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Flotation Froth Image Analysis by Use of a Dynamic Feature Extraction Algorithm

IFAC-PapersOnLine 2016年第20期49卷 84-89页

作者： Fu, Yihao Aldrich, Chris Department of Mining Engineering and Metallurgical Engineering Western Australian School of Mines Curtin University GPO Box U1987 PerthWA6845 Australia

Froth image analysis has been well established as a means to infer the performance of froth flotation cells in real time. Apart from linking the appearance of the froth to the behavior of the flotation system, the dynamic behaviour of the froth is also an important determinant of the performance of the flotation cell, and ideally, this information should also be taken into consideration. In this investigation, the dynamic behaviour of the froth was incorporated implicitly in the features extracted from the images. As a case study, mineral mixtures consisting of realgar, orpiment and quartz were floated in a laboratory batch flotation cell. Videographic mages of the froths generated by the experiments and a dynamic local binary pattern algorithm (LBP-TOP) was used to extract features from the video data. A random forest model could subsequently be built to reliably classify the conditions prevailing in each of the batch runs. The dynamic LBP algorithm did not perform significantly better than its 2D equivalent that did not incorporate the temporal behaviour of the froth, as both approaches could very reliably identify the different froth classes. © 2016

关键词： Froth flotation Computer vision Content based retrieval Decision trees Equivalence classes Flotation Image analysis Learning systems Batch flotation Dynamic behaviours Dynamic features Flotation cell Flotation froths local binary patterns Random forest modeling Temporal behaviour

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Fusing Facial Texture Features for Face Recognition

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES 2016年第3期86卷 395-403页

作者： Shao, Yanqing Tang, Chaowei Xiao, Min Tang, Hui Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China Chongqing Coll Elect Engn Dept Commun Engn Chongqing 401331 Peoples R China Sichuan Changhong Elect Co Ltd Software & Serv Ctr Mianyang 621000 Sichuan Peoples R China Chinese Acad Sci Inst Acoust High Performance Network Lab Beijing 100190 Peoples R China

Aiming at taking full advantage of facial information both in low-frequency and high-frequency regions and further improving face recognition rate, this paper constructs a robust nonsubsampled contourlet transform local binary patterns (NSCTLBP) feature and proposes a face recognition method fusing NSCTLBP and Gabor features. Firstly, face image is decomposed by NSCT, and the LBP values of NSCT high-frequency subbands are computed to construct NSCTLBP features. Meanwhile, convolution of 2D-Gabor wavelet with face image is performed to extract Gabor texture feature in low-frequency. Secondly, Euclidean distance and eigenvalue-weighted cosine (EWC) distance are adopted to explore the similarity measurement of NSCTLBP and Gabor features respectively. Finally, the face images are matched according to the weighted similarity of NSCTLBP feature and Gabor feature collaboratively. Experimental results on Yale and ORL databases show that the proposed method has better performances than that based on NSCT feature, NSCTLBP feature and Gabor feature separately against illumination, expression, and angle variations and glasses occlusion.

关键词： Eigenvalue-weighted cosine distance Face recognition Gabor wavelet local binary patterns Nonsubsampled contourlet transform

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Flotation Froth Image Analysis by Use of a Dynamic Feature Extraction Algorithm

Flotation Froth Image Analysis by Use of a Dynamic Feature E...

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17th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing (MMM)

作者： Fu, Yihao Aldrich, Chris Curtin Univ Western Australian Sch Mines Dept Min Engn & Met Engn GPO Box U1987 Perth WA 6845 Australia

Froth image analysis has been well established as a means to infer the performance of froth flotation cells in real time. Apart from linking the appearance of the froth to the behavior of the flotation system. the dynamic behaviour of the froth is also an important determinant of the performance of the flotation cell, and ideally, this information should also be taken into consideration. In this investigation, the dynamic behaviour of the froth was incorporated implicitly in the features extracted from the images As a case study, mineral mixtures consisting of realgar. orpiment and quartz were floated in a laboratory batch flotation cell. Videographic mages of the froths generated by the experiments and a dynamic local binary pattern algorithm (LBP-TOP) was used to extract features from the video data. A random forest model could subsequently be built to reliably classify the conditions prevailing in each of the batch runs. The dynamic LBP algorithm did not perform significantly better than its 2D equivalent that did not incorporate the temporal behaviour of the froth, as both approaches could very reliably identify the different froth classes. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Image Analysis Machine Learning Flotation Computer Vision local binary patterns

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local binary pattern based face recognition with automatically detected fiducial points

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INTEGRATED COMPUTER-AIDED ENGINEERING 2016年第2期23卷 129-139页

作者： Lenc, Ladislav Kral, Pavel Univ W Bohemia Dept Comp Sci & Engn Plzen 30614 Czech Republic Univ W Bohemia New Technol Informat Soc Plzen 30614 Czech Republic

This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (CTK). We focus on popular local binary patterns (LPBs) that are frequently used in this field with high recognition accuracy. One drawback of current LBP based methods is that the positions and number of the fiducial points are fixed. These points thus do not reflect the properties of a particular image whereas we believe it is beneficial to identify the most representative ones. The main contribution consists in proposing and comparing several LBP-based approaches that detect such points fully automatically. We use a set of Gabor filters for this task. local extrema in the filter responses are detected and then used as the feature points. The number of points is further reduced by a clustering algorithm. Our approaches also differ from the other ones in the matching procedure. The proposed methods are evaluated on three standard corpora: ORL, FERET, AR face database and our CTK dataset containing uncontrolled face images. Recognition results clearly show high quality of the proposed approaches that outperform significantly the baseline LBP approach on all corpora. The benefits of our methods are particularly evident in the case of real non-controlled images (CTK corpus) where the accuracy is increased by more than 20% in absolute value.

关键词： Automatic face recognition czech news agency gabor filter local binary patterns LBP

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An assessment of automatic speaker verification vulnerabilities to replay spoofing attacks

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SECURITY AND COMMUNICATION NETWORKS 2016年第15期9卷 3030-3044页

作者： Janicki, Artur Alegre, Federico Evans, Nicholas Warsaw Univ Technol Warsaw Poland ValidSoft London England EURECOM Biot France Sophia Antipolis EURECOM Nice France

This paper analyses the threat of replay spoofing or presentation attacks in the context of automatic speaker verification. As relatively high-technology attacks, speech synthesis and voice conversion, which have thus far received far greater attention in the literature, are probably beyond the means of the average fraudster. The implementation of replay attacks, in contrast, requires no specific expertise nor sophisticated equipment. Replay attacks are thus likely to be the most prolific in practice, while their impact is relatively under-researched. The work presented here aims to compare at a high level the threat of replay attacks with those of speech synthesis and voice conversion. The comparison is performed using strictly controlled protocols and with six different automatic speaker verification systems including a state-of-the-art iVector/probabilistic linear discriminant analysis system. Experiments show that low-effort replay attacks present at least a comparable threat to speech synthesis and voice conversion. The paper also describes and assesses two replay attack countermeasures. A relatively new approach based on the local binary pattern analysis of speech spectrograms is shown to outperform a competing approach based on the detection of far-field recordings. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： speaker verification spoofing presentation attack replay countermeasures local binary patterns

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Flower segmentation with level sets evolution controlled by colour, texture and shape features

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COGENT ENGINEERING 2017年第1期4卷 Article: 1323572页

作者： Inthiyaz, Syed Madhav, B. T. P. Madhav, P. V. V. KL Univ Dept Elect & Commun Engn Guntur Andhra Pradesh India

This work proposes a pre-informed Chan vese based level sets algorithm. Pre information includes objects colour, texture and shape fused features. The aim is to use this algorithm to segment flower images and extract meaningful features that will help is classification of floral content. Shape pre-information modelling is handled manually using advance image processing tools. local binary patterns features makeup texture pre-information and Red, Green and Blue colour channels of the object provide colour pre-information. All pre-defined object information is fused together to for high dimension subspace defining object characteristics. Testing of the algorithm on flower images datasets show a jump in information content in the resulting segmentation output compared to other models in the category. Segmentation of flowers is important for recognition, classification and quality assessment to ever increasing volumes in floral markets.

关键词： flower image segmentation texture priors local binary patterns shape priors level sets

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Individual and Inter-related Action Unit Detection in Videos for Affect Recognition

Individual and Inter-related Action Unit Detection in Videos...

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作者： Anil YUCE ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

The human face has evolved to become the most important source of non-verbal in- formation that conveys our affective, cognitive and mental state to others. Apart from human to human communication facial expressions have also become an indispensable component of human-machine interaction (HMI). Systems capable of understanding how users feel allow for a wide variety of applications in medical, learning, entertain- ment and marketing technologies in addition to advancements in neuroscience and psy- chology research and many others. The Facial Action Coding System (FACS) has been built to objectively define and quantify every possible facial movement through what is called Action Units (AU), each representing an individual facial action. In this thesis we focus on the automatic detection and exploitation of these AUs using novel appearance representation techniques as well as incorporation of the prior co-occurrence information between them. Our contributions can be grouped in three parts. In the first part, we propose to improve the detection accuracy of appearance features based on local binary patterns (LBP) for AU detection in videos. For this pur- pose, we propose two novel methodologies. The first one uses three fundamental image processing tools as a pre-processing step prior to the application of the LBP transform on the facial texture. These tools each enhance the descriptive ability of LBP by em- phasizing different transient appearance characteristics, and are proven to increase the AU detection accuracy significantly in our experiments. The second one uses multi- ple local curvature Gabor binary patterns (LCGBP) for the same problem and achieves state-of-the-art performance on a dataset of mostly posed facial expressions. The curva- ture information of the face, as well as the proposed multiple filter size scheme is very effective in recognizing these individual facial actions. In the second part, we propose to take advantage of the co-occurrence relation

关键词： facial analysis facial expressions action units local binary patterns Gabor wavelets multi-label embedding driver monitoring cognitive distraction

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Semi-local Structure patterns for Robust Face Detection

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IEEE SIGNAL PROCESSING LETTERS 2015年第9期22卷 1400-1403页

作者： Jeong, Kyungjoong Choi, Jaesik Jang, Gil-Jin Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan 689798 South Korea Kyungpook Natl Univ Sch Elect Engn Daegu 702701 South Korea

In many image processing and computer vision problems, including face detection, local structure patterns such as local binary patterns (LBP) and modified census transform (MCT) have been adopted in widespread applications due to their robustness against illumination changes. However, being reliant on the local differences between neighboring pixels, they are inevitably sensitive to noise. To overcome the problem of noise-vulnerability of the conventional local structure patterns, we propose semi-local structure patterns (SLSP), a novel feature extraction method based on local region-based differences. The SLSP is robust to illumination variations, distortion, and sparse noise because it encodes the relative sizes of the central region with locally neighboring regions into a binary code. The principle of SLSP leads noise-robust expansions of LBP and MCT feature extraction frameworks. In a statistical analysis, we find that the proposed methods transform a substantial amount of random noise patterns in face images into more meaningful uniform patterns. The empirical results on the MIT + CMU dataset and FDDB (face detection dataset and benchmark) show that the proposed semi-local patterns applied to LBP and MCT feature extraction frameworks outperform the conventional LBP and MCT features in AdaBoost-based face detectors, with much higher detection rates.

关键词： AdaBoost distortion face detection local binary patterns semi-local structure patterns

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Automatic Emotion Recognition in the Wild using an Ensemble of Static and Dynamic Representations 16

Automatic Emotion Recognition in the Wild using an Ensemble ...

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18th ACM International Conference on Multimodal Interaction (ICMI)

作者： Ghazi, Mostafa Mehdipour Ekenel, Hazim Kemal Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Istanbul Tech Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781450345569

Automatic emotion recognition in the wild video datasets is a very challenging problem because of the inter-class similarities among different facial expressions and large intraclass variabilities due to the significant changes in illumination, pose, scene, and expression. In this paper, we present our proposed method for video-based emotion recognition in the EmotiW 2016 challenge. The task considers the unconstrained emotion recognition problem by training from short video clips extracted from movies and testing on short movie clips and spontaneous video clips of the reality TV data. Four different methods are employed to extract both static and dynamic emotion representations from the videos. First, local binary patterns of three orthogonal planes are used to describe spatiotemporal features of the video frames. Second, principal component analysis is applied to the image patches in a two-stage convolutional network to learn weights and extract facial features from the aligned faces. Third, the deep convolutional neural network model of VGG-Face is deployed to extract deep facial representations from aligned faces. Fourth, a bag of visual words is computed based on dense scale-invariant feature transform descriptors from aligned face images to form hand-crafted representations. Support vector machines are then utilized to train and classify the obtained spatiotemporal representations and facial features. Finally, score-level fusion is applied to combine the classification results and predict the emotion labels of the video clips. The results show that the proposed combined method has outperformed all the utilized techniques with the overall validations and test accuracies of 43.13% and 40.13%, respectively. This system, is relatively a good classifier in Happy and Angry emotion categories and is unsuccessful in detecting Surprise, Disgust, and Fear.

关键词： Automatic emotion recognition local binary patterns principal component analysis scale-invariant feature transform convolutional neural network support vector machines

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Video Shot Boundary Detection using Midrange local binary Pattern 5

Video Shot Boundary Detection using Midrange Local Binary Pa...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Rashmi, B. S. Nagendraswamy, H. S. Univ Mysore Comp Sci Mysore Karnataka India Karnataka State Open Univ Mysore Karnataka India

ISBN: (纸本)9781509020287

The fundamental step in content based video retrieval is shot boundary detection. This step is essential for characterizing videos in any video processing system. Reliable detection of shots in videos is still a challenging issue. In this work, we address automatic detection of abrupt shots in video sequences. We have proposed a method termed as Midrange LBP (MRLBP), which enhances the discriminative capability of basic LBP. Each frame of a video is processed to extract LBP histogram values based on midrange statistics for its description. A dissimilarity measure is applied on the feature vectors of adjacent frames and the distance values obtained are used for shot detection process using adaptive threshold approach. To check the efficacy of the proposed method, we carried out experiments on a subset of standard video data set TRECVID 2001. The results obtained by the proposed approach outperform the existing shot boundary detection algorithms in terms of different performance measures.

关键词： Adaptive Threshold Dissimilarity Measure Euclidean local binary patterns Midrange Shot boundary Detection Video Analysis

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