Int. J. Semantic Comput.最新文献_第8页

Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion 基于核- dmcca特征融合的判别鲁棒头部姿态和凝视估计

Int. J. Semantic Comput. Pub Date : 2020-03-01 DOI: 10.1142/s1793351x20500014

Salah Rabba, M. Kyan, Lei Gao, A. Quddus, A. S. Zandi, L. Guan

{"title":"Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion","authors":"Salah Rabba, M. Kyan, Lei Gao, A. Quddus, A. S. Zandi, L. Guan","doi":"10.1142/s1793351x20500014","DOIUrl":"https://doi.org/10.1142/s1793351x20500014","url":null,"abstract":"There remain outstanding challenges for improving accuracy of multi-feature information for head-pose and gaze estimation. The proposed framework employs discriminative analysis for head-pose and gaze estimation using kernel discriminative multiple canonical correlation analysis (K-DMCCA). The feature extraction component of the framework includes spatial indexing, statistical and geometrical elements. Head-pose and gaze estimation is constructed by feature aggregation and transforming features into a higher dimensional space using K-DMCCA for accurate estimation. The two main contributions are: Enhancing fusion performance through the use of kernel-based DMCCA, and by introducing an improved iris region descriptor based on quadtree. The overall approach is also inclusive of statistical and geometrical indexing that are calibration free (does not require any subsequent adjustment). We validate the robustness of the proposed framework across a wide variety of datasets, which consist of different modalities (RGB and Depth), constraints (wide range of head-poses, not only frontal), quality (accurately labelled for validation), occlusion (due to glasses, hair bang, facial hair) and illumination. Our method achieved an accurate head-pose and gaze estimation of 4.8∘ using Cave, 4.6∘ using MPII, 5.1∘ using ACS, 5.9∘ using EYEDIAP, 4.3∘ using OSLO and 4.6∘ using UULM datasets.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"85 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks 基于生成对抗网络的SAR图像水体和陆地的改进语义分割

Int. J. Semantic Comput. Pub Date : 2020-03-01 DOI: 10.1142/s1793351x20400036

M. Pai, Vaibhav Mehrotra, Ujjwal Verma, R. Pai

{"title":"Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks","authors":"M. Pai, Vaibhav Mehrotra, Ujjwal Verma, R. Pai","doi":"10.1142/s1793351x20400036","DOIUrl":"https://doi.org/10.1142/s1793351x20400036","url":null,"abstract":"The availability of computationally efficient and powerful Deep Learning frameworks and high-resolution satellite imagery has created new approach for developing complex applications in the field of remote sensing. The easy access to abundant image data repository made available by different satellites of space agencies such as Copernicus, Landsat, etc. has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. The challenging research problem in this direction is the accurate identification and subsequent segmentation of surface water in images in the microwave spectrum. In the recent years, deep learning methods for semantic segmentation are the preferred choice given its high accuracy and ease of use. One major bottleneck in semantic segmentation pipelines is the manual annotation of data. This paper proposes Generative Adversarial Networks (GANs) on the training data (images and their corresponding labels) to create an enhanced dataset on which the networks can be trained, therefore, reducing human effort of manual labeling. Further, the research also proposes the use of deep-learning approaches such as U-Net and FCN-8 to perform an efficient segmentation of auto annotated, enhanced data of water body and land. The experimental results show that the U-Net model without GAN achieves superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.9923. However, when augmented with GANs, the results saw a rise in these metrics with PA of 0.99 and F1 score of 0.9954.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134281043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Deep Learning-Based Stair Segmentation and Behavioral Cloning for Autonomous Stair Climbing 基于深度学习的自主爬楼梯分割与行为克隆

Int. J. Semantic Comput. Pub Date : 2019-12-26 DOI: 10.1142/s1793351x1940021x

Navid Panchi, Khush Agrawal, Unmesh Patil, Aniket Gujarathi, Aman Jain, Harsha Namdeo, S. Chiddarwar

引用次数: 3

Towards Adaptive Ontology Visualization - Predicting User Success from Behavioral Data 面向自适应本体可视化——从行为数据预测用户成功

Int. J. Semantic Comput. Pub Date : 2019-12-26 DOI: 10.1142/s1793351x1940018x

Bo Fu, B. Steichen, Wenlu Zhang

引用次数: 1

Motion Planning and Control with Randomized Payloads on Real Robot Using Deep Reinforcement Learning 基于深度强化学习的真实机器人随机载荷运动规划与控制

Int. J. Semantic Comput. Pub Date : 2019-12-26 DOI: 10.1142/s1793351x19400233

A. Demir, Volkan Sezer

引用次数: 1

Learning-Based Adaptive Management of QoS and Energy for Mobile Robotic Missions 基于学习的移动机器人任务QoS和能量自适应管理

Int. J. Semantic Comput. Pub Date : 2019-12-01 DOI: 10.1142/s1793351x19400221

Dinh-Khanh Ho, K. B. Chehida, Benoît Miramond, M. Auguin

引用次数: 0

Decrease Product Rating Uncertainty Through Focused Reviews Solicitation 通过集中评论征集减少产品评级的不确定性

Int. J. Semantic Comput. Pub Date : 2019-12-01 DOI: 10.1142/s1793351x19400208

Nhat X. T. Le, Ryan Rivas, James M. Flegal, Vagelis Hristidis

{"title":"Decrease Product Rating Uncertainty Through Focused Reviews Solicitation","authors":"Nhat X. T. Le, Ryan Rivas, James M. Flegal, Vagelis Hristidis","doi":"10.1142/s1793351x19400208","DOIUrl":"https://doi.org/10.1142/s1793351x19400208","url":null,"abstract":"Customer reviews are an essential resource to reduce an online product’s uncertainty, which has been shown to be a critical factor for its purchase decision. Existing e-commerce platforms typically ask users to write free-form text reviews, which are sometimes augmented by a small set of predefined questions, e.g. “rate the product description’s accuracy from 1 to 5.” In this paper, we argue that this “passive” style of review solicitation is suboptimal in achieving low-uncertainty “review profiles” for products. Its key drawback is that some product aspects receive a very large number of reviews while other aspects do not have enough reviews to draw confident conclusions. Therefore, we hypothesize that we can achieve lower-uncertainty review profiles by carefully selecting which aspects users are asked to rate. To test this hypothesis, we propose various techniques to dynamically select which aspects to ask users to rate given the current review profile of a product. We use Bayesian inference principles to define reasonable review profile uncertainty measures; specifically, via an aspect’s rating variance. We compare our proposed aspect selection techniques to several baselines on several review profile uncertainty measures. Experimental results on two real-world datasets show that our methods lead to better review profile uncertainty compared to aspect selection baselines and traditional passive review solicitations. Moreover, we present and evaluate a hybrid solicitation method that combines the advantages of both active and passive review solicitations.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Feature Learning Methods for Voice Disorder Detection 语音障碍检测的特征学习方法评价

Int. J. Semantic Comput. Pub Date : 2019-12-01 DOI: 10.1142/s1793351x19400191

Hongzhao Guan, Alexander Lerch

引用次数: 0

Wavelet-packets Associated with Support Vector Machine Are Effective for Monophone Sorting in Music Signals 结合支持向量机的小波包对音乐信号中的单声道进行分类是有效的

Int. J. Semantic Comput. Pub Date : 2019-09-01 DOI: 10.1142/s1793351x19500028

Rafael Rubiati Scalvenzi, R. Guido, N. Marranghello

引用次数: 2

User Perception of Situated Product Recommendations in Augmented Reality 增强现实中定位产品推荐的用户感知

Int. J. Semantic Comput. Pub Date : 2019-09-01 DOI: 10.1142/s1793351x19400129

Brandon Huynh, Adam Ibrahim, YunSuk Chang, Tobias Höllerer, J. O'Donovan

引用次数: 5