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40th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Demir, Fatih Sengur, Abdulkadir Cummins, Nicholas Amiriparian, Shahin Schuller, Bjoern Firat Univ Technol Fac Elect & Elect Engn Dept Elazig Turkey Univ Augsburg Chair Embedded Intelligence Hlth Care & Wellbeing Augsburg Germany Tech Univ Munich Machine Intelligence & Signal Proc Grp Munich Germany Imperial Coll London GLAM Grp Language Audio & Mus London England

ISBN: (纸本)9781538636466

Snoring is often associated with serious health risks such as obstructive sleep apnea and heart disease and may require targeted surgical interventions. In this regard, research into automatically and unobtrusively analysing the site of blockages that cause snore sounds is growing in popularity. Herein, we investigate the use of low level image texture features in classification of four specific types of snore sounds. Specifically, we explore histogram of local binary patterns (LBP) in dense grid of rectangular regions and histogram of oriented gradients (HOG) extracted from colour spectrograms for snore sound characterisation. Support vector machines with homogeneous mapping are used in the classification stage of the proposed method. Various experimental works are carried out with both LBP and HOG descriptors on the INTERSPEECH ComParE 2017 snoring sub-challenge dataset. Results presented indicate that LBP descriptors are better than the HOG descriptors in snore type detection and fusion of the LBP and HOG descriptors produces stronger results than either individual descriptor. Further, when compared to the challenge baseline and state-of-the-art deep spectrum features, our approach achieved relative percentage increases in unweighted average recall of 23.1% and 8.3% respectively.

关键词： Snore sound classification audio spectrograms low level texture features local binary patterns histogram of oriented gradients

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Performance Evaluation of Selected Thermal Imaging-Based Human Face Detectors 10th

Performance Evaluation of Selected Thermal Imaging-Based Hum...

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10th International Conference on Computer Recognition Systems (CORES)

作者： Forczmanski, Pawel West Pomeranian Univ Technol Fac Comp Sci & Informat Technol Zolnierska St 52 PL-71210 Szczecin Poland

ISBN: (纸本)9783319591629

The paper is devoted to the problem of face detection in thermal imagery. Its aim was to investigate several contemporary general-purpose object detectors known to be accurate when working in visible lighting conditions. Employed classifiers are based on AdaBoost learning method with three types of low-level descriptors, namely Haar-like features, Histogram of Oriented Gradients, and local binary patterns. Additionally, the performance of recently proposed Max-Margin Object-Detection Algorithm joint with HOG feature extractor and Deep Neural Network-based approach have been investigated. Performed experiments, on images taken in controlled and uncontrolled conditions, gathered in our own benchmark database and in a few other databases support final observations and conclusions.

关键词： Thermovision Biometrics Face detection Haar-like features Histogram of Oriented Gradients local binary patterns AdaBoost Max-Margin Object-Detection Algorithm Deep Neural Network

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Formation the texture features vector using the energy approach 24

Formation the texture features vector using the energy appro...

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24th International Symposium on Atmospheric and Ocean Optics - Atmospheric Physics

作者： Galileiskii, Viktor P. Elizarov, Alexey I. Kokarev, Dmitrii V. Morozov, Aleksandr M. Inst Atmospher Opt SB RAS Academician Zuev Sq 1 Tomsk 634021 Russia Natl Res Tomsk State Univ 36 Lenin Prospekt Tomsk 634050 Russia

The application of cluster analysis in the problem of classification of data obtained from remote sensing of the Earth is considered. A method for searching for image local features using masks Laws, and the formation... 详细信息

ISBN: (数字)9781510622920

ISBN: (纸本)9781510622920

关键词： image processing cloudiness remote sensing of the Earth texture features local binary patterns Laws texture energy

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SIFRS: Spoof Invariant Facial Recognition System (A Helping Hand For Visual Impaired People) 9

SIFRS: Spoof Invariant Facial Recognition System (A Helping ...

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9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Dixit, Aditya Satpute, V. R. Visvesvaraya Natl Inst Technol Dept Elect & Commun Nagpur Maharashtra India

ISBN: (纸本)9781538644300

Facial recognition is the fast growing and challenging field in biometric applications. Variety of improvements has been suggested considering the feature extraction and matching techniques for accurate and efficient facial recognition. In this paper we proposed an approach which uses facial recognition methodologies to implement a computational efficient technique for facial biometric application to help visually impaired people. In this system we used LBP for feature extraction to classify image either valid or invalid person using SVM classification so that it helps visual impaired people in improving their lifestyle and safety. The architecture has been verified with both in a real environment Actual users and printed images have achieved very good results.

关键词： Facial Recognition SVM local binary patterns Mobile app desktop application

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Anti-rotation binary Descriptor for Circular Image Recognition

Anti-rotation Binary Descriptor for Circular Image Recogniti...

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Chinese Automation Congress (CAC)

作者： Chen, Honglei Zhang, Dongbo Yi, Liangling Tang, Hongzhong Xu, Haixia Xiangtan Univ Coll Informat Engn Xiangtan Peoples R China Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan Peoples R China

ISBN: (纸本)9781728113128

Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor was proposed in this paper. To achieve rotation invariance during feature computation, a local coordinate system was found with radial transform technology. On the basis of that, local binary patterns against rotation could be extracted. Meanwhile, the circular image was divided into a set of overlapped annular regions, and pairs of patch description were constructed with histogram within the ring. Finally the rotation invariant image description could be obtained by concatenating the ring features from inner to outer. The performance of proposed method was tested with 3 data sets. The test results showed that its recognition accuracy reached 100%, 100% and 97.07% respectively, which were superior to the results of traditional methods based on LBP feature. Moreover, the algorithm was efficient, as single point feature computation needed only 0.045ms.

关键词： Image Descriptor local binary patterns Image Recognition Rotation Invariance

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SEA ICE CLASSIFICATION FROM HYPERSPECTRAL IMAGES BASED ON SELF-PACED BOOST LEARNING 38

SEA ICE CLASSIFICATION FROM HYPERSPECTRAL IMAGES BASED ON SE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Dong Gao, Feng Dong, Junyu Yang, Yang Wang, Shengke Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China Ocean Univ China Qingdao Key Lab Mixed Real & Virtual Ocean Qingdao Peoples R China Ocean Univ China Sch Law & Polit Qingdao Peoples R China

ISBN: (纸本)9781538671504

Hyperspectral imagery has evident advantages for sea ice classification due to enormous spectral bands. In this paper, we proposed a novel sea ice classification framework from hyperspectral image based on self-paced boost learning (SPBL). First, the criterion of linear prediction error is used for unsupervised band selection. Then, local binary pattern (LBP) features are extracted from the selected bands. Finally, SPBL is employed as the classifier to provide probability outputs using the extracted features. The proposed framework can capture the intrinsic inter-class discriminative models while ensuring the reliability of the samples involved in learning. The experimental results in real-world dataset demonstrate that the proposed framework is superior to several closely related methods.

关键词： Sea ice hyperspectral image local binary patterns self-paced boost learning

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DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGES USING AN ACTIVE SHAPE MODEL WITH A CASCADE CLASSIFIER 5

DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGE...

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5th International Conference on Geoinformation Science / ISPRS Conference on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances)

作者： Azzaoui, M. A. Adnani, M. El Belrhiti, H. Chaouki, I. E. Masmoudi, L. Univ Mohammed V Agdal Lab Elect & Traitement Signal Geomat LETS Geomat Fac Sci Rabat 4 Ave Ibn Battouta BP 1014 RP Rabat Morocco Inst Agron & Vet Hassan II Dept Sci Fondamentales & Appliquees BP 6202 Rabat 10101 Morocco Ecole Natl Sci Appl Agadir BP 1136 Agadir Morocco

Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remote sensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400 km corridor of sand dunes. We used image processing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. The process we developed combined two main parts: The first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern. For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001;Liao et al., 2007). Then, we merged positive bounding boxes exceeding a defined overlapping ratio. The positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an image processing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different de

关键词： Remote Sensing IKONOS High resolution satellite images Cascade classifiers Active Shape Model local binary patterns Haar features SURF SVM barchans dunes desertification

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Iris Recognition Using Color and Texture Features 7th

Iris Recognition Using Color and Texture Features

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7th International Workshop on Soft Computing Applications (SOFA)

作者： Pavaloi, Ioan Ignat, Anca Romanian Acad Inst Comp Sci Str Codrescu 2 Iasi 700481 Romania Alexandru Ioan Cuza Univ Fac Comp Sci Str Gen Berthelot 16 Iasi 700483 Romania

ISBN: (纸本)9783319625249

We approach the problem of iris recognition by combining both color and texture information. For color features, a well-known global color criterion was extended and for texture we adapted three classical methods: Gray-Level Co-occurrence Matrix (GLCM), local binary patterns (LBP), and Gabor filters. As classifiers we employed k-NN (with Euclidean, Manhattan, Canberra and some variations of these distances), Support Vector Machines (SVM) and a two steps recognition process based on k-NN. We tested the methods using RGB, HSV, and LAB color spaces on two irises datasets (UBIRIS and UPOL). We get better results by combining color and texture features than by only considering color and texture separately.

关键词： Color indexing Color spaces Texture Gray-Level Co-occurrence Matrix local binary patterns Gabor filters K-NN SVM Iris recognition

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Predicting 1p/19q co-deletion status in low grade gliomas: The effect of using local binary convolutional networks

Predicting 1p/19q co-deletion status in low grade gliomas: T...

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作者： Maria Garcia Sanz Delft University of Technology

学位级别：硕士

Patients with 1p/19q co-deleted low grade glioma (LGGs) have better prognosis and react better to certain treatments than patients with intact 1p/19q LGG. Currently, information about the 1p/19q co-deletion sta- tus is obtained by means of an invasive procedure called biopsy. As an alternative, non-invasive techniques to extract this information from medical images are being studied. Recent research suggests that local bi- nary patterns (LBPs), a textural image descriptor, are an important feature which can predict the 1p/19q co-deletion from MRI scans. In this project we report the effect of including LBP information in a convolu- tional neural network (CNN) to predict the 1p/19q co-deletion status in patients suffering from a presumed LGG using pre-operative MRI scans. A combination of convolutional filters was designed and included in the CNN, resulting into local binary con- volutional neural networks (LBCNNs). Three LBP descriptors, each of them representing a different textural scale, were studied, as well as the combination of the three. A default CNN without LBPs was also stud- ied. To validate the designed filters and to study more sophisticated LBPs images like the uniform LBPs, pre-computed LBP images were directly input to the CNN. An in-house multi-institution MRI dataset con- sisting of 284 patients who had undergone a biopsy or resection before the treatment, and with available pre-operative T1-weighted post contrast and T2-weighted scans was used to train the different network archi- tectures. An independent dataset consisting of 129 patients was used to validate the results. The performance of the LBCNNs was compared to the performance of the CNN. The performance of the CNN and LBCNNs was similar, reporting an area under the receiver operating char- acteristic curve (AUC) ranging from 0.816 to 0.872 for the different architectures. These findings suggest that the CNN can extract information relative to LBPs by itself. In addition, pre-computed unifo

关键词： Deep Learning Low Grade Gliomas 1p/19q co-deletion Convolutional Neural Network local binary patterns Radiogenomics

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Exploiting global and local features for image retrieval

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Journal of Central South University 2018年第2期25卷 259-276页

作者： LI Li FENG Lin WU Jun SUN Mu-xin LIU Sheng-lan School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology School of Innovation and Entrepreneurship Dalian University of Technology School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

Two lines of image representation based on multiple features fusion demonstrate excellent performance in image ***,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad *** address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval *** bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an *** proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density *** performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,*** experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

关键词： local binary patterns hue, saturation, value (HSV) color space graph fusion image retrieval

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