In this paper, we present a novel method for activity analysis in outdoor environment. Difficulties, such as low resolution, shadows, long distances, and segmentation problems, usually exist in outdoor environment. To...
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ISBN:
(纸本)9781467352512
In this paper, we present a novel method for activity analysis in outdoor environment. Difficulties, such as low resolution, shadows, long distances, and segmentation problems, usually exist in outdoor environment. To deal with these difficulties, the activity width sequence image which maintains 3D features using 2D representation is exploited to represent the silhouette structure information of each frame and dynamic properties of activity. The activity width vectors are converted to the gray value successively according to the order in activity sequence and the gray image is formed in spatio-temporal space. We regard the activity width sequence image as the texture and choose local binary patterns which is a powerful texture extraction operator to analyze the spatio-temporal pattern. The method is simple and effective. We test our method based on the outdoor database and the results are encouraging.
Speckle is a multiplicative noise that greatly deteriorates images. In this paper a model of local binary patterns (LBP) adapted to images with speckle (MuLBP) is proposed. The multiplicative model is constructed by s...
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ISBN:
(数字)9781839531088
Speckle is a multiplicative noise that greatly deteriorates images. In this paper a model of local binary patterns (LBP) adapted to images with speckle (MuLBP) is proposed. The multiplicative model is constructed by substituting the additive comparisons of the traditional LBP for multiplicative comparisons from the Bigeometric Calculus. The experiments were carried out considering the 10.824 images of the KTH-TIPS2, FMD, CASIA and UFI databases. To compare the additive and multiplicative models, the Euclidean distance between the LBP histograms of the image with noise and without noise is adopted. The results indicate that, the distance between the histograms, the image with noise with respect to the image without noise, is smaller for the multiplicative models than for the traditional additive models. The above means that, the multiplicative LBP represent in a better way the textures in images contaminated with speckle.
The partial occlusion is one of the key issues in the face recognition community. To resolve the problem of partial occlusion, based on our previous work of local Gabor binarypatterns (LGBP) for face recognition, we ...
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The partial occlusion is one of the key issues in the face recognition community. To resolve the problem of partial occlusion, based on our previous work of local Gabor binarypatterns (LGBP) for face recognition, we further propose Kullback-Leibler divergence (KLD)-based LGBP for partial occluded face recognition. The local property of LGBP face recognition is thoroughly used in the method, by introducing KLD between the LGBP feature of the local region and that of the non-occluded local region to estimate the probability of occlusion. The probability is used as the weight of the local region for the final feature matching. The experimental results on the AR face database demonstrate the effectiveness of the KLD-based LGBP face recognition method for partially occluded face images.
In this work, we present a novel algorithm for face recognition named statistical local binary patterns (sLBP) . This is a further development of original localbinary Pattern algorithm. Our method is applied for face...
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In this work, we present a novel algorithm for face recognition named statistical local binary patterns (sLBP) . This is a further development of original localbinary Pattern algorithm. Our method is applied for face recognition under visual light environment dealing with dramatically illumination varying on faces. After a statistical analysis on the distribution probability of the gray-level difference values between neighbor pixels, a mapping function is proposed to encode a wide range of these values into three binary bits. Three extension LBP layers are then generated Finally the uniform pattern histograms of all these layers in every divided region are concatenated as an enhanced local feature vector of the face image. Experimental results on FERET face database show considerable effectiveness and robustness of our proposed method.
Facial expression recognition is an interesting and challenging problem and impacts important applications in many areas such as intelligent environments and multimodal human computer ***,Gabor wavelets representation...
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Facial expression recognition is an interesting and challenging problem and impacts important applications in many areas such as intelligent environments and multimodal human computer ***,Gabor wavelets representation and local binary patterns (LBP) are three types of successful facial features for facial expression *** this paper,the recognition performance of geometry,Gabor wavelets representation and LBP,was compared on facial expression recognition *** results on the popular JAFFE facial expression database demonstrate Gabor wavelets representation obtains the best performance,outperforming geometry and LBP.
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial...
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ISBN:
(纸本)9781479900312
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called local binary patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.
This paper introduces a method for characterizing and classifying skin lesions in dermoscopic color images with the goal of detecting which ones are melanoma (cancerous lesions). The images are described by means of t...
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ISBN:
(纸本)9781479983407
This paper introduces a method for characterizing and classifying skin lesions in dermoscopic color images with the goal of detecting which ones are melanoma (cancerous lesions). The images are described by means of the local binary patterns (LBPs) computed on geometrical feature maps of each color component of the image. These maps are extracted from geometrical measurements of the General Adaptive Neighborhoods (GAN) of the pixels. The GAN of a pixel is a region surrounding it and fitting its local image spatial structure. The performance of the proposed texture descriptor has been evaluated by means of an Artificial Neural Network, and it has been compared with the classical LBPs. Experimental results using ROC curves show that the GAN-based method outperforms the classical one and the dermatologists' predictions.
local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture ch...
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ISBN:
(纸本)9781479910267
local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture characterisation. Multidimensional LBP (MD-LBP) utilises this concept, while also maintaining the relationships between the different scales by building a multi-dimensional histogram of LBP features. Although this has been shown to give good discriminatory power, the resulting feature vectors are also rather large. In this paper, we show that Dominant MD-LBP (D-MD-LBP), which utilises only dominant texture bins, provides an effective texture descriptor of reduced dimensionality as our experimental results, run on three benchmark datasets of the Outex test suite, confirm.
A novel medical image retrieval algorithm based on texture is proposed. The contourlet transform combines non-separable and directional niters banks, and has multiscale and directional properties. The marginal distrib...
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ISBN:
(纸本)9781424427994
A novel medical image retrieval algorithm based on texture is proposed. The contourlet transform combines non-separable and directional niters banks, and has multiscale and directional properties. The marginal distribution of contourlet transform coefficients is modeled by generalized Gaussian density. It is used for texture feature extraction in transform domain. Uniform local binary patterns have good rotation invariance. It extracts texture feature in spatial domain and his retrieval time is short. A texture feature extracting algorithm combined statistical features of the contourlet with block-based uniform local binary patterns is proposed further. The two texture feature were extracted in spatial domain and in transform domain, which are complementary. A database of medical images was retrieved by this algorithm. The result shows it can achieve a high precision of retrieval.
This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at ...
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ISBN:
(纸本)9781424442959
This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at a specific scale and orientation. local binary patterns are extracted from the selected curvelet sub-bands to form the descriptive feature set of the expressions. The average of the features of a particular class of expression is considered as the representative feature set of that class. The expression recognition is performed using a nearest neighbor classifier with Chi-square as the dissimilarity metric. Experiments show that our method yields recognition rates of 93percent and 90percent in JAFFE and Cohn-Kanade databases respectively.
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