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检索条件"主题词=Local binary patterns"

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Glaucoma Detection based on local binary patterns in Fundus Photographs

Glaucoma Detection based on Local Binary Patterns in Fundus ...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Ali, Maya Alsheh Hurtut, Thomas Faucon, Timothee Cheriet, Farida Univ Paris 05 LIPADE Paris France Ecole Polytechn Montral Montreal PQ Canada CDIAGNOS inc Montreal PQ Canada

ISBN: (纸本)9780819498281

Glaucoma, a group of diseases that lead to optic neuropathy, is one of the most common reasons for blindness worldwide. Glaucoma rarely causes symptoms until the later stages of the disease. Early detection of glaucoma is very important to prevent visual loss since optic nerve damages cannot be reversed. To detect glaucoma, purely data-driven techniques have advantages, especially when the disease characteristics are complex and when precise image-based measurements are difficult to obtain. In this paper, we present our preliminary study for glaucoma detection using an automatic method based on local texture features extracted from fundus photographs. It implements the completed modeling of local binary patterns to capture representative texture features from the whole image. A local region is represented by three operators: its central pixel (LBPC) and its local differences as two complementary components, the sign (which is the classical LBP) and the magnitude (LBPM). An image texture is finally described by both the distribution of LBP and the joint-distribution of LBPM and LBPC. Our images are then classified using a nearest-neighbor method with a leave-one-out validation strategy. On a sample set of 41 fundus images (13 glaucomatous, 28 non-glaucomatous), our method achieves 95.1 % success rate with a specificity of 92.3 % and a sensitivity of 96.4 %. This study proposes a reproducible glaucoma detection process that could be used in a low-priced medical screening, thus avoiding the inter-experts variability issue.

关键词： Glaucoma texture features computer aided diagnosis local binary patterns Fundus photographs

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Using local binary patterns in Speckle Image Analysis 41

Using Local Binary Patterns in Speckle Image Analysis

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41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Bento, L. Tavora, L. Assuncao, P. Faria, S. Fonseca-Pinto, R. Inst Telecomunicacoes Multimedia Signal Proc Leiria Portugal Polytech Inst Leiria Leiria Portugal

ISBN: (纸本)9789532330953

Firstly described by Newton in the 17th century, speckle is an optical phenomenon which can be translated into image patterns produced by wave interferences of diffused reflections. In fact, the speckle pattern is generated by the multiple interference phenomena that occur when a rough surface is illuminated with a coherent source of light, producing randomly distributed reflected waves of the same frequency but different phases and amplitudes. Although it has been known for a long time, capturing video sequences of speckle patterns was dependent on recent technological developments, in particular, related to laser technology and microsensors. The speckle acquisition setup comprises a light source, usually a laser, an optical beam expander and a CCD camera. The generated interference patterns are captured in series of video sequences, to further be processed. In previous works, several image processing algorithms have been applied to analyze video frames of speckle, aimed to capture the evolution patterns in dynamic processes. However, due to the typical high frequencies of the changing patterns, classical texture algorithms mostly fail this goal. In this work, speckle dynamics are evaluated using local binary patterns (LBP) jointly with some of its main variants and a newly proposed algorithm, in a reactive hyperemia controlled test. The proposed methodology goes beyond the traditional implementations of LBPs, by considering an additional Gaussian filtering, a methodology thus coined as LBPg. The results, on one hand, confirm that the classical formulations of LBP are not sensitive to changes in the simulated patterns but, on the other hand, demonstrate that the newly proposed LBP-adapted algorithm successfully identify the dynamics of the processes under study.

关键词： local binary patterns Laser Speckle Video Processing Computer Vision

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Automatic Gleason Grading of Prostate Cancer using Gabor Filter and local binary patterns 40

Automatic Gleason Grading of Prostate Cancer using Gabor Fil...

引用

40th International Conference on Telecommunications and Signal Processing (TSP)

作者： Farooq, Muhammad Tahir Shaukat, Arslan Akram, Usman Waqas, Omer Ahmad, Masood Natl Univ Sci & Technol Islamabad Pakistan Shaukat Khanum Mem Canc Hosp & Res Ctr Lahore Pakistan

ISBN: (纸本)9781509039821

Prostate Cancer is one of the most common types of cancer found in men aged over 40 years. Detection and staging is the most critical step for pathologists. This research supports the development of Computer Aided Detection system capable of grading prostate cancer with high accuracy and less human involvement. Real patient dataset collection is a challenge, we collected real and graded dataset from Shaukat Khanum Cancer Research Hospital, Pakistan. Texture feature sets are extracted using Gabor and local binary patterns with different variations. The proposed system showed an improved accuracy due to fusion of different texture features. The goal of this study is to grade the E&H stained histological images into benign, grade 3, grade 4 or grade 5. K-Nearest Neighbor classifier is used and dataset is divided randomly into training and testing using 10-fold cross validation. The proposed system shows overall accuracy of 98.3% for real dataset of 268 histological E&H images collected from 160 different patients at different times.

关键词： E&H histological images Gabor features Gleason Grading local binary patterns Prostate Cancer

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Robust ASL Fingerspelling Recognition Using local binary patterns And Geometric Features

Robust ASL Fingerspelling Recognition Using Local Binary Pat...

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Weerasekera, C. S. Jaward, M. H. Kamrani, N. Monash Univ Dept Elect & Comp Syst Engn Clayton Vic Australia

ISBN: (纸本)9781479921263

Sign language recognition using computer vision techniques enables machines to function as interpreters of sign language while eliminating the need for cumbersome data gloves. In this paper, a robust approach for recognition of bare-handed static sign language is presented, using a novel combination of features. These include local binary patterns (LBP) histogram features based on color and depth information, and also geometric features of the hand. Linear binary Support Vector Machine (SVM) classifiers are used for recognition, coupled with template matching in the case of multiple matches. An accurate hand segmentation scheme using the Kinect depth sensor is also presented. The resulting sign language recognition system could be employed in many practical scenarios and works in complex environments in real-time. It is also shown to be robust to changes in distance between the user and camera and can handle possible variations in fingerspelling among different users. The algorithm is tested on two ASL fingerspelling datasets where overall classification rates over 90% are observed.

关键词： Sign Language Recognition local binary patterns Support Vector Machines Microsoft Kinect

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Fuzzy local binary patterns for ultrasound texture characterization

引用

5th International Conference on Image Analysis and Recognition

作者： Iakovidis, Dimitris K. Keramidas, Eystratios G. Maroulis, Dimitris Univ Athens Dept Informat & Telecommun GR-15784 Athens Greece

ISBN: (纸本)9783540698111

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the local binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy local binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.

关键词： fuzzy local binary patterns ultrasound thyroid nodules support vector machines

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Hyperspectral Image Classification Based on local binary patterns and PCANet 9

Hyperspectral Image Classification Based on Local Binary Pat...

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： Yang, Huizhen Gao, Feng Dong, Junyu Yang, Yang Ocean Univ China Dept Comp Sci & Technol Qingdao Peoples R China Ocean Univ China Law & Polit Sch Qingdao Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

关键词： local binary patterns hyperspectral image pattern classification principal component analysis convolutional network

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No-Reference Image Quality Assessment Using local binary patterns: A Comprehensive Performance Evaluation 3

No-Reference Image Quality Assessment Using Local Binary Pat...

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3rd Workshop on Quality of Experience in Visual Multimedia Applications (QoEVMA)

作者： Zhou, Zihan Xu, Yong Wan, Xi Quan, Yuhui Xu, Ruotao Li, Jing Le Callet, Patrick South China Agr Univ Guangzhou Peoples R China Inst Super Robot Guangzhou Peoples R China Alibaba Grp Moku Lab Beijing Peoples R China Nantes Univ Nantes France PaZhou Lab Guangzhou Guangzhou Peoples R China Guangdong Prov Key Lab Multimodal Big Data Intell Guangzhou Peoples R China

ISBN: (纸本)9798400712043

One key in image quality assessment (IQA) is the design of image representations that can capture the changes in image structures caused by distortions. In the last decades, local binary patterns (LBP) have been proven to be a powerful tool as a statistical model for texture and local structure representation. LBP and its variants, as the texture descriptor with low computational complexity, have been applied widely and successfully in several specific applications, such as texture classification, face recognition and IQA. Generally, visible impairments alter the statistics of LBP descriptors, making it possible to measure degradation and then estimate image quality. However, only a few variants of LBP are applied in no-reference (NR) IQA and a few characteristics of LBP descriptors are explored for quality prediction, while some characteristics useful in the rest variants are ignored for IQA. Two previous studies give a review of 16/4 LBP operators and compare the performance with them separately in IQA application only on synthetic distorted data. To extend this work, we provide a review of LBP methodologies to assist the scientific community and new researchers, as well as explore more LBP descriptors in NR-IQA methods under various distortion conditions, particularly real-world cases. Specifically, we comprehensively review 30 widely-used or effective LBP-based operators, including recent variations. We then utilize a common framework for applying LBP descriptors in NR-IQA. The practicality of the reviewed descriptors is demonstrated and analyzed using experimental results under synthetic and authentic cases, indicating suitable LBP descriptors and characteristics for NR-IQA. Codes implementing the reviewed LBP operators is available at https://***/csxwan/local-binary-patterns-for-IQA.

关键词： No-Reference Image Quality Assessment local binary patterns Variants Texture Descriptors

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A comprehensive study of texture analysis based on local binary patterns

A comprehensive study of texture analysis based on local bin...

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Conference on Optics, Photonics and Digital Technologies for Multimedia Applications II

作者： Nava, Rodrigo Cristobal, Gabriel Escalante-Ramirez, Boris Univ Nacl Autonoma Mexico Posgrado Ciencia &Ingn Computac Mexico City 04510 DF Mexico CSIC Inst Opt Madrid 28006 Spain Univ Nacl Autonoma Mexico Fac Ingn Dept Procesamiento Senales Mexico City DF Mexico

ISBN: (纸本)9780819491282

One of the main goals of texture analysis is to provide a robust mathematical description of the spatial behavior of intensity values in any given neighborhood. These local distributions called textures characterize object surfaces and are used for pattern identification and recognition of images. However, some spatial patterns may vary from quite simple stripes to randomness, where textures look like unstructured noise. Since textures can exhibit a large number of properties such as surface materials and geometry of the lighting sources, many different approaches have been proposed. A featured method is the modification of Wang's algorithm made by Ojala et al, the so-called local binary patterns (LBP). The LBP algorithm uses a 3 x 3 square mask named "texture spectrum" which represents a neighborhood around a central pixel. The values in the square mask are compared with the central pixel and then multiplied by a weighting function according with their positions. This technique has become popular due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. Specially, it has gained increased importance in image classification, where the success not only depends on a robust classifier but also relies in a good selection of the feature descriptors. However, Ojala's algorithm presents some limitations such as noise sensitivity and lack of invariance to rotational changes. This fact has fostered many extensions of the original LBP approach that in many cases are based on minor changes in order to attain e.g. illumination and rotational invariance or improving the robustness to noise. In this paper we present a detailed overview of the LBP algorithm and other recently modifications. In addition, we perform a texture classification study with seven algorithms in presence of rotational changes, noise degradation, contrast information, and different sizes of LBP masks using the USC-SIPI database. The LBP histograms have

关键词： local binary patterns texture analysis texture classification distance measure USI-SIPI Brodatz

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GENDER CLASSIFICATION WITH local ZERNIKE MOMENTS AND local binary patterns

GENDER CLASSIFICATION WITH LOCAL ZERNIKE MOMENTS AND LOCAL B...

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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Coban, Bahtiyar Samet Gokmen, Muhittin Istanbul Tech Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781479948741

This study provides a new feature extraction method to gender classification. local Zernike Moments is a method used for face recognition and proved that it is more successful than Gabor or LBP representations. In this study, LZM method is used for gender classification on FERET and LFW databases and demonstrated that it is more successful than LBP method on both databases. In the light of analysis done on the test results of these two methods, a new hybrid feature method built by combining LZM and LBP features is created and the performance rates are achieved as 99.57% for FERET and 97.71% for LFW databases by using Support Vector Machines (SVM) classifier. This indicates the superiority of the proposed method over suggested methods for gender classification on both controlled environment and real-world images.

关键词： local Zernike Moments local binary patterns Gender Classification Support Vector Machines FERET LFW

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A novel approach for Drowsiness Detection using local binary patterns and Histogram of Gradients

A novel approach for Drowsiness Detection using Local Binary...

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International Conference on Electrical and Computing Technologies and Applications (ICECTA)

作者： Rajevenceltha, J. Gaidhane, Vilas H. Anjana, V. Birla Inst Technol & Sci Dept Elect & Elect Engn Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9781728155326

Driver fatigue is the main cause for lack of attention and drowsiness while driving. This may lead to road accidents;car crashes and fatalities. Hence, detection of drowsiness is highly essential to prevent these accidents. In this paper, a highly accurate drowsiness detection system which alerts the driver about the exhaustion while driving is presented. A novel drowsiness detection system based on face detection, eye and mouth movement detection is developed. By analysing the significant features such as opening and closing of the eyelids, yawning and head movements, the system alerts the driver. From the experimental results, it is observed that the developed system outperforms other best performing systems which are presented in the literature.

关键词： Drowsiness face detection local binary patterns image processing tracking

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