Paper Group ANR 67
Anomaly Detection in Clutter using Spectrally Enhanced Ladar. NdFluents: A Multi-dimensional Contexts Ontology. Improved phase-unwrapping method using geometric constraints. A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification. Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation. Socratic Learning: …
Anomaly Detection in Clutter using Spectrally Enhanced Ladar
| Title | Anomaly Detection in Clutter using Spectrally Enhanced Ladar |
| Authors | Puneet S Chhabra, Andrew M Wallace, James R Hopgood |
| Abstract | Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camouflaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral signature that does not conform to a prior expectation, represented using a learnt subspace (dictionary) and set of coefficients that capture co-occurring local-patterns using an overlapping temporal window. A modified optimization scheme is proposed for subspace learning based on stochastic approximations. The objective function is augmented with a discriminative term that represents the subspace’s separability properties and supports anomaly characterisation. The algorithm detects several man-made objects and anomalous spectra hidden in a dense clutter of vegetation and also allows tree species classification. |
| Tasks | Anomaly Detection |
| Published | 2016-02-17 |
| URL | http://arxiv.org/abs/1602.05264v1 |
| http://arxiv.org/pdf/1602.05264v1.pdf | |
| PWC | https://paperswithcode.com/paper/anomaly-detection-in-clutter-using-spectrally |
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NdFluents: A Multi-dimensional Contexts Ontology
| Title | NdFluents: A Multi-dimensional Contexts Ontology |
| Authors | José M. Giménez-García, Antoine Zimmermann, Pierre Maret |
| Abstract | Annotating semantic data with metadata is becoming more and more important to provide information about the statements being asserted. While initial solutions proposed a data model to represent a specific dimension of meta-information (such as time or provenance), the need for a general annotation framework which allows representing different context dimensions is needed. In this paper, we extend the 4dFluents ontology by Welty and Fikes—on associating temporal validity to statements—to any dimension of context, and discuss possible issues that multidimensional context representations have to face and how we address them. |
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| Published | 2016-09-22 |
| URL | http://arxiv.org/abs/1609.07102v1 |
| http://arxiv.org/pdf/1609.07102v1.pdf | |
| PWC | https://paperswithcode.com/paper/ndfluents-a-multi-dimensional-contexts |
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Improved phase-unwrapping method using geometric constraints
| Title | Improved phase-unwrapping method using geometric constraints |
| Authors | Guangliang Du, Minmin Wang, Canlin Zhou, Shuchun Si, Hui Li, Zhenkun Lei, Yanjie Li |
| Abstract | Conventional dual-frequency fringe projection algorithm often suffers from phase unwrapping failure when the frequency ratio between the high frequency and the low one is too large. Zhang et.al. proposed an enhanced two-frequency phase-shifting method to use geometric constraints of digital fringe projection(DFP) to reduce the noise impact due to the large frequency ratio. However, this method needs to calibrate the DFP system and calculate the minimum phase map at the nearest position from the camera perspective, these procedures are are relatively complex and more time-cosuming. In this paper, we proposed an improved method, which eliminates the system calibration and determination in Zhang’s method,meanwhile does not need to use the low frequency fringe pattern. In the proposed method,we only need a set of high frequency fringe patterns to measure the object after the high frequency is directly estimated by the experiment. Thus the proposed method can simplify the procedure and improve the speed. Finally, the experimental evaluation is conducted to prove the validity of the proposed method.The results demonstrate that the proposed method can overcome the main disadvantages encountered by Zhang’s method. |
| Tasks | Calibration |
| Published | 2016-09-28 |
| URL | http://arxiv.org/abs/1610.04261v1 |
| http://arxiv.org/pdf/1610.04261v1.pdf | |
| PWC | https://paperswithcode.com/paper/improved-phase-unwrapping-method-using |
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A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
| Title | A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification |
| Authors | Liao Ni, Yi Zhang, He Zheng, Shilei Liu, Houjun Huang, Wenxin Li |
| Abstract | Finger vein verification has developed a lot since its first proposal, but there is still not a perfect algorithm. It is proved that algorithms with the same overall accuracy may have different misclassified patterns. We could make use of this complementation to fuse individual algorithms together for more precise result. According to our observation, algorithm has different confidence on its decisions but it is seldom considered in fusion methods. Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting. Experiment conducted on a data set of 1000 fingers and 5 images per finger proves the effectiveness of the method. The classifier obtained by MDRR method gets an accuracy of 99.42% while the maximum accuracy of the original individual classifiers is 97.77%. The experiment results also show the MDRR outperforms the traditional fusion methods as Voting, Weighted Voting, Sum and Weighted Sum. |
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| Published | 2016-12-17 |
| URL | http://arxiv.org/abs/1612.05712v1 |
| http://arxiv.org/pdf/1612.05712v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-fusion-method-based-on-decision-reliability |
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Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation
| Title | Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation |
| Authors | Felix Järemo Lawin, Per-Erik Forssén, Hannes Ovrén |
| Abstract | In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them. The confidence produced by the KDE is also an effective means to detect outliers. We also introduce a new closed-form expression for phase noise prediction, that better fits real data. The method is applied to depth decoding for the Kinect v2 sensor, and compared to the Microsoft Kinect SDK and to the open source driver libfreenect2. The intended Kinect v2 use case is scenes with less than 8m range, and for such cases we observe consistent improvements, while maintaining real-time performance. When extending the depth range to the maximal value of 8.75m, we get about 52% more valid measurements than libfreenect2. The effect is that the sensor can now be used in large depth scenes, where it was previously not a good choice. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/datasets/kinect2-dataset. |
| Tasks | Density Estimation |
| Published | 2016-08-18 |
| URL | http://arxiv.org/abs/1608.05209v1 |
| http://arxiv.org/pdf/1608.05209v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-multi-frequency-phase-unwrapping |
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Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
| Title | Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data |
| Authors | Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré |
| Abstract | A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like user-defined heuristics or knowledge bases, to label training data. Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set. In particular, they fail to model latent subsets in the training data in which the supervision sources perform differently than on average. We present Socratic learning, a paradigm that uses feedback from a corresponding discriminative model to automatically identify these subsets and augments the structure of the generative model accordingly. Experimentally, we show that without any ground truth labels, the augmented generative model reduces error by up to 56.06% for a relation extraction task compared to a state-of-the-art weak supervision technique that utilizes generative models. |
| Tasks | Relation Extraction |
| Published | 2016-10-25 |
| URL | http://arxiv.org/abs/1610.08123v4 |
| http://arxiv.org/pdf/1610.08123v4.pdf | |
| PWC | https://paperswithcode.com/paper/socratic-learning-augmenting-generative |
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Learning to Translate in Real-time with Neural Machine Translation
| Title | Learning to Translate in Real-time with Neural Machine Translation |
| Authors | Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O. K. Li |
| Abstract | Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively. |
| Tasks | Machine Translation |
| Published | 2016-10-03 |
| URL | http://arxiv.org/abs/1610.00388v3 |
| http://arxiv.org/pdf/1610.00388v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-translate-in-real-time-with |
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Convergence rates of Kernel Conjugate Gradient for random design regression
| Title | Convergence rates of Kernel Conjugate Gradient for random design regression |
| Authors | Gilles Blanchard, Nicole Krämer |
| Abstract | We prove statistical rates of convergence for kernel-based least squares regression from i.i.d. data using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is related to Kernel Partial Least Squares, a regression method that combines supervised dimensionality reduction with least squares projection. Following the setting introduced in earlier related literature, we study so-called “fast convergence rates” depending on the regularity of the target regression function (measured by a source condition in terms of the kernel integral operator) and on the effective dimensionality of the data mapped into the kernel space. We obtain upper bounds, essentially matching known minimax lower bounds, for the $\mathcal{L}^2$ (prediction) norm as well as for the stronger Hilbert norm, if the true regression function belongs to the reproducing kernel Hilbert space. If the latter assumption is not fulfilled, we obtain similar convergence rates for appropriate norms, provided additional unlabeled data are available. |
| Tasks | Dimensionality Reduction |
| Published | 2016-07-08 |
| URL | http://arxiv.org/abs/1607.02387v1 |
| http://arxiv.org/pdf/1607.02387v1.pdf | |
| PWC | https://paperswithcode.com/paper/convergence-rates-of-kernel-conjugate |
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Extracted Social Network Mining
| Title | Extracted Social Network Mining |
| Authors | Mahyuddin K. M. Nasution |
| Abstract | In this paper we study the relationship between the resources of social networks by exploring the Web as big data based on a simple search engine. We have used set theory by utilizing the occurrence and co-occurrence for defining the singleton or doubleton spaces of event in a search engine model, and then provided them as representation of social actors and their relationship in clusters. Thus, there are behaviors of social actors and their relation based on Web. |
| Tasks | |
| Published | 2016-04-24 |
| URL | http://arxiv.org/abs/1604.06976v1 |
| http://arxiv.org/pdf/1604.06976v1.pdf | |
| PWC | https://paperswithcode.com/paper/extracted-social-network-mining |
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Detection of surface defects on ceramic tiles based on morphological techniques
| Title | Detection of surface defects on ceramic tiles based on morphological techniques |
| Authors | Grasha Jacob, R. Shenbagavalli, S. Karthika |
| Abstract | Ceramic tiles have become very popular and are used in the flooring of offices and shopping malls. As testing the quality of tiles manually in a highly polluted environment in the manufacturing industry is a labor-intensive and time consuming process, analysis is carried out on the tile images. This paper discusses an automated system to detect the defects on the surface of ceramic tiles based on dilation, erosion, SMEE and boundary detection techniques. |
| Tasks | Boundary Detection |
| Published | 2016-07-21 |
| URL | http://arxiv.org/abs/1607.06676v1 |
| http://arxiv.org/pdf/1607.06676v1.pdf | |
| PWC | https://paperswithcode.com/paper/detection-of-surface-defects-on-ceramic-tiles |
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Classification with Ultrahigh-Dimensional Features
| Title | Classification with Ultrahigh-Dimensional Features |
| Authors | Yanming Li, Hyokyoung Hong, Jian Kang, Kevin He, Ji Zhu, Yi Li |
| Abstract | Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. We also show that the proposed procedure provides more powerful discovery boundaries compared to those in \citet{CaiSun:2014} and \citet{JJin:2009}. The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with different post-transplantation renal functional types. |
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| Published | 2016-11-04 |
| URL | http://arxiv.org/abs/1611.01541v1 |
| http://arxiv.org/pdf/1611.01541v1.pdf | |
| PWC | https://paperswithcode.com/paper/classification-with-ultrahigh-dimensional |
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The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study
| Title | The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study |
| Authors | Luiz H. Nunes, Julio C. Estrella, Alexandre C. B. Delbem, Charith Perera, Stephan Reiff-Marganiec |
| Abstract | Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept. |
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| Published | 2016-11-16 |
| URL | http://arxiv.org/abs/1611.05170v1 |
| http://arxiv.org/pdf/1611.05170v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-effects-of-relative-importance-of-user |
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A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics
| Title | A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics |
| Authors | Friederike Laus, Mila Nikolova, Johannes Persch, Gabriele Steidl |
| Abstract | Nonlocal patch-based methods, in particular the Bayes’ approach of Lebrun, Buades and Morel (2013), are considered as state-of-the-art methods for denoising (color) images corrupted by white Gaussian noise of moderate variance. This paper is the first attempt to generalize this technique to manifold-valued images. Such images, for example images with phase or directional entries or with values in the manifold of symmetric positive definite matrices, are frequently encountered in real-world applications. Generalizing the normal law to manifolds is not canonical and different attempts have been considered. Here we focus on a straightforward intrinsic model and discuss the relation to other approaches for specific manifolds. We reinterpret the Bayesian approach of Lebrun et al. (2013) in terms of minimum mean squared error estimation, which motivates our definition of a corresponding estimator on the manifold. With this estimator at hand we present a nonlocal patch-based method for the restoration of manifold-valued images. Various proof of concept examples demonstrate the potential of the proposed algorithm. |
| Tasks | Denoising |
| Published | 2016-07-28 |
| URL | http://arxiv.org/abs/1607.08481v3 |
| http://arxiv.org/pdf/1607.08481v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-nonlocal-denoising-algorithm-for-manifold |
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Comparative study of histogram distance measures for re-identification
| Title | Comparative study of histogram distance measures for re-identification |
| Authors | Pedro A. Marín-Reyes, Javier Lorenzo-Navarro, Modesto Castrillón-Santana |
| Abstract | Color based re-identification methods usually rely on a distance function to measure the similarity between individuals. In this paper we study the behavior of several histogram distance measures in different color spaces. We wonder whether there is a particular histogram distance measure better than others, likewise also, if there is a color space that present better discrimination features. Several experiments are designed and evaluated in several images to obtain measures against various color spaces. We test in several image databases. A measure ranking is generated to calculate the area under the CMC, this area is the indicator used to evaluate which distance measure and color space present the best performance for the considered databases. Also, other parameters such as the image division in horizontal stripes and number of histogram bins, have been studied. |
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| Published | 2016-11-24 |
| URL | http://arxiv.org/abs/1611.08134v1 |
| http://arxiv.org/pdf/1611.08134v1.pdf | |
| PWC | https://paperswithcode.com/paper/comparative-study-of-histogram-distance |
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Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
| Title | Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs) |
| Authors | Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao |
| Abstract | Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low-dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document. |
| Tasks | Document Classification |
| Published | 2016-06-09 |
| URL | http://arxiv.org/abs/1606.02979v2 |
| http://arxiv.org/pdf/1606.02979v2.pdf | |
| PWC | https://paperswithcode.com/paper/generative-topic-embedding-a-continuous-1 |
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