Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

2014 ◽  
Vol 15 (5) ◽  
pp. 1480-1500 ◽  
Author(s):  
Motong Qiao ◽  
Wei Wang ◽  
Michael Ng
Keyword(s):  
Gabor Filter ◽  
Error Function ◽  
Feature Space ◽  
Wasserstein Distance ◽  
Energy Functional ◽  
Phase Image ◽  
Minimization Method ◽  
Closed Form Solutions ◽  
Gabor Feature ◽  
Multi Phase

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

2017 ◽  
Vol 31 (12) ◽  
pp. 1757009 ◽  
Author(s):  
Xiaojing Gao ◽  
Heru Xue ◽  
Xin Pan ◽  
Xinhua Jiang ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Somatic Cells ◽  
Bovine Mastitis ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Large Set ◽  
Novel Approach ◽  
Gabor Feature ◽  
Feature Based

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

scholarly journals KECA Similarity-Based Monitoring and Diagnosis of Faults in Multi-Phase Batch Processes

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 121 ◽  
Author(s):  
Yongsheng Qi ◽  
Xuebin Meng ◽  
Chenxi Lu ◽  
Xuejin Gao ◽  
Lin Wang
Keyword(s):  
Feature Space ◽  
Batch Processes ◽  
Data Matrix ◽  
Nonlinear Characteristics ◽  
Nonlinear Contribution ◽  
Fault Monitoring ◽  
Feature Information ◽  
Stage Transition ◽  
Multi Phase ◽  
Entropy Component

Multiple phases with phase to phase transitions are important characteristics of many batch processes. The linear characteristics between phases are taken into consideration in the traditional algorithms while nonlinearities are neglected, which can lead to inaccuracy and inefficiency in monitoring. The focus of this paper is nonlinear multi-phase batch processes. A similarity metric is defined based on kernel entropy component analysis (KECA). A KECA similarity-based method is proposed for phase division and fault monitoring. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity variation of the extracted feature information. Then, a series of KECA models and slide-KECA models are established for steady and transitions phases respectively, which can reflect the diversity of transitional characteristics objectively and preferably deal with the stage-transition monitoring problem in multistage batch processes. Next, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed, which is easier, more intuitive and implementable compared with the traditional one. Finally, simulations are performed on penicillin fermentation and industrial application. Specifically, the proposed method detects the abnormal agitation power and the abnormal substrate supply at 47 h and 86 h, respectively. Compared with traditional methods, it has better real-time performance and higher efficiency. Results demonstrate the ability of the proposed method to detect faults accurately and effectively in practice.

scholarly journals Multi-Source Distilling Domain Adaptation

2020 ◽  
Vol 34 (07) ◽  
pp. 12975-12983
Author(s):  
Sicheng Zhao ◽  
Guangzhi Wang ◽  
Shanghang Zhang ◽  
Yang Gu ◽  
Yaxian Li ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Wasserstein Distance ◽  
Training Data ◽  
Source Distribution ◽  
Single Source ◽  
Multiple Sources ◽  
Target Domain ◽  
Target Feature ◽  
Training Samples

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

Identification of In-Plane Elastic Constants of Composite Laminates Using Measured Strains

1999 ◽  
Author(s):  
T. Y. Kam ◽  
C. H. Lin ◽  
W. T. Wang
Keyword(s):  
Nondestructive Evaluation ◽  
Minimization Problem ◽  
Composite Laminates ◽  
Global Minimum ◽  
Error Function ◽  
Laminated Composite ◽  
Pressure Vessels ◽  
Minimization Method ◽  
Material Constants ◽  
Constrained Minimization Problem

Abstract A method for nondestructive evaluation of material constants of composite laminates is presented. An error function is established to measure the differences between the theoretically and experimentally predicted strains. The identification of the material constants is formulated as a constrained minimization problem in which the material constants are determined to make the error function a global minimum. A global minimization method together with a technique for normalizing the gradient of the objective function are used to solve the above minimization problem. The applications of the proposed method are demonstrated by means of several examples on the material constants identification of laminated composite cylindrical pressure vessels.

Multi-phase image segmentation with the adaptive deformable mesh

2019 ◽  
Vol 117 ◽  
pp. 97-103 ◽  
Author(s):  
Tuan T. Nguyen ◽  
Vedrana A. Dahl ◽  
J. Andreas Bærentzen
Keyword(s):  
Image Segmentation ◽  
Phase Image ◽  
Deformable Mesh ◽  
Multi Phase

scholarly journals Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings

2020 ◽  
Author(s):  
Yuanfei Dai ◽  
Chenhao Guo ◽  
Wenzhong Guo ◽  
Carsten Eickhoff
Keyword(s):  
Feature Space ◽  
Wasserstein Distance ◽  
Generative Models ◽  
Supplementary Information ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Pharmacological Agents ◽  
Comprehensive Information ◽  
Low Dimensional ◽  
Wasserstein Distances

Abstract An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug–drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, several knowledge graph (KG) embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new KG embedding framework by introducing adversarial autoencoders (AAEs) based on Wasserstein distances and Gumbel-Softmax relaxation for DDI tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation—an inherent flaw in traditional generative models—we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks: link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines. Supplementary information: Supplementary data and code are available at https://github.com/dyf0631/AAE_FOR_KG.

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