Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

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Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0029 ◽

2017 ◽

Vol 108 (1) ◽

pp. 307-318 ◽

Cited By ~ 1

Author(s):

Eleftherios Avramidis

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Machine Translation ◽

State Of The Art ◽

Linear Method ◽

The State ◽

Quality Estimation ◽

Art Methods ◽

Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

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Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽

10.3390/s20123603 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3603

Author(s):

Dasol Jeong ◽

Hasil Park ◽

Joongchol Shin ◽

Donggoo Kang ◽

Joonki Paik

Keyword(s):

Large Scale ◽

Body Shape ◽

State Of The Art ◽

The State ◽

Whole Body ◽

Distinctive Features ◽

Common Features ◽

Siamese Network ◽

Art Methods ◽

Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

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Review of the state-of-the-art methods for Privacy Preserved Classification in Outsourced Environment

2020 International Conference on Innovative Trends in Information Technology (ICITIIT) ◽

10.1109/icitiit49094.2020.9071526 ◽

2020 ◽

Author(s):

Vijayendra Sanjay Gaikwad ◽

K. H. Walse ◽

V. M. Thakare

Keyword(s):

State Of The Art ◽

The State ◽

Art Methods

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MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018513 ◽

2019 ◽

Vol 33 ◽

pp. 8513-8520 ◽

Cited By ~ 10

Author(s):

Jianwen Jiang ◽

Di Bao ◽

Ziqiang Chen ◽

Xibin Zhao ◽

Yue Gao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

State Of The Art ◽

Shape Representation ◽

The State ◽

Shape Retrieval ◽

3D Shape Retrieval ◽

3D Shape ◽

Loop Level ◽

Art Methods

3D shape retrieval has attracted much attention and become a hot topic in computer vision field recently.With the development of deep learning, 3D shape retrieval has also made great progress and many view-based methods have been introduced in recent years. However, how to represent 3D shapes better is still a challenging problem. At the same time, the intrinsic hierarchical associations among views still have not been well utilized. In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval. In this method, multiple groups of views are extracted from different loop directions first. Given these multiple loop views, the proposed MLVCNN framework introduces a hierarchical view-loop-shape architecture, i.e., the view level, the loop level, and the shape level, to conduct 3D shape representation from different scales. In the view-level, a convolutional neural network is first trained to extract view features. Then, the proposed Loop Normalization and LSTM are utilized for each loop of view to generate the loop-level features, which considering the intrinsic associations of the different views in the same loop. Finally, all the loop-level descriptors are combined into a shape-level descriptor for 3D shape representation, which is used for 3D shape retrieval. Our proposed method has been evaluated on the public 3D shape benchmark, i.e., ModelNet40. Experiments and comparisons with the state-of-the-art methods show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks. Our MLVCNN outperforms the state-of-the-art methods by the mAP of 4.84% in 3D shape retrieval task. We have also evaluated the performance of the proposed method on the 3D shape classification task where MLVCNN also achieves superior performance compared with recent methods.

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Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

Applied Sciences ◽

10.3390/app10144791 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4791 ◽

Cited By ~ 1

Author(s):

Pedro Narváez ◽

Steven Gutierrez ◽

Winston S. Percybrooks

Keyword(s):

Wavelet Transform ◽

Heart Sound ◽

State Of The Art ◽

Automatic Segmentation ◽

Automatic Classification ◽

The State ◽

Heart Sounds ◽

Empirical Wavelet Transform ◽

Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

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Relational Prototypical Network for Weakly Supervised Temporal Action Localization

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6760 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11053-11060

Author(s):

Linjiang Huang ◽

Yan Huang ◽

Wanli Ouyang ◽

Liang Wang

Keyword(s):

State Of The Art ◽

Previous Method ◽

The State ◽

Localization Method ◽

Action Localization ◽

Art Methods ◽

Prototype Learning ◽

Weakly Supervised ◽

Temporal Action

In this paper, we propose a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks. We observe two challenges posed by weakly supervision, namely action-background separation and action relation construction. Unlike the previous method, we propose to achieve action-background separation only by the original videos. To achieve this, a clustering loss is adopted to separate actions from backgrounds and learn intra-compact features, which helps in detecting complete action instances. Besides, a similarity weighting module is devised to further separate actions from backgrounds. To effectively identify actions, we propose to construct relations among actions for prototype learning. A GCN-based prototype embedding module is introduced to generate relational prototypes. Experiments on THUMOS14 and ActivityNet1.2 datasets show that our method outperforms the state-of-the-art methods.

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Learning to Transfer Relational Representations through Analogy

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110015 ◽

2019 ◽

Vol 33 ◽

pp. 10015-10016

Author(s):

Gaetano Rossiello ◽

Alfio Gliozzo ◽

Michael Glass

Keyword(s):

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

The State ◽

Large Set ◽

Relational Information ◽

Siamese Network ◽

Distant Supervision ◽

Novel Approach ◽

Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

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Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/683 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chaotao Chen ◽

Jinhua Peng ◽

Fan Wang ◽

Jun Xu ◽

Hua Wu

Keyword(s):

State Of The Art ◽

The State ◽

Target Response ◽

Empirical Results ◽

Art Methods ◽

Mapping Mechanism ◽

The One ◽

Selection Of

In human conversation an input post is open to multiple potential responses, which is typically regarded as a one-to-many problem. Promising approaches mainly incorporate multiple latent mechanisms to build the one-to-many relationship. However, without accurate selection of the latent mechanism corresponding to the target response during training, these methods suffer from a rough optimization of latent mechanisms. In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses. For accurate optimization of latent mechanisms, a posterior mapping selection module is designed to select the corresponding mapping module according to the target response for further optimization. We also introduce an auxiliary matching loss to facilitate the optimization of posterior mapping selection. Empirical results demonstrate the superiority of our model in generating multiple diverse and informative responses over the state-of-the-art methods.

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On-Line Visual Tracking with Occlusion Handling

Sensors ◽

10.3390/s20030929 ◽

2020 ◽

Vol 20 (3) ◽

pp. 929 ◽

Cited By ~ 1

Author(s):

Tharindu Rathnayake ◽

Amirali Khodadadian Gostar ◽

Reza Hoseinnezhad ◽

Ruwan Tennakoon ◽

Alireza Bab-Hadiashar

Keyword(s):

Target Tracking ◽

Visual Tracking ◽

State Of The Art ◽

The State ◽

Occlusion Handling ◽

Art Methods ◽

Multi Target Tracking ◽

Motion Features ◽

On Line ◽

Tracking Point

One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.

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