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An Improved Texture-Based Method for Background Subtraction Using local binary patterns

An Improved Texture-Based Method for Background Subtraction ...

引用

2nd International Congress on Image and Signal Processing

作者： Tian, Guodong Men, Aidong Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100088 Peoples R China

ISBN: (纸本)9781424441303

Texture-based method (TBM) using local binary patterns (LBP) proposed in ill is a successful solution to background subtraction especially for dynamic background scenes. However, it usually suffers from inaccuracy of the shapes of segmentation results and slow adaptation to the current situation. In this paper, we present an improved TBM that solves the two problems. To solve the first problem, a spatially weighted LBP histogram (SWLH) is proposed to be the feature vector and a simple shadow removing method is introduced. When dealing with the second one, we use an adaptive learning rate for each model LBP histogram and maintain multiple frame level models to process sudden illumination changes. Experimental results show that the proposed method outperforms the original TBM.

关键词： background subtraction texture-based method local binary patterns

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Image Retrieval Based on Contourlet Transform and local binary patterns

Image Retrieval Based on Contourlet Transform and Local Bina...

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4th IEEE Conference on Industrial Electronics and Applications

作者： Zhang, Qidong Wu, Jianhua Gao, Liqun Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781424427994

A novel medical image retrieval algorithm based on texture is proposed. The contourlet transform combines nonseparable and directional filters 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.

关键词： Texture feature Contourlet transform local binary patterns

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Comparative Analysis of the Speed Performance of Texture Analysis Algorithms on a Graphic Processing Unit (GPU)

Comparative Analysis of the Speed Performance of Texture Ana...

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Conference on Computational Imaging XII

作者： Triana-Martinez, J. Orjuela-Vargas, S. A. Philips, W. Univ Antonio Narino Ibague Tolima Colombia Univ Ghent Dept Telecommun & Informat Proc TELIN IPI IMINDS Ghent Belgium

ISBN: (纸本)9780819499370

This paper compares the speed performance of a set of classic image algorithms for evaluating texture in images by using CUDA programming. We include a summary of the general program mode of CUDA. We select a set of texture algorithms, based on statistical analysis, that allow the use of repetitive functions, such as the Co-ocurrence Matrix, Haralick features and local binary patterns techniques. The memory allocation time between the host and device memory is not taken into account. The results of this approach show a comparison of the texture algorithms in terms of speed when executed on CPU and GPU processors. The comparison shows that the algorithms can be accelerated more than 40 times when implemented using CUDA environment.

关键词： local binary patterns image analysis using CUDA Robustness to artificial lighting

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Comparison of 3D Texture-Based Image Descriptors in Fluorescence Microscopy

Comparison of 3D Texture-Based Image Descriptors in Fluoresc...

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16th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Majtner, Tomas Svoboda, David Masaryk Univ Ctr Biomed Image Anal Brno Czech Republic

ISBN: (纸本)9783319071480;9783319071473

In recent years, research groups pay even more attention on 3D images, especially in the field of biomedical image processing. Adding another dimension enables to capture the entire object. On the other hand, handling 3D images also requires new algorithms, since not all of them can be modified for higher dimensions intuitively. In this article, we introduce a comparison of various implementations of 3D texture descriptors presented in the literature in recent years. We prepared an unified environment to test all of them under the same conditions. From the results of our tests we came to conclusion, that 3D variants of LBP in the combination with k-NN classifier are a very strong approach with the classification accuracy more than 99% on selected group of 3D biomedical images.

关键词： 3D images Texture descriptors Fluorescence microscopy local binary patterns

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Combination of OC-LBP and Longest Run Features for Off-Line Signature Verification 10

Combination of OC-LBP and Longest Run Features for Off-Line ...

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International Conference on Signal-Image Technology and Internet-Based Systems SITIS

作者： Serdouk, Yasmine Nemmour, Hassiba Chibani, Youcef Univ Sci & Technol Houari Boumediene Fac Elect & Comp Sci Algiers Algeria

ISBN: (纸本)9781479979783

In this paper, we propose new data features to improve the off-line handwritten signature verification. The proposed features combine advantages of LBP and topological characteristics. Specifically, the Orthogonal Combination of LBP, which provides an LBP histogram with a reduced size, is combined with a topological descriptor that is called longest run features. The verification task is achieved by SVM classifiers and the performance assessment is conducted comparatively to the basic LBP descriptors. Results obtained on both GPDS 300 and CEDAR datasets show that the proposed features improve the verification accuracy while reducing the data size.

关键词： Signature verification Longest run features local binary patterns SVM

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Clustering Low Quality Farsi Sub-words For Word Recognition

Clustering Low Quality Farsi Sub-words For Word Recognition

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Iranian Conference on Intelligent Systems (ICIS)

作者： ArabYarmohammadi, Hamed AhmadyFard, Alireza Khosravi, Hossein Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Iran

ISBN: (纸本)9781479933501

OCR of low resolution documents is not so common, because it has a lot of problems. However, today there are several archives of digital documents which are scanned at low resolution, to consume less storage. These documents which usually have a resolution of 100 to 150 dpi, require to be converted to searchable documents. In this paper presents a new method for clustering of low quality printed Persian sub-words. This is necessary to reduce the number of classes of sub-words in order to improve the overall recognition rate. Two popular clustering methods, hierarchical and k-means implemented and compared. local binary patterns (LBP) and zoning algorithms used for feature extraction. Both features are fast and represent the global shape information very well. Moreover, we used different distance measures to find the similarity of feature vectors. We applied our algorithms on a dataset of 10,700 images of distinct Persian sub-words with 96 dpi resolution. Experimental results show that the hierarchical clustering with the correlation distance measure has the best performance over other clustering methods and distance measures.

关键词： Clustering Persian Typing Sub-Words Low Resolution Hierarchical K-meams local binary patterns Zoning

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Texture descriptor approaches to level set segmentation in medical images

Texture descriptor approaches to level set segmentation in m...

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Optics, Photonics, and Digital Technologies for Multimedia Applications III

作者： Olveres, Jimena Nava, Rodrigo Moya-Albor, Ernesto Escalante-Ramirez, Boris Brieva, Jorge Cristobal, Gabriel Vallejo, Enrique Univ Nacl Autonoma Mexico Mexico City 04510 DF Mexico Univ Panamericana Fac Ingn Mexico City DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Dept Procesamiento Senales Mexico City DF Mexico CSIC Inst Opt E-28006 Madrid Spain Hosp Angeles Pedregal Mexico City DF Mexico

ISBN: (纸本)9781628410860

Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation;such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values.

关键词： Hermite features Hounsfield Left ventricular blood flow Level sets local binary patterns Texture segmentation

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Body Joint Tracking in Low Resolution Video Using Region-Based Filtering 10

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10th International Symposium on Visual Computing (ISVC)

作者： Nair, Binu M. Kendricks, Kimberly D. Asari, Vijayan K. Tuttle, Ronald F. Univ Dayton Dept ECE Dayton OH 45469 USA Cent State Univ CHPSA Lab Wilberforce OH USA Air Force Inst Technol Wright Patterson AFB OH USA

ISBN: (纸本)9783319142494;9783319142487

We propose a region-based body joint tracking scheme to track and estimate continuous joint locations in low resolution imagery where the estimated trajectories can be analyzed for specific gait signatures. The true transition between the joint states are of a continuous nature and specifically follows a sinusoidal trajectory. Recent state of art techniques enables us to estimate pose at each frame from which joint locations can be deduced. But these pose estimates at low resolution are often noisy and discrete and hence not suitable for further gait analysis. Our proposed 2-level region-based tracking scheme gets a good approximation to the true trajectory and obtains finer estimates. Initial joint locations are deduced from a human pose estimation algorithm and subsequent finer locations are estimated and tracked by a Kalman filter. We test the algorithm on sequences containing individuals walking outdoors and evaluate their gait using the estimated joint trajectories.

关键词： kalman filter region-based tracking local binary patterns histogram of oriented gradients low resolution

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A New Facial Expression Recognition Method Based on Geometric Alignment and LBP Features 17

A New Facial Expression Recognition Method Based on Geometri...

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IEEE 17th International Conference on Computational Science and Engineering (CSE)

作者： Wang, Xun Liu, Xingang Lu, Lingyun Shen, Zhixin Univ Elect Sci & Technol China Dept Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781479979813

Automatic facial expression recognition has been drawn many attentions in both computer vision and artificial intelligence (AI) for the past decades. Although much progress has been made, facial expression recognition (FER) is still a challenging and interesting problem. In this paper, we propose a new FER system, which uses the active shape mode (ASM) algorithm to align the faces, then extracts local binary patterns (LBP) features and uses support vector machine (SVM) classifier to predict the facial emotion. Experiments on the Jaffe database show that the proposed method has a promising performance and increases the recognition rate by 5.2% compared to the method using Gabor features.

关键词： Facial expression recognition Active shape mode local binary patterns Support vector machine

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Traversable Region Detection based on Near-to-far Self-supervised Incremental Learning

Traversable Region Detection based on Near-to-far Self-super...

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International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

作者： Chen, Yunlei Lu, Huimin Xiao, Junhao Zhang, Hui Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479967315

Effective far-range traversable region detection is a fundamental issue for mobile robots. However, the performance of traditional methods is limited as distance estimation of stereo-vision system is unreliable beyond 10-15m. In this paper, we proposed a far-range traversable region detection algorithm based on near-to-far self-supervised learning. In the algorithm, superpixel segmentation is employed as preprocessing to reduce the computational complexity. Then, near-range LBP features are extracted for each superpixel. Afterwards, the resulted LBP fea-tures are used to train an Incremental Supported Vector Machine (ISVM) for classification, which enhances the far-range region classification performance. Thorough experiments have been carried out utilizing our Nubot mobile robot in outdoor environments. Furthermore the proposed algorithm has also been evaluated using the KITTI Vision Benchmark dataset compared against state of the art algorithms. The results show that the proposed algorithm can detect the traversable regions in a far range efficiently at a high successful rate.

关键词： traversable region detection near-to-far self-supervised learning local binary patterns SLIC superpixels incremental SVM

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