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检索条件"主题词=Local binary patterns"

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DYNAMIC BACKGROUND MODELING AND SUBTRACTION USING SPATIO-TEMPORAL local binary patterns

DYNAMIC BACKGROUND MODELING AND SUBTRACTION USING SPATIO-TEM...

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Zhang, Shengping Yao, Hongxun Liu, Shaohui Harbin Inst Technol Sch Engn & Comp Sci Harbin 150001 Peoples R China

ISBN: (纸本)9781424417650

Traditional background modeling and subtraction methods shave a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poorly in dynamic scenes. In this paper, we present a solution to this problem. We first extend the local binary patterns from spatial domain to spatio-temporal domain, and present a new online dynamic texture extraction operator, named spatio-temporal local binary patterns (STLBP). Then we present a novel and effective method for dynamic background modeling and subtraction using STLBP. In the proposed method, each pixel is modeled as a group of STLBP dynamic texture histograms which combine spatial texture and temporal motion information together. Compared with traditional methods, experimental results show that the proposed method adapts quickly to the changes of the dynamic background. It achieves accurate detection of moving objects and suppresses most of the false detections for dynamic changes of nature scenes.

关键词： Background modeling object detection spatio-temporal features local binary patterns

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MEDICAL IMAGE SEARCH AND RETRIEVAL USING local binary patterns AND KLT FEATURE POINTS

MEDICAL IMAGE SEARCH AND RETRIEVAL USING LOCAL BINARY PATTER...

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Unay, Devrim Ekin, Ahmet Jasinschi, Radu Philips Res Europe Video Proc & Anal Grp NL-5656 AE Eindhoven Netherlands

ISBN: (纸本)9781424417650

In the medical domain, experts usually look at specific anatomical structures to identify the cause of a pathology, and therefore they can largely benefit from automated tools that retrieve relevant slice(s) from a patient's image volume in diagnosis. Accordingly, this paper introduces a novel search and retrieval work for finding relevant slices in brain MR (magnetic resonance) volumes. As intensity is non-standard in MR we explore performance of two complementary intensity invariant features, local binary patterns and Kanade-Lucas-Tomasi feature points, their extended versions with spatial context, and a simple edge descriptor with spatial context. Experiments on real and simulated data showed that the local binary patterns with spatial context is fast, highly accurate, and robust to geometric deformations and intensity variations.

关键词： search and retrieval brain MR local binary patterns Kanade-Lucas-Tomasi feature points spatial context

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Random texture defect detection using 1-D Hidden Markov Models based on local binary patterns

Random texture defect detection using 1-D Hidden Markov Mode...

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10th IAPR Conference on Machine Vision Applications (MVA2007)

作者： Hadizadeh, Hadi Shokouhi, Shahriar Baradaran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

In this paper a novel method for the purpose of random texture defect detection using a collection of 1-D HMMs is presented. The sound textural content of a sample of training texture images is first encoded by a compressed LBP histogram and then the local patterns of the input training textures are learned, in a multiscale framework, through a series of HMMs according to the LBP codes which belong to each bin of this compressed LBP histogram. The hidden states of these HMMs at different scales are used as a texture descriptor that can model the normal behavior of the local texture units inside the training images. The optimal number of these HMMs (models) is determined in an unsupervised manner as a model selection problem. Finally, at the testing stage, the local patterns of the input test image are first predicted by the trained HMMs and a prediction error is calculated for each pixel position in order to obtain a defect map at each scale. The detection results are then merged by an inter-scale post fusion method for novelty detection. The proposed method is tested with a database of grayscale ceramic the images.

关键词： hidden Markov models random texture texture defect detection local binary patterns

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Objective Grading of Facial Paralysis Using local binary patterns in Video Processing

Objective Grading of Facial Paralysis Using Local Binary Pat...

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30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： He, Shu Soraghan, John J. O'Reilly, Brian F. Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland Southern Gen Hosp Inst Neurol Sci Glasgow Lanark Scotland

ISBN: (纸本)9781424418145

This paper presents a novel framework for objective measurement of facial paralysis in biomedial videos. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBP) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of block schemes. A multi-resolution extension of uniform LBP is proposed to efficiently combine the micro-patterns and large-scale patterns into a feature vector, which increases the algorithmic robustness and reduces noise effects while still retaining computational simplicity. The symmetry of facial movements is measured by the Resistor-Average Distance (RAD) between LBP features extracted from the two sides of the face. Support Vector Machine (SVM) is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) Scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

关键词： Facial Paralysis Measurement local binary patterns Facial Image Analysis

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local information statistics of LBP and HOG for Pedestrian Detection

Local information statistics of LBP and HOG for Pedestrian D...

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IEEE International Conference on Intelligent Computer Communication and Processing

作者： Raluca Brehar Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

We present several methods of pedestrian detection in intensity images using different local statistical measures applied to two classes of features extensively used in pedestrian detection: uniform local binary patterns - LBP and a modified version of histogram of oriented gradients - HOG. Our work extracts local binary patterns and magnitude and orientation of the gradient image. Then we divide the image into blocks. Within each block we extract different statistics like: histogram (weighted by the gradient magnitude in the case of HOG), information, entropy and energy of the local binary code. We use Adaboost for training four classifiers and we analyze the classification error of each method on the Daimler benchmark pedestrian dataset.

关键词： Pedestrian detection local binary patterns Histogram of Oriented Gradients local Information local Energy local Entropy

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A METHOD FOR COUNTING MOVING AND STATIONARY PEOPLE BY INTEREST POINT CLASSIFICATION

A METHOD FOR COUNTING MOVING AND STATIONARY PEOPLE BY INTERE...

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IEEE International Conference on Image Processing

作者： Chi Yoon Jeong SuGil Choi Seung Wan Han Software Research Lab. Electronics & Telecommunications Research Institute S. Korea

ISBN: (纸本)9781479923427

While the methods for people counting based on moving interest points have been shown good performance, the problem related to count static or temporarily stopped people remains particularly challenging. This paper presents a novel method for people counting which is considering moving and stationary people. The proposed method first separates the moving and static points by the motion information. Then, the temporarily static points of stationary people classified by analyzing the texture information of the points between the current and the eigenbackground image. Finally, we estimate the number of people using the moving and the temporarily static points. The experimental results show that the proposed method can identify the static points related to people correctly. Additionally, the results confirm that the proposed method reduces the estimation error in the sequences containing the many temporarily static people.

关键词： People counting video surveillance image analysis interest points local binary patterns video monitoring Persons Moving image analysis binary pattern

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Face verification experiments on the LFW database with simple features, metrics and classifiers

Face verification experiments on the LFW database with simpl...

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nDS '13;Proceedings of the 8th International Workshop on Multidimensional Systems

作者： Subhradeep Kayal Dept. of Information and Computer Science Aalto University

Face verification is fast becoming a dual platform for testing face recognition algorithms. Unlike recognition, verification is non-intrusive and more practical for security applications, where it is more important to look for match or mismatch between faces, rather than the exact identity. This paper aims at providing comprehensive overwiev af the verification accuracies that can be achieved through the use of simple techniques and out-of-the-box methods. Experiments are performed with features, metrics and learning methods from different categories, i.e, global an local features, generative and discriminative models, and results are appropriately reported. The main contribution of this paper is to provide a starting point for future researchers in the field of face recognition and verification.

关键词： Labeled Faces in the Wild Face recognition Face verification Gabor Feature local binary patterns Distance metric GMM SVM Random Forest

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Regression based Learning of Human Actions from Video using HOF-LBP Flow patterns

Regression based Learning of Human Actions from Video using ...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Binu M Nair Vijayan K Asari UD Vision Lab Electrical and Computer Engineering University of Dayton Dayton OH USA

ISBN: (纸本)9781479906505

A human action recognition framework is proposed which models motion variations corresponding to a particular class of actions without the need for sequence length normalization. The motion descriptors used in this framework are based on the optical flow vectors computed at every point on the silhouette of the human body. Histogram of flow(HOF) is computed from the optical flow vectors and these give the motion orientation in a local neighborhood. To get a relationship between the motion vectors at a particular instant, the magnitude and direction of the optical flow vector are coded with local binary patterns(LBP). The concatenation of these histograms(HOF-LBP) are considered as the action feature set to be used in the proposed framework. We illustrate that this motion descriptor is suitable for classifying various human actions when used in conjunction with the proposed action recognition framework which models the motion variations in time for each class using regression based techniques. The feature vectors extracted from the training set are suitably mapped to a lower dimensional space using Empirical Orthogonal Functional Analysis. A regression based technique such as Generalized Regression Neural Networks(GRNN), are used to compute the functional mapping from the action feature vectors to its reduced Eigenspace representation for each class, thereby obtaining separate action manifolds. The feature set obtained from a test sequence are compared with each of the action manifolds by comparing the test coefficients with the ones corresponding to the manifold (as estimated by GRNN) to determine the class using Mahalanobis distance.

关键词： Generalized Regression Neural Networks Action Recognition Histogram of Flow local binary patterns General regression neural networks Optic Flow Regression (Psychology) test sequence Action Manifolds Videotapes motion vector binary pattern Frameworks

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High performance architecture for object detection in streamed video (abstract only) 13

High performance architecture for object detection in stream...

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Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays

作者： Pavel Zemčík Roman Juránek Petr Musil Martin Musil Michal Hradiš Brno University of Technology Brno Czech Rep

ISBN: (纸本)9781450318877

Object detection is one of the key tasks in computer vision. It is computationally intensive and it is reasonable to accelerate it in hardware. The possible benefits of the acceleration are reduction of the computational load of the host computer system, increase of the overall performance of the applications, and reduction of the power consumption. We present novel architecture for multi-scale object detection in video streams. The architecture uses scanning window classifiers produced by WaldBoost learning algorithm, and simple image features. It employs small image buffer for data under processing, and on-the-fly scaling units to enable detection of object in multiple scales. The whole processing chain is pipelined and thus more image windows are processed in parallel. We implemented the engine in Spartan 6 FPGA and we show that it can process 640x480 pixel video streams at over 160 frames per second without the need of external memory. The design takes only a fraction of resources, compared to similar state of the art approaches.

关键词： FPGA object detection local binary patterns waldboost stream processing local rank functions

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A Texture Feature Ranking Model for Predicting Survival Time of Brain Tumor Patients

A Texture Feature Ranking Model for Predicting Survival Time...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Mu Zhou Lawrence O. Hall Dmitry B. Goldgof Robert A.Gatenby Robert J.Gillies Department of Computer Science and Engineering University of South Florida Tampa Florida USA 33620 Department of Imaging H.Lee Moffitt Cancer and Research Institute Tampa Florida USA 33613

ISBN: (纸本)9781479906505

Automated prediction of patient-specific disease progression can significantly contribute to clinical treatment. This paper presents a computer-assisted framework to tackle the survival time prediction problem. Inspired by the assumption that niche tumor regions may play a significant role in cancer diagnosis, we explore local visual variations from multiple MRI sequences. The research consists of three parts: 1) the extraction of multi-scale local binary patterns (LBP) to describe the visual variations;2) a supervised forward feature selection approach, called the Feature Ranking Model (FRM) which captures single feature predictive ability efficiently, and combines the top features to form a feature subset;3) We cast the clinical survival time prediction task as a binary category classification problem. We tested the framework using a dataset of 32 cases collected from The Cancer Genome Atlas (TCGA). We obtained a 93.75% accuracy rate for the prediction of survival time.

关键词： Survival time prediction Brain tumor MRI GBM Feature Selection Support Vector Machine local binary patterns Ranking Relief-F t-test survival time Brain Neoplasms Feature extraction t-test Support Vector Network Glomerular Basement Membrane neoplasm diagnosis binary pattern C7orf49 gene Magnetic Resonance Imaging accuracy rate

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