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TANET2018 台湾网际网路研讨会 2018年 764-768页

作者：沈英謀蔡哲民王建仁陳冠傑

树木与人关系密切，但要单独以影像辨识树木种类并不容易，因此本研究以Location-based Service(LBS)结合树干之树皮特徵，辨识树木种类。传统的图像识别系统使用颜色和形状作为识别物体的特徵，如果两者相似，则经常使用纹理，以识别... 详细信息

树木与人关系密切，但要单独以影像辨识树木种类并不容易，因此本研究以Location-based Service(LBS)结合树干之树皮特徵，辨识树木种类。传统的图像识别系统使用颜色和形状作为识别物体的特徵，如果两者相似，则经常使用纹理，以识别不同种类树木。因此本研究以台南市台南公园为例，利用手机之拍照功能取得影像，再透过电脑装置的影像辨识系统，撷取DWT及LBP纹理特徵，再利用LBS取得精准度10公尺内之少数树种样本资料库，将测试样本与前述树种样本资料库比对，以进行树木种类之辨识。系统使用14种树木来做树皮影像实验，实验结果最高可达86%之辨识率。

关键词： Location-based Service Discrete Wavelet Transform local binary patterns

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Extended Mapping local binary Pattern Operator for Texture Classification

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2017年第6期31卷 1750019-1750019页

作者： Shakoor, Mohammad Hossein Boostani, Reza Islamic Azad Univ Shiraz Branch Dept Comp Engn Shiraz Iran Shiraz Univ Sch Elect & Comp Engn Dept Comp Engn Shiraz Iran

In this paper, an Extended Mapping local binary Pattern (EMLBP) method is proposed that is used for texture feature extraction. In this method, by extending nonuniform patterns a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping is tested for some LBP operators such as CLBP, LBP, and LTP to improve the classification rate of them. The proposed approach is used for coding nonuniform patterns into more than one feature. The proposed method is rotation invariant and has all the positive points of previous approaches. By concatenating and joining two or more histograms significant improvement can be made for rotation invariant texture classification. The implementation of proposed mapping on Outex, UIUC and CUReT datasets shows that proposed method can improve the rate of classifications. Furthermore, the introduced mapping can increase the performance of any rotation invariant LBP, especially for large neighborhood. The most accurate result of the proposed technique has been obtained for CLBP. It is higher than that of some state-of-the-art LBP versions such as multiresolution CLBP and CLBC, DLBP, VZ MR8, VZ Joint, LTP, and LBPV.

关键词： Extended mapping local binary patterns rotation invariance texture classification non-uniform patterns

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SEAM CARVING BASED IMAGE RESIZING DETECTION USING HYBRID FEATURES

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第6期24卷 1825-1832页

作者： Senturk, Zehra Karapinar Akgun, Devrim Duzce Univ Fac Engn Dept Comp Engn Konuralp Campus TR-81620 Duzce Turkey Sakarya Univ Fac Comp & Informat Sci Comp Engn Dept Esentepe Campus TR-54187 Sakarya Turkey

Detection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable. local binary patterns (LBP), a visual descriptor, unearths local changes in image texture. Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio. In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios. We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features. We achieved approximately 9 % improvement in low detection ratios. The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach.

关键词： forgery detection local binary patterns seam carving Support Vector Machines

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

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

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International Conference on Computing, Communication and Networking Technologies

作者： Aditya Dixit V. R. Satpute Visvesvaraya National Institute Of Technology Nagpur India

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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Topology-based image segmentation using LBP pyramids

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MACHINE VISION AND APPLICATIONS 2016年第8期27卷 1161-1174页

作者： Cerman, Martin Janusch, Ines Gonzalez-Diaz, Rocio Kropatsch, Walter G. TU Wien PRIP Grp Vienna Austria Univ Seville Sch Comp Engn Dept Appl Math Seville Spain

In this paper, we present a new image segmentation algorithm which is based on local binary patterns (LBPs) and the combinatorial pyramid and which preserves structural correctness and image topology. For this purpose, we define a codification of LBPs using graph pyramids. Since the LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region, we use it to obtain a "minimal" image representation in terms of the topological characterization of a given 2D grayscale image. Based on this idea, we further describe our hierarchical texture aware image segmentation algorithm and compare its segmentation output and the "minimal" image representation.

关键词： local binary patterns Irregular graph pyramid Primal and dual graph Topological characterization Image segmentation

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Score level fusion of classifiers in off-line signature verification

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INFORMATION FUSION 2016年第PartB期32卷 109-119页

作者： Yilmaz, Mustafa Berkay Yanikoglu, Berrin Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

Offline signature verification is a task that benefits from matching both the global shape and local details;as such, it is particularly suitable to a fusion approach. We present a system that uses a score-level fusion of complementary classifiers that use different local features (histogram of oriented gradients, local binary patterns and scale invariant feature transform descriptors), where each classifier uses a feature-level fusion to represent local features at coarse-to-fine levels. For classifiers, two different approaches are investigated, namely global and user-dependent classifiers. User-dependent classifiers are trained separately for each user, to learn to differentiate that user's genuine signatures from other signatures;while a single global classifier is trained with difference vectors of query and reference signatures of all users in the training set, to learn the importance of different types of dissimilarities. The fusion of all classifiers achieves a state-of-the-art performance with 6.97% equal error rate in skilled forgery tests using the public GPDS-160 signature database. The proposed system does not require skilled forgeries of the enrolling user, which is essential for real life applications. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Offline signature Score-level fusion Histogram of oriented gradients local binary patterns Scale invariant feature transform

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Detection of High-Density Crowds in Aerial Images Using Texture Classification

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REMOTE SENSING 2016年第6期8卷 470页

作者： Meynberg, Oliver Cui, Shiyong Reinartz, Peter German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany

Automatic crowd detection in aerial images is certainly a useful source of information to prevent crowd disasters in large complex scenarios of mass events. A number of publications employ regression-based methods for crowd counting and crowd density estimation. However, these methods work only when a correct manual count is available to serve as a reference. Therefore, it is the objective of this paper to detect high-density crowds in aerial images, where counting- or regression-based approaches would fail. We compare two texture-classification methodologies on a dataset of aerial image patches which are grouped into ranges of different crowd density. These methodologies are: (1) a Bag-of-words (BoW) model with two alternative local features encoded as Improved Fisher Vectors and (2) features based on a Gabor filter bank. Our results show that a classifier using either BoW or Gabor features can detect crowded image regions with 97% classification accuracy. In our tests of four classes of different crowd-density ranges, BoW-based features have a 5%-12% better accuracy than Gabor.

关键词： texture crowd detection bag of words fisher vectors local binary patterns gabor filter aerial images crowd density

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Topological Attribute patterns for texture recognition

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PATTERN RECOGNITION LETTERS 2016年第0期80卷 91-97页

作者： Thanh Phuong Nguyen Manzanera, Antoine Kropatsch, Walter G. Xuan Son Nguyen Aix Marseille Univ ENSAM LSIS CNRSUMR 7296 F-13397 Marseille France Univ Toulon & Var LSIS CNRS UMR 7296 F-83957 La Garde France Univ Paris Saclay U2IS ENSTA Paristech 828 Bd Marechaux F-91762 Palaiseau France TU Wien PRIP Favoritenstr 9-186-34 Stock A-1040 Vienna Austria Univ Lorraine LORIA INRIA CNRSUMR 7503 Villers Les Nancy France

An efficient texture modeling framework based on Topological Attribute patterns (TAP) is presented considering topology related attributes calculated from local binary patterns (LBP). Our main contribution is to introduce new efficient mapping mechanisms that improve some typical mappings for LBP-based operators in texture classification such as rotation invariant patterns (ri), rotation invariant uniform patterns (riu 2), and local binary Count (LBC). Like them, the proposed approach allows contrast and rotation invariant image description using more compact descriptors by projecting binary patterns to a reduced feature space. However, its expressiveness, and then its discrimination capability, is higher, since it includes additional information, related to the connected components of the binary patterns. The proposed mapping, evaluated and compared with different popular mappings, validates the interest of our approach. We then develop Complemented patterns of Topological Attributes (CTAP) that generalize TAP model and exploit complemented information to further enhance its discrimination capability, and evaluate it on different texture datasets. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Mapping Texture descriptor local binary patterns

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On the influence of interpolation method on rotation invariance in texture recognition

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2016年第1期2016卷 1-12页

作者： Kylberg, Gustaf Sintorn, Ida-Maria Vironova AB Gavlegatan 22 SE-11330 Stockholm Sweden Uppsala Univ Dept Informat Technol Lagerhyddsvagen 20 SE-75105 Uppsala Sweden

In this paper, rotation invariance and the influence of rotation interpolation methods on texture recognition using several local binary patterns (LBP) variants are investigated. We show that the choice of interpolation method when rotating textures greatly influences the recognition capability. Lanczos 3 and B-spline interpolation are comparable to rotating the textures prior to image acquisition, whereas the recognition capability is significantly and increasingly lower for the frequently used third order cubic, linear and nearest neighbour interpolation. We also show that including generated rotations of the texture samples in the training data improves the classification accuracies. For many of the descriptors, this strategy compensates for the shortcomings of the poorer interpolation methods to such a degree that the choice of interpolation method only has a minor impact. To enable an appropriate and fair comparison, a new texture dataset is introduced which contains hardware and interpolated rotations of 25 texture classes. Two new LBP variants are also presented, combining the advantages of local ternary patterns and Fourier features for rotation invariance.

关键词： local binary patterns Texture dataset Filter banks

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Improved RGB-D-T based face recognition

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IET BIOMETRICS 2016年第4期5卷 297-304页

作者： Oliu Simon, Marc Corneanu, Ciprian Nasrollahi, Kamal Nikisins, Olegs Escalera, Sergio Sun, Yunlian Li, Haiqing Sun, Zhenan Moeslund, Thomas B. Greitans, Modris Univ Barcelona Comp Vis Ctr Human Pose Recovery & Behav Anal HuPBA Grp Barcelona 08193 Spain Aalborg Univ Visual Anal People VAP Lab Rendsburggade 14 DK-9000 Aalborg Denmark Inst Elect & Comp Sci Dzerbenes 14 LV-1006 Riga Latvia Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR 95 Zhongguancun East Rd Beijing 100190 Peoples R China

Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.

关键词： face recognition learning (artificial intelligence) neural nets visual databases image colour analysis RGB-D-T database histogram of Gabor ordinal measures Haar-like rectangular features histogram of oriented gradients local binary patterns handcrafted features CNN-based recognition block multimodal RGB-depth-thermal based facial recognition deep learning convolutional neural networks multimodal facial recognition unimodal facial recognition systems improved RGB-D-T based face recognition

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