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A NEW APPROACH FOR DIAGNOSTIC ESTIMATION OF OBSTRUCTIVE SLEEP APNEA SYNDROME BASED ON ONE DIMENSIONAL local binary PATTERN

A NEW APPROACH FOR DIAGNOSTIC ESTIMATION OF OBSTRUCTIVE SLEE...

引用

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

作者： Kaya, Yilmaz Sezgin, Necmettin Tekin, Ramazan Siirt Univ Bilgisayar Muhendisligi Bolumu Siirt Turkey Batman Univ Elekt Elekt Muhendisligi Bolumu Batman Turkey Batman Univ Bilgisayar Muhendisligi Bolumu Batman Turkey

ISBN: (纸本)9781479948741

In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional local binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG.

关键词： local binary patterns feature extraction EEG EMG Obstructive sleep apnea syndrome

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A Color and Texture Based Multi-level Fusion Scheme for Ethnicity Identification

A Color and Texture Based Multi-level Fusion Scheme for Ethn...

引用

Conference on Mobile Multimedia/Image Processing, Security, and Applications

作者： Du, Hongbo Salah, Sheerko Hma Ahmed, Hawkar O. Univ Buckingham Dept Appl Comp Buckingham MK18 1EG England Koya Univ KRG Ctr Sci Res Koy Sanjaq Iraq Univ Sulaimani Coll Commerce Dept Statist & Comp Sulaimani Iraq

ISBN: (纸本)9781628410570

Ethnicity identification of face images is of interest in many areas of application. Different from face recognition of individuals, ethnicity identification classifies faces according to the common features of a specific ethnic group. This paper presents a multi-level fusion scheme for ethnicity identification that combines texture features of local areas of a face using local binary patterns with color features using HSV binning. The scheme fuses the decisions from a k-nearest neighbor classifier and a support vector machine classifier into a final identification decision. We have tested the scheme on a collection of face images from a number of publicly available databases. The results demonstrate the effectiveness of the combined features and improvements on accuracy of identification by the fusion scheme over the identification using individual features and other state-of-art techniques.

关键词： local binary patterns HSV binning Support Vector Machine k Nearest Neighbors Fusion

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LBP and Machine Learning for Diabetic Retinopathy Detection

引用

15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： de la Calleja, Jorge Tecuapetla, Lourdes Auxilio Medina, Ma Barcenas, Everardo Urbina Najera, Argelia B. Univ Politecn Puebla Mexico City 72640 DF Mexico

ISBN: (纸本)9783319108407;9783319108391

Diabetic retinopathy is a chronic progressive eye disease associated to a group of eye problems as a complication of diabetes. This disease may cause severe vision loss or even blindness. Specialists analyze fundus images in order to diagnostic it and to give specific treatments. Fundus images are photographs taken of the retina using a retinal camera, this is a noninvasive medical procedure that provides a way to analyze the retina in patients with diabetes. The correct classification of these images depends on the ability and experience of specialists, and also the quality of the images. In this paper we present a method for diabetic retinopathy detection. This method is divided into two stages: in the first one, we have used local binary patterns (LBP) to extract local features, while in the second stage, we have applied artificial neural networks, random forest and support vector machines for the detection task. Preliminary results show that random forest was the best classifier with 97.46% of accuracy, using a data set of 71 images.

关键词： machine learning local binary patterns medical image analysis

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Real time algorithm invariant to natural lighting with LBP techniques through an adaptive thresholding implemented in GPU processors

Real time algorithm invariant to natural lighting with LBP t...

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Conference on Digital Photography X

作者： Orjuela-Vargas, S. A. Triana-Martinez, J. Yanez, J. P. Philips, W. Univ Antonio Narino Ibague Tolima Colombia Univ Ghent IPI IMINDS Dept Telecommun & Informat Proc TELIN B-9000 Ghent Belgium

ISBN: (纸本)9780819499400

Video analysis in real time requires fast and efficient algorithms to extract relevant information from a considerable number, commonly 25, of frames per second. Furthermore, robust algorithms for outdoor visual scenes may retrieve correspondent features along the day where a challenge is to deal with lighting changes. Currently, local binary Pattern (LBP) techniques are widely used for extracting features due to their robustness to illumination changes and the low requirements for implementation. We propose to compute an automatic threshold based on the distribution of the intensity residuals resulting from the pairwise comparisons when using LBP techniques. The intensity residuals distribution can be modelled by a Generalized Gaussian Distribution (GGD). In this paper we compute the adaptive threshold using the parameters of the GGD. We present a CUDA implementation of our proposed algorithm. We use the LBPSYM technique. Our approach is tested on videos of four different urban scenes with mobilities captured during day and night. The extracted features can be used in a further step to determine patterns, identify objects or detect background. However, further research must be conducted for blurring correction since the scenes at night are commonly blurred due to artificial lighting.

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

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Compactifying Multi-Dimensional LBP Variance Texture Descriptors based on DCT and Feature Reduction

Compactifying Multi-Dimensional LBP Variance Texture Descrip...

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International Conference on Multimedia Computing and Systems (ICMCS)

作者： Doshi, Niraj P. Schaefer, Gerald Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781479938247

Texture analysis and classification have received significant research interest and have been shown to be essential in many computer vision systems and applications. local binary patterns (LBP) are powerful yet simple texture descriptors which describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture description. Furthermore, local contrast information can be integrated into LBP leading to LBP variance (LBPV) features. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated resulting in a loss of information between different resolutions and added ambiguity. Multi-dimensional LBPV (MD-LBPV) preserves the relationships between the scales by building a multidimensional histogram of LBPV patterns and can lead to improved texture classification. In this paper, we address the relatively large feature length of MD-LBPV descriptors, and show that feature reduction based on discrete cosine transform (DCT) combined with principal component analysis (PCA) can yield effective and compact texture descriptors with high classification accuracy.

关键词： Texture local binary patterns LBP LBPV MDLBPV DCT

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Face Recognition Based on Histogram of the 2D-FrFT Magnitude and Phase

Face Recognition Based on Histogram of the 2D-FrFT Magnitude...

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International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)

作者： Wang, Yaxing Qi, Lin Guo, Xin Gao, Lei Zhengzhou Univ Zhengzhou 450000 Peoples R China

ISBN: (纸本)9781479931972

In face recognition, there are great challenges with variations arising from illumination, expression and other factors. Since the fractional Fourier transform feature is robust to illumination and expression variations and has been used in face recognition area, we propose a novel algorithm to face recognition with the local region histogram of the two dimensional fractional Fourier transform (2D-FrFT) magnitude and phase (LFMP). A face image is modeled as a "histogram sequence" by concatenating the histogram of all local regions of 2D-FrFT magnitude and phase binary pattern maps. The histogram intersection is used to measure the similarity of different LFMP binary pattern maps and the nearest neighborhood is exploited for final classification. We evaluate our approach on ORL and FERET face databases. Extensive experimental results verify the effectiveness of our LFMP descriptor.

关键词： face recognition 2D-FrFT local binary patterns fusion local histogram

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A Survey on Content Based Image Retrieval Using Various Operators

A Survey on Content Based Image Retrieval Using Various Oper...

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2014 International Conference on Computer Communication and Systems (ICCCS)

作者： Bhagyalakshmi, A. Vijayachamundeeswari, V. Velammal Engn Coll CSE Dept Madras Tamil Nadu India

ISBN: (纸本)9781479936717

Retrieval of images plays a major role in different domains including medical diagnosis, biometrics, industry inspection, geographical information satellite systems, web searching and historical research and so on. When size of the database increases continuously, the applications involving images face new challenges and crucial problems such as storage management, indexing, knowledge management and retrieval presentation. We need an efficient retrieval mechanism to retrieve images from the multimedia database. CBIR- Content-based image retrieval is an image retrieval technique used to retrieve images efficiently by using low level image features texture, shape and color. In CBIR system, an image query is characterised by primitive, logical features and abstract attributes. Queries with primitive features are directly derivable from the images but logical and abstract features require logical inference and objects semantic reasoning. This survey paper focuses on different features descriptors for image retrieval and analysis of various retrieval operators like LBP-local binary patterns, LTP-local Ternary patterns, LDP-local Derivative patterns and LTrP-local Tetra patterns using high level features to improve the performance and accuracy in CBIR system.

关键词： Content based image retrieval local binary patterns local Ternary patterns local Derivative patterns local Tetra patterns

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Implementation of Object Recognition based on local Texture Operators in FPGA Technology

Implementation of Object Recognition based on Local Texture ...

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1st International Conference on Advancements in Automation and Control (ICAAC)

作者： Ranjini, M. Monica Dhana Parthiban, P. Gnana Skanda Prabhakar, G. Rajithkhan, A. Mohamed Jeyaraj, M. Paul Syed Ammal Engn Coll Ramanathapuram Tamil Nadu India

ISBN: (纸本)9783038351245

In the arrival of today's highly integrated multimedia device and fast emerging applications, image processing have become more important than any others. These multimedia devices require complex image processing tasks lead to a very challenge design process as it demands more efficient and high processing systems. The scope of this project is to extract features of multi objects in an image for object recognition. The proposed system is designed in FPGA for low power consumption and better performance. In existing system a cell based multi object feature extraction algorithm is used to extract autocorrelation feature of objects simultaneously. It is calculated using zeroth order and first order moments to obtain the size and location of multiple objects. In proposed system a local binary Pattern (LBP) and local Ternary Pattern (LTP) is used to extract the feature of multiple objects to reduce computational complexity and memory consumption. An EDDR(Error Detection and Data recovery) circuit is used to detect error and recover the data of an image. The proposed architecture is designed using verilog HDL, simulated using Modelsim software and synthesized using Xilinx project navigator.

关键词： Object Recognition local binary patterns local Ternary patterns Error Detection and Data Recovery

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Texture Descriptors Based on Dijkstra's Algorithm for Medical Image Analysis 2

Texture Descriptors Based on Dijkstra's Algorithm for Medica...

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2nd KES International Conference on Innovation in Medicine and Healthcare (InMed)

作者： Ghidoni, Stefano Nanni, Loris Brahnam, Sheryl Menegatti, Emanuele Univ Padua DEI Viale Gradenigo 6 Padua Italy Missouri State Univ CIS Springfield MO USA

ISBN: (纸本)9781614994749;9781614994732

In this paper we investigate a new approach for extracting features from a texture using Dijkstra's algorithm. The method maps images into graphs and gray level differences into transition costs. Texture is measured over the whole image comparing the costs found by Dijkstra's algorithm with the geometric distance of the pixels. In addition, we compare and combine our new strategy with a previous method for describing textures based on Dijkstra's algorithm. For each set of features, a support vector machine (SVM) is trained. The set of classifiers is then combined by weighted sum rule. Combining the proposed set of features with the well-known local binary patterns and local ternary patterns boosts performance. To assess the performance of our approach, we test it using six medical datasets representing different image classification problems. Tests demonstrate that our approach outperforms the performance of standard methods presented in the literature. All source code for the approaches tested in this paper will be available at: http://***/node/2357.

关键词： texture descriptors local binary patterns ensemble support vector machines

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local binary Pattern based Facial Expression Recognition using Self-organizing Map

Local Binary Pattern based Facial Expression Recognition usi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Majumder, Anima Behera, Laxmidhar Subramanian, Venkatesh K. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781479914845

This paper presents an appearance feature based facial expression recognition system using Kohonen Self-Organizing Map (KSOM). Appearance features are extracted using uniform local binary patterns (LBPs) from equally sub-divided blocks applied over face image. The dimensionality of the LBP feature vector is further reduced using principal component analysis (PCA) to remove the redundant data that leads to unnecessary computation cost. Using our proposed KSOM based classification approach, we train only 59 dimensional LBP features extracted from whole facial region. The classifier is designed to categorize six basic facial expressions (happiness, sadness, disgust, anger, surprise and fear). To validate the performance of the reduced 59 dimensional LBP feature vector, we also train the original data of dimension 944 using the KSOM. The results demonstrates, that with marginal degradation in overall recognition performance, the reduced 59 dimensional data obtains very good classification results. The paper also presents three more comparative studies based on widely used classifiers like;Support vector machine (SVM), Radial basis functions network (RBFN) and Multi-layer perceptron (MLP3). Our KSOM based approach outperforms all other classification methods with average recognition accuracy 69.18%. Whereas, the average recognition rated obtained by SVM, RBFN and MLP3 are 65.78%, 68.09% and 62.73% respectively.

关键词： Self-organizing map Facial expression recognition local binary patterns Principal component analysis Support vector machine Multi-layer perceptron Radial basis function

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