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23rd KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Agbele, Tobechukwu Ojeme, Blessing Jiang, Richard Univ Lancaster Dept Comp Sci Lancaster LA1 4YW England Univ Cape Town Dept Comp Sci CAIR ZA-7701 Rondebosh South Africa CAIR Pretoria South Africa

The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist's analytical toolkit. 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.

关键词： computer vission machine learning mice behavioural pattern detection local binary patterns cascade AdaBoost classifier

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Recaptured image detection based on convolutional neural networks with local binary patterns coding 4

Recaptured image detection based on convolutional neural net...

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4th International Workshop on Pattern Recognition (IWPR)

作者： Zhu, Nan Qin, Minying Yin, Yuting Xian Technol Univ Dept Elect Informat Engn Xian 710021 Shaanxi Peoples R China

ISBN: (数字)9781510631144

ISBN: (纸本)9781510631144

With the great development of image display technology and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relative convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, inspired by the effectiveness of LBP (local binary pattern) on recaptured image detection and the satisfactory performance of deep learning techniques on many image forensics tasks, we propose a recaptured image detection method based on convolutional neural networks with local binary patterns coding. The LBP coded maps are extracted as the input of the proposed convolutional neural networks architecture. Extensive experiments on two public high-quality recaptured image databases under two different scenarios demonstrate the superior of our designed method when compared with the state-of-the-art approaches.

关键词： Image forensics recaptured image detection bio-authentication deep learning local binary patterns

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Enhanced local binary patterns for Automatic Face Recognition 18th

Enhanced Local Binary Patterns for Automatic Face Recognitio...

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18th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Kral, Pavel Vrba, Antonin Lenc, Ladislav Univ West Bohemia Fac Appl Sci Dept Comp Sci & Engn Plzen Czech Republic Univ West Bohemia Fac Appl Sci NTIS Plzen Czech Republic

ISBN: (纸本)9783030209155;9783030209148

This paper presents a novel automatic face recognition approach based on local binary patterns. This descriptor considers a local neighbourhood of a pixel to compute the feature vector values. This method is not very robust to handle image noise, variances and different illumination conditions. We address these issues by proposing a novel descriptor which considers more pixels and different neighbourhoods to compute the feature vector values. The proposed method is evaluated on two benchmark corpora, namely UFI and FERET face datasets. We experimentally show that our approach outperforms state-of-the-art methods and is efficient particularly in the real conditions where the above mentioned issues are obvious. We further show that the proposed method handles well one training sample issue and is also robust to the image resolution.

关键词： E-LBP Enhanced local binary patterns Face recognition local binary patterns LBP

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Hyperspectral Image Classification Based on local binary patterns and SVDNet 10

Hyperspectral Image Classification Based on Local Binary Pat...

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10th International Conference on Graphics and Image Processing (ICGIP)

作者： Sun, Mengjie Gao, Feng Dong, Junyu Ocean Univ China Sch Informat Sci & Engn Qingdao Shandong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Hyperspectral image classification is a critical issue in hyperspectral data processing. However, the task has been acknowledged as extremely challenging due to its characteristics including high dimensionality in data, spatial variability of spectral features and scarcity of marked data. In this paper, we propose a new classification method combined with local binary patterns (LBP) and Singular Value Decomposition Networks (SVDNet). Linear Prediction Error is first employed to select informative spectral bands. Then LBP is utilized to extract the texture features. After that, the extracted features of a specified field are transformed to 2-D images. Finally, SVDNet classifies the obtained images and then the classification result can be obtained. Experimental results on the real hyperspectral dataset demonstrate that the proposed method is capable to achieve higher classification accuracy or at least comparable to existing methods.

关键词： local binary patterns singular value decomposition hyperspectral image classification convolutional neural network

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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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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...

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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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Classification of Cell Types on Histopathological Images Using local binary patterns

Classification of Cell Types on Histopathological Images Usi...

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Medical Technologies Congress (TIPTEKNO)

作者： Ozer, Hatice Sumeyye Demir, Ceyda Bilgin, Gokhan Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu TR-34220 Istanbul Turkey

ISBN: (纸本)9781728124209

In recent years, the use of computer aided diagnostic (CAD) systems has been increasing with a high acceleration in the field of digital pathology. Application and study areas are expanding over time include the detection, classification and segmentation of nuclei. In this study, various traditional machine learning methods (k-closest neighborhood, random forests and support vector machines) and deep learning (convolutional neural network) were used comparatively on CRC colorectal adenocarcinomas dataset. Since conventional machine learning algorithms do not receive a two-dimensional input such as convolutional neural network, local binary images are utilized. As a result, when the feature extraction for machine learning algorithms is performed, KNN and RF algorithms provide very successful results, whereas CNN algorithm gave better results without making any feature extraction.

关键词： Hisopathological images convolutional neural network classification local binary patterns

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binary Tomography Using Variants of local binary patterns as Texture Priors 18th

Binary Tomography Using Variants of Local Binary Patterns as...

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18th international Conference on Computer Analysis of Images and patterns (CAIP)

作者： Szucs, Judit Balazs, Peter Univ Szeged Dept Image Proc & Comp Graph Arpad Ter 2 H-6720 Szeged Hungary

ISBN: (纸本)9783030298883;9783030298876

In this paper, we propose a novel approach for binary image reconstruction from few projections. The binary reconstruction problem can be highly underdetermined and one way to reduce the search space of feasible solutions is to exploit some prior knowledge of the image to be reconstructed. We use texture information extracted from sample image patches as prior knowledge. Experimental results show that this approach can retain the structure of the image even if just a very few number of projections are used for the reconstruction.

关键词： binary Tomography Image reconstruction Texture descriptors local binary patterns Simulated annealing

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local binary patterns for Graph Characterization 24

Local Binary Patterns for Graph Characterization

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24th International Conference on Pattern Recognition (ICPR)

作者： Jawad, Muhammad Aziz, Furqan Hancock, Edwin Inst Management Sci Peshawar Pakistan Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781538637883

In this paper we propose a novel approach for defining local binary patterns (LBP) to directly encode graph structure. LBP is a simple and widely used technique for texture analysis in static 2D images, and there is no work in the literature describing its generalisation to graphs. The proposed method (GraphLBP) is efficient and yet effective as a noise-tolerant graph-based representation. We compute the new feature representation for graphs by combining LBP with Galois Fields, using irreducible polynomials. The proposed method is scalable as it preserves the local and global properties of the graph. Experimental results show that GraphLBP can both increase the recognition accuracy and is both simpler and more computationally efficient when compared with state of the art techniques.

关键词： Graph Characterization local binary patterns Galois Fields

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Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns

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INFORMATION SCIENCES 2018年 460卷 202-222页

作者： Yuan, Feiniu Xia, Xue Shi, Jinting Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Agr Univ Vocat Sch Teachers & Technol Nanchang 330045 Jiangxi Peoples R China

local binary patterns (LBP) have powerful discriminative capabilities. However, traditional methods with LBP histograms cannot capture spatial structures of LBP codes. To extract the spatial structures of an LBP code map, we compute and encode the Hamming distances between LBP codes of a center point and its neighbors on the LBP code map to generate a new code, which is called Hamming-distance-based local binary patterns (HDLBP). Then, we calculate a joint histogram of LBP and HDLBP to represent the LBP co-occurrence with HDLBP (LBPCoHDLBP). Circular bit-wise shift techniques are used to align HDLBP with LBP for rotation invariance. To achieve scale invariance, we extract the feature of LBPCoHDLBP from each scale and concatenate all features of different scales. Finally, we use the sum of absolute differences (SAD) between the intensities of the center point and its neighbors to weight LBPCoHDLBP for further improvement. Extensive experiments show that our method achieves better performance for smoke detection, texture classification and material recognition than most existing methods and is more computationally efficient. (C) 2018 Elsevier Inc. All rights reserved.

关键词： local binary patterns Hamming distance Mixed co-occurrence Smoke detection Image classification

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