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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...

引用

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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Classification of Clothing with Weighted SURF and local binary patterns 19

Classification of Clothing with Weighted SURF and Local Bina...

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International Computer Science and Engineering Conference (ICSEC)

作者： Surakarin, Wisarut Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781467378253

This work proposes a recognition system for clothing classification by computer vision. The input is an image of the type of fashion catalog where the clothes are fully exposed with models showing their faces. For the preprocessing and features extraction the Bag of Features (BoF) is employed. There are four steps in the proposed classification method: (i) the cloth in an image is identified and located, then it is segmented by GrabCut;(ii) the area in the image of cloth is divided into three sub-windows (right side, middle, and left side);(iii) the feature extraction, Speed-Up Robust Features (SURF) and local binary patterns (LBP) are applied to each sub-window to create a codebook;(iv) the classification is done by Support Vector Machine (SVM). Our dataset consists of total 1131 images out of which the training set is 991 images and the remainder is the testing set. We separate types into seven categories of clothing image which included, jacket, shirt, suit, sweater, t-shirt, poloshirt and tank top. The result of the experiment illustrates that the proposed method can recognize types of clothing images accurately 73.57%.

关键词： Bag of Features local binary patterns clothing recognition and clothing classification

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Enhanced Line local binary patterns (EL-LBP): An Efficient Image Representation for Face Recognition 19th

Enhanced Line Local Binary Patterns (EL-LBP): An Efficient I...

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19th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS)

作者： Hung Phuoc Truong Kim, Yong-Guk Sejong Univ Dept Comp Engn Seoul South Korea Univ Sci Dept Comp Sci VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9783030014490;9783030014483

local binary patterns (LBP) is one of the efficient approaches for image representation, especially in the face recognition field. The motivation of the present study is to find a compact descriptor which captures texture information and yet is robust against several visual challenges such as illumination variation, facial expressions and head pose variation. The proposed approach, called it Enhance Line local binary patterns (EL-LBP), is an improvement of 1D-local binary patterns (1D-LBP) by reducing the dimension of feature vectors within 1D-LBP histogram and it leads to decrease the time cost during the matching stage. Experiments using ORL, Yale and AR datasets show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.

关键词： local binary patterns Line local binary patterns Face recognition Short bins in histogram YALE ORL AR

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A Comparison of Classification Methods for local binary patterns 24

A Comparison of Classification Methods for Local Binary Patt...

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24th Signal Processing and Communication Application Conference (SIU)

作者： Kazak, Nihan Koc, Mehmet Benligiray, Burak Topal, Cihan Bilecik Seyh Edebali Univ Bilgisayar Muh Bol Bilecik Turkey Bilecik Seyh Edebali Univ Elek Elekt Muh Bol Bilecik Turkey Anadolu Univ Elek Elekt Muh Bol Eskisehir Turkey

ISBN: (纸本)9781509016792

Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.

关键词： texture classification local binary patterns classification methods UIUC texture database

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OBJECT DETECTION USING NON-REDUNDANT local binary patterns

OBJECT DETECTION USING NON-REDUNDANT LOCAL BINARY PATTERNS

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IEEE International Conference on Image Processing

作者： Duc Thanh Nguyen Zong, Zhimin Ogunbona, Philip Li, Wanqing Univ Wollongong Sch Comp Sci & Software Engn ICT Res Inst Adv Multimedia Res Lab Wollongong NSW 2522 Australia

ISBN: (纸本)9781424479948

local binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant local binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object's appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human's appearance in a human detection task. Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.

关键词： Human detection local binary patterns

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Combining Contrast Information and local binary patterns for Gender Classification 1

引用

17th Scandinavian Conference on Image Analysis (SCIA)

作者： Ylioinas, Juha Hadid, Abdenour Pietikainen, Matti Univ Oulu Machine Vis Grp FI-90014 Oulu Finland

ISBN: (数字)9783642212277

ISBN: (纸本)9783642212277

Recent developments in face analysis showed that local binary patterns (LBP) provide excellent results in representing faces. LBP is by definition a purely gray-scale invariant texture operator, codifying only the facial patterns while ignoring the magnitude of gray level differences (i.e. contrast). However, pattern information is independent of the gray scale, whereas contrast is not. On the other hand, contrast is not affected by rotation, but patterns are, by default. So, these two measures can supplement each other. This paper addresses how well facial images can be described by means of both contrast information and local binary patterns. We investigate a new facial representation which combines both measures and extensively evaluate the proposed representation on the gender classification problem, showing interesting results. Furthermore, we compare our results against those of using Haar-like features and AdaBoost learning, demonstrating improvements with a significant margin.

关键词： Texture Features local binary patterns Contrast Gender Classification

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One Dimensional Multi-Resolution local binary patterns Features (1DMRLBP) for Regular Electrocardiogram (ECG) Waveform Detection 19

One Dimensional Multi-Resolution Local Binary Patterns Featu...

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19th International Conference on Digital Signal Processing (DSP)

作者： Louis, Wael Hatzinakos, Dimitrios Venetsanopoulos, Anastasios Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781479946129

Feeding a noisy signal to a biometric system degrades its performance. Hence, signal quality measure is used to avoid passing irregular signals to subsequent systems such as biometric systems. To tackle this issue, 1DMRLBP features, which are 1 dimensional signal feature extraction (inspired by the 2 dimensional image local binary patterns) is proposed. 1DMRLBP with its multi-resolution capability captures local and global signal characteristics;and with its histogram extraction avoids segments misalignment and reduces the number of features. Also with some modifications, 1DMRLBP accommodates the problem of unknown amplitude of a signal. 1DMRLBP achieves 91% performance rate in distinguishing between regular and irregular ECG waveforms. MATLAB code and more information are available at ***/similar to wlouis/1DMRLBP.

关键词： Biomedical signal detection ECG detection local binary patterns Signal quality measure

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Leaf Recognition Algorithm using Support Vector Machine with Hu Moments and local binary patterns 15

Leaf Recognition Algorithm using Support Vector Machine with...

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15th IEEE International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Lukic, Marko Tuba, Eva Tuba, Milan John Naisbitt Univ Grad Sch Comp Sci Bulevar Umetnosti 29 Belgrade Serbia Univ Belgrade Fac Math Studentski Trg 16 Belgrade Serbia

ISBN: (纸本)9781509056552

Leaf recognition is convenient for plant classification and it is an important subfield of pattern recognition. Different leaf features such as color, shape and texture are used as well as different classifiers including artificial neural networks, k-nearest neighbor and support vector machines. In this paper we propose an algorithm based on tuned support vector machine as a classifier and Hu moments and uniform local binary pattern histogram parameters as features. Our proposed algorithm was tested on leaf images from standard benchmark database and compared with other approaches from literature where it proved to be more successful (higher recognition percentage).

关键词： Leaf recognition plant classification support vector machines Hu moments local binary patterns

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Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features

Automatic organ localizations on 3D CT images by using major...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Zhou, Xiangrong Yamaguchi, Shoutarou Zhou, Xinin Chen, Huayue Hara, Takeshi Yokoyama, Ryujiro Kanematsu, Masayuki Fujita, Hiroshi Gifu Univ Grad Sch Med Div Regenerat & Adv Med Sci Dept Intelligent Image Informat Gifu 5011194 Japan

ISBN: (纸本)9780819494443

This paper describes an approach to accomplish the fast and automatic localization of the different inner organ regions on 3D CT scans. The proposed approach combines object detections and the majority voting technique to achieve the robust and quick organ localization. The basic idea of proposed method is to detect a number of 2D partial appearances of a 3D target region on CT images from multiple body directions, on multiple image scales, by using multiple feature spaces, and vote all the 2D detecting results back to the 3D image space to statistically decide one 3D bounding rectangle of the target organ. Ensemble learning was used to train the multiple 2D detectors based on template matching on local binary patterns and Haar-like feature spaces. A collaborative voting was used to decide the corner coordinates of the 3D bounding rectangle of the target organ region based on the coordinate histograms from detection results in three body directions. Since the architecture of the proposed method (multiple independent detections connected to a majority voting) naturally fits the parallel computing paradigm and multi-core CPU hardware, the proposed algorithm was easy to achieve a high computational efficiently for the organ localizations on a whole body CT scan by using general-purpose computers. We applied this approach to localization of 12 kinds of major organ regions independently on 1,300 torso CT scans. In our experiments, we randomly selected 300 CT scans (with human indicated organ and tissue locations) for training, and then, applied the proposed approach with the training results to localize each of the target regions on the other 1,000 CT scans for the performance testing. The experimental results showed the possibility of the proposed approach to automatically locate different kinds of organs on the whole body CT scans.

关键词： Torso CT images Organ localization Object detection Majority voting Haar-like feature local binary patterns

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STATISTICAL local binary patterns (SLBP): APPLICATION TO PROSTATE CANCER GLEASON SCORE PREDICTION FROM WHOLE SLIDE PATHOLOGY IMAGES 16

STATISTICAL LOCAL BINARY PATTERNS (SLBP): APPLICATION TO PRO...

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Xu, Hongming Hwang, Tae Hyun Cleveland Clin Dept Quantitat Hlth Sci Cleveland OH 44106 USA

ISBN: (纸本)9781538636411

Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more micro- and macrostructure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade <= 7 vs grade >= 8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-the-art LBP variants.

关键词： pathology image analysis prostate cancer grading local binary patterns

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