Proceedings of the 6th International Conference on Digital Signal Processing最新文献_第2页

LCNet: Location Combination for Object Detection LCNet:用于目标检测的位置组合

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529596

Xin Yi, Bo Ma

{"title":"LCNet: Location Combination for Object Detection","authors":"Xin Yi, Bo Ma","doi":"10.1145/3529570.3529596","DOIUrl":"https://doi.org/10.1145/3529570.3529596","url":null,"abstract":"Object detection is a widely studied task in the computer vision field. In recent years, some milestone approaches and solid benchmarks have been proposed, which significantly boosts the development of related researches. The previous object detection methods follow a paradigm: the classification head and the regression head share the same feature extracted by the backbone network. In this paper, we revisit this paradigm for two-stage detectors and prove that the regression head can achieve better results by using the local features. In our proposed Location Combination Networks (LCNet), we extract the effective region of the feature in a Laplace way, and we introduce auxiliary confidence gain loss, Intersection over Union (IoU) gain loss, and distribution loss to guide its convergence. In the classification head, we combine these local features into the global feature for better classification. In the regression head, by ranking these effective regions in the spatial dimension, we can select the local features closest to each foreground boundary and use the selected features to predict the offset of each foreground boundary. Finally, we combine the locations of the four boundaries to obtain the final bounding box prediction. Extensive experimental results on the MS COCO benchmark validate the effectiveness of our proposed method.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131990430","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

Inference and Prediction in Big Data Using Sparse Gaussian Process Method 基于稀疏高斯过程的大数据推理与预测

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529580

Leta Yobsan Bayisa, Weidong Wang, Qing-xian Wang, Meseret Debele Gurmu, Lamessa Bona Debela

引用次数: 0

Direction-of-Arrival Estimation of Acoustic Sources Using Acoustic Array Based on SOM and BP Neural Network 基于SOM和BP神经网络的声阵列声源到达方向估计

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529605

Baoliang Sun, C. Jiang, Yuguang Song, K. Xue, Weike Shi

引用次数: 0

A Frequency-Dependent Head-Related Transfer Functions Modeling Approach Based on Spherical Harmonic Expansion: FREQUENCY-DEPENDENT HRTF MODELING 基于球谐展开的频率相关头部传递函数建模方法:频率相关HRTF建模

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529603

Yunan Wang, Hongbo Zhao, W. Feng, Dingding Yao

引用次数: 0

Human Motion Generation Using Variational Recurrent Neural Network 基于变分递归神经网络的人体运动生成

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529588

Makoto Murakami, Takahiro Ikezawa

{"title":"Human Motion Generation Using Variational Recurrent Neural Network","authors":"Makoto Murakami, Takahiro Ikezawa","doi":"10.1145/3529570.3529588","DOIUrl":"https://doi.org/10.1145/3529570.3529588","url":null,"abstract":"∗ Human motion control, edit, and synthesis are important tasks to create 3D computer graphics video games or movies, because some characters act like humans in most of them. The purpose of this study is to construct a system which can generate various natural character motions. In this study, we consider that the process of human motion generation is complicated and non-linear, and it can be modeled by deep neural network. Since the motion generation process (deep neural network parameters) cannot be observed di-rectly, it needs to be estimated by learning from observable human motion data recorded by motion capture system. On the other hand, the process of inference which is opposite to the generation is also expressed by deep neural network. And inference and generation are performed for human motion data, and the parameters of the both deep neural networks are optimized based on the criteria that the original motion should be obtained through inference and generation processes. In this study, we constructed a human motion generative model using recurrent neural network and variational autoencoders, and confirmed that various human motions can be generated from a low-dimensional latent space.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485217","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

An Effective Method for Weak Multi-target Detection and Tracking in Clutter Environment 杂波环境下弱多目标检测与跟踪的一种有效方法

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529593

Chun Li, X. Bai, Juan Zhao, T. Shan

引用次数: 1

Iriscode Matching Comparator to Improve Decidability of Human Iris Recognition 提高虹膜识别可判决性的虹膜码匹配比较器

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529591

Yenlung Lai, Tong-Yuen Chai, MingJie Lee, B. Goi

引用次数: 0

Enhancing Kindergarten Learning Environment via Interactive Projection Design: A Concept Framework 以互动投影设计提升幼儿园学习环境:一个概念框架

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529585

Bing Lai

{"title":"Enhancing Kindergarten Learning Environment via Interactive Projection Design: A Concept Framework","authors":"Bing Lai","doi":"10.1145/3529570.3529585","DOIUrl":"https://doi.org/10.1145/3529570.3529585","url":null,"abstract":"A child's thoughts, feelings, and conduct will be influenced by their physical environment. The phrase \"physical environment\" relates to how structures such as classrooms and schools are organised and designed. As a result, a comfortable kindergarten atmosphere is critical for increasing children's productivity, learning, and well-being. The advancement of digital technology has significantly improved the living conditions of children. Create a system that enables children to interact autonomously while learning and provides multiple interactive modalities, as well as intuitive interactive spaces in kindergarten, based on the rapid growth of interactive technology. The purpose of this paper is to identify the planning criteria for specific interactive projection methods used in kindergarten, to present the fundamental design concepts, and to discuss various aspects of the interactive projection mechanism, with the goal of providing a safe living space, entertainment, and learning for children's experience development in terms of motivation, self-involvement, joy, physical needs, communication, and a balanced flow of experiences. It is intended that this conceptual framework would provide some direction for kindergarten instructors and designers in terms of improving the physical environment's quality, particularly in terms of providing interactive environments for children that fulfil current needs.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506739","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

Multilinear Compressed Sensing using Tensor Least Angle Regression (T-LARS) 基于张量最小角回归(T-LARS)的多线性压缩感知

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529571

Ishan Wickramasingha, S. Sherif

引用次数: 0

Exploration of Depth Algorithm Applied to Time-Frequency Image Processing Method of ECG Signal 深度算法应用于心电信号时频图像处理方法的探索

Proceedings of the 6th International Conference on Digital Signal Processing Pub Date : 2022-02-25 DOI: 10.1145/3529570.3529608

Peng-yu Ran, Jinjie Xie, Jingwen Wang

{"title":"Exploration of Depth Algorithm Applied to Time-Frequency Image Processing Method of ECG Signal","authors":"Peng-yu Ran, Jinjie Xie, Jingwen Wang","doi":"10.1145/3529570.3529608","DOIUrl":"https://doi.org/10.1145/3529570.3529608","url":null,"abstract":"The classification of arrhythmia is of great significance for the prevention and treatment of heart disease. Based on the deep learning algorithm, it has excellent performance in image classification and recognition. The ECG signal is divided into two cases of abnormal interval and abnormal amplitude to perform signal image classification. The time-domain abnormal signal is directly processed into a two-dimensional image set, and the time domain information of the amplitude abnormal signal is Fourier transformed to obtain a two-dimensional time-frequency image set, and different image sets are migrated to VGG16 After the model is reduced by the PCA algorithm, it can clearly distinguish between normal ECG signals and ECG signals with abnormal intervals or amplitude abnormalities. Finally, after a fine-tuned fully connected layer, the abnormal intervals and amplitudes can be obtained. The accuracy rates of abnormal classification were 96.15% and 92.98%, respectively. After the image processing of the ECG signal, this method can effectively distinguish the abnormal signal from the normal signal.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"407 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123381308","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