design. In this paper local binary patterns are used to classify the age from facial images. Th...
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ISBN:
(纸本)9781424428809
Estimating the age exactly and then producing the younger and older images of the person is important in security systems design. In this paper local binary patterns are used to classify the age from facial images. The local binary patterns (LBP) are fundamental properties of local image texture and the occurrence histogram of these patterns is an effective texture feature for face description. In the study we classify the FERET images according to their ages with 10 years intervals. The faces are divided into small regions from which the LBP histograms are extracted and concatenated into a feature vector to be used as an efficient face descriptor. For every new face presented to the system, spatial LBP histograms are produced and used to classify the image into one of the age classes. In the classification phase, minimum distance, nearest neighbor and k-nearest neighbor classifiers are used. The experimental results have shown that system performance is 80% for age estimation.
Illumination normalization is very important for 2D face verification. This study examines the state-of-art illumination normalization methods, and proposes two solutions, namely horizontal Gaussian derivative filters...
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ISBN:
(纸本)9789898111210
Illumination normalization is very important for 2D face verification. This study examines the state-of-art illumination normalization methods, and proposes two solutions, namely horizontal Gaussian derivative filters and local binary patterns. Experiments show that our methods significantly improve the generalization capability, while maintaining good discrimination capability of a face verification system. The proposed illumination normalization methods have low requirements on image acquisition, and low computation complexities, and are very suitable for low-end 2D face verification systems.
In this work, we present a novel facial feature extraction method for automatic facial expression recognition, which is based on difference of localbinary pattern histogram sequences. First, the localbinary Pattern ...
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ISBN:
(纸本)9781424421787
In this work, we present a novel facial feature extraction method for automatic facial expression recognition, which is based on difference of localbinary pattern histogram sequences. First, the localbinary Pattern (LBP) technique is used to extract facial texture features. Then the LBP Map is divided into non-overlapping rectangle regions with specific size, and histogram is computed for each region, which is concatenate to form histogram sequence called LBPHS. At last, the difference between the LBPHSes (DLBPHS) of the expression image and the neutral expression image is computed to form the final facial expression feature. The proposed feature extraction algorithm is tested on the JAFFE database and experimental results show that it is promising.
In this paper, a multiple classification method for plant species based on Gabor filters and local binary patterns (LBP) operator is proposed. We classify plant species by extracting global texture features with Gabor...
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ISBN:
(纸本)9783540859291
In this paper, a multiple classification method for plant species based on Gabor filters and local binary patterns (LBP) operator is proposed. We classify plant species by extracting global texture features with Gabor filters and local features with LBP operator. Simply speaking, the LBP operator is conducted on the magnitude maps of multi-scale and multi-orientation Gabor filtered image rather than original image. Thus, a plant leaf is presented as more spatial histograms with varying scales and orientations. The method has impressively improved the performance comparing with using Gabor transform or LBP absolutely.
In this work,we present a novel facial feature extraction method for automatic facial expression recognition,which is based on difference of localbinary pattern histogram ***,the localbinary Pattern (LBP) technique ...
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In this work,we present a novel facial feature extraction method for automatic facial expression recognition,which is based on difference of localbinary pattern histogram ***,the localbinary Pattern (LBP) technique is used to extract facial texture *** the LBP Map is divided into non-overlapping rectangle regions with specific size,and histogram is computed for each region,which is concatenate to form histogram sequence called *** last,the difference between the LBPHSes (DLBPHS) of the expression image and the neutral expression image is computed to form the final facial expression *** proposed feature extraction algorithm is tested on the JAFFE database and experimental results show that it is promising.
Ultrasound imaging of thyroid gland provides the ability to acquire valuable information for medical diagnosis. This study presents a novel scheme for the analysis of longitudinal ultrasound images aiming at efficient...
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ISBN:
(纸本)9783540742586
Ultrasound imaging of thyroid gland provides the ability to acquire valuable information for medical diagnosis. This study presents a novel scheme for the analysis of longitudinal ultrasound images aiming at efficient and effective computer-aided detection of thyroid nodules. The proposed scheme involves two phases: a) application of a novel algorithm for the detection of the boundaries of the thyroid gland and b) detection of thyroid nodules via classification of localbinary Pattern feature vectors extracted only from the area between the thyroid boundaries. Extensive experiments were performed on a set of B-mode thyroid ultrasound images. The results show that the proposed scheme is a faster and more accurate alternative for thyroid ultrasound image analysis than the conventional, exhaustive feature extraction and classification scheme.
In this paper, we present an effective approach for spatiotemporal face recognition from videos using an Extended set of Volume LBP (localbinary Pattern features) and a boosting scheme. Among the key properties of ou...
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ISBN:
(数字)9783540756903
ISBN:
(纸本)9783540756897
In this paper, we present an effective approach for spatiotemporal face recognition from videos using an Extended set of Volume LBP (localbinary Pattern features) and a boosting scheme. Among the key properties of our approach are: (1) the use of local Extended Volume LBP based spatiotemporal description instead of the holistic representations commonly used in previous works;(2) the selection of only personal specific facial dynamics while discarding the intra-personal temporal information;and (3) the incorporation of the contribution of each local spatiotemporal information. To the best of our knowledge, this is the first work addressing the issue of learning the personal specific facial dynamics for face recognition. We experimented with three different publicly available video face databases (MoBo, CRIM and Honda/UCSD) and considered five benchmark methods (PCA, LDA, LBP, HMMs and ARMA) for comparison. Our extensive experimental analysis clearly assessed the excellent performance of the proposed approach, significantly outperforming the comparative methods and thus advancing the state-of-the-art.
Cílem tohoto projektu je zhodnotit účinnost využití texturních příznaků při rozpoznávání a klasifikaci textur v počítačovém zpracování obrazu. Stěžejn&...
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Cílem tohoto projektu je zhodnotit účinnost využití texturních příznaků při rozpoznávání a klasifikaci textur v počítačovém zpracování obrazu. Stěžejním úkolem práce je porovnat a diskutovat experimentálně získané výsledky a efektivitu jejich dosažení použitím texturních příznaků implementovaných metodou lokálních binárních vzorů v konfrontaci s výsledky docílenými s využitím matic sousednosti při klasifikaci shlukovou analýzou.
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