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Kinect Sensor-Based Indoor 3D Reconstruction - A Pilot Study 7

Kinect Sensor-Based Indoor 3D Reconstruction - A Pilot Study

7th ieee International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Amruth Mandappa, T.S. Pavan Kumar, B.N. Chakravarthi, Bharatesh Manjunath, V. Girish, N. Prabhu Prasad, M. Iiit Dharwad Dept. of Cse India Iiit Sri City Dept. of Cse India Scai Arizona State University Active Perception Group United States Iiit Dharwad Dept. of Data Science and Intelligent Systems India

ISBN: (纸本)9798350343144

This study investigates the application of a low-cost Kinect sensor for 3D reconstruction inside. The project uses structured light technologies and the Kinect depth sensor to collect and rebuild interior surroundings in three dimensions. The primary goal is to provide realistic 3D representations of interior locations for a variety of purposes. Using the Kinect sensor and specialized software, interior scenes are scanned and then recreated. The outcomes indicate how well the Kinect-based method works for producing accurate indoor 3D reconstructions, and also suggest possible uses for it in a number of industries, including robotics, virtual reality, and architectural visualization. This work advances the use of Kinect sensors for indoor 3D reconstruction, providing new opportunities for improved spatial comprehension and virtual environment engagement. © 2023 ieee.

关键词： Three dimensional computer graphics

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Static Image Analysis of Two Dimensional Projection of Particles

Static Image Analysis of Two Dimensional Projection of Parti...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Pranav, A. Shashank, B. Pranav, S. Reddy, D.K. Bharath Reddy, M. Aswartha Dhamija, Pulkit Alva, A. Sahaj Moharir, Minal Hemavathy Bothra, Vivek Dept. of Computer Science RV College of Engineering Bangalore India Culture Instruments LLP Bangalore India

ISBN: (纸本)9798350325188

Particle visualization and analysis using static image analysis has emerged as a valuable technique in the study of particle behaviour and characteristics in a two-dimensional environment. The significance of this analysis unveils essential insights into particle size, shape, and overarching characteristics. This paper presents a comprehensive methodology for particle analysis using image processing, based on a combination of previously advocated methods and papers which have been referenced to help the reader further understand the concept of static image analysis of particles. Advanced imaging equipment is employed for data acquisition, followed by preprocessing techniques. The watershed segmentation algorithm is applied to precisely separate individual particles, even in complex cases. Geometric properties are computed, enabling insights into particle size, shape, and characteristics. Statistical analysis allows for comparisons across datasets, facilitating better understanding and anomaly detection. This methodology serves as a powerful tool for particle analysis in nanotechnology, fluid dynamics, and biomedical research, leading to advancements in various applications. © 2023 ieee.

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Multi-Level Second-Order Few-Shot Learning

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 2111-2126页

作者： Zhang, Hongguang Li, Hongdong Koniusz, Piotr AMS Syst Engn Inst Shanghai 100141 Peoples R China Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 0200 Australia CSIRO Data61 Acton ACT 2601 Australia

We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction, and we use self-supervised discriminating mechanisms. As Second-order Pooling (SoP) is popular in image recognition, we employ its basic element-wise variant in our pipeline. The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning. As SoP can handle convolutional feature maps of varying spatial sizes, we also introduce image inputs at multiple spatial scales into MlSo. To exploit the discriminative information from multi-level and multi-scale features, we develop a Feature Matching (FM) module that reweights their respective branches. We also introduce a self-supervised step, which is a discriminator of the spatial level and the scale of abstraction. Our pipeline is trained in an end-to-end manner. With a simple architecture, we demonstrate respectable results on standard datasets such as Omniglot, mini-ImageNet, tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and mini-MIT.

关键词： Task analysis Pipelines Image recognition visualization Feature extraction Training Streaming media Few-shot learning second-order statistics image classification action recognition

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IoT Based ECG Monitoring and Detection System Using Pan-Tompkins Algorithm and Ubidots Cloud

IoT Based ECG Monitoring and Detection System Using Pan-Tomp...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Pasha, Afroz Nagaraja, S.R. Presidency University School of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350317060

The Internet of Things (IoT) has revolutionized the healthcare industry by enabling real-time monitoring of patient vital signs. Electrocardiogram (ECG) monitoring plays a crucial role in diagnosing cardiac abnormalities. In this research article, we propose an IoT-based ECG detection system that utilizes the Pan-Tompkins algorithm for accurate and reliable ECG signal processing. The system enables remote monitoring of ECG signals, facilitating early detection of cardiac anomalies and timely intervention. This article presents the design, implementation, and evaluation of the proposed IoT-based ECG detection system, highlighting its effectiveness and potential impact on healthcare. This paper illustrates the implementation of Internet of Things (IoT) technology in the healthcare domain, focusing on a proposed system for remotely monitoring the ECG of patients. The system's key components include the Arduino Uno, an ECG Sensor, and an IoT Cloud for real-time storage and visualization of ECG data. The primary goal of the system is to accurately measure the heart rate of distant patients and facilitate continuous monitoring of their ECG signals from a remote location. © 2023 ieee.

关键词： Electrocardiograms

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Inter-Intra Modal Representation Augmentation With DCT-Transformer Adversarial Network for Image-Text Matching

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 8933-8945页

作者： Chen, Chen Wang, Dan Song, Bin Tan, Hao Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Guangdong OPPO Mobile Telecommun Corp Software Engn Div Dongguan 523860 Peoples R China

Image-text matching has become a challenging task in the multimedia analysis field. Many advanced methods have been used to explore local and global cross-modal correspondence in matching. However, most methods ignore the importance of eliminating potential irrelevant features in the original features of each modality and cross-modal common feature. Moreover, the features extracted from regions in images and words in sentences contain cluttered background noise and different occlusion noise, which negatively affects alignment. Different from these methods, we propose a novel DCT-Transformer Adversarial Network (DTAN) for image-text matching in this paper. This work can obtain an effective metric based on two aspects: i) DCT-Transformer uses DCT (Discrete Cosine Transform) method based on a transformer mechanism to extract multi-domain common representations and eliminate irrelevant features from different modalities (inter-modal). Among them, DCT divides multi-modal content into chunks of different frequencies and quantifies them. ii) The adversarial network introduces an adversary idea by combining the original features of various single modalities and the multi-domain common representation, alleviating the background noise within each modality (intra-modal). The proposed adversarial feature augmentation method can easily obtain the common representation that is only useful for alignment. Extensive experiments are completed on the benchmark datasets Flickr30 K and MS-COCO, demonstrating the superiority of the DTAN model over the state-of-the-art methods.

关键词： Feature extraction Transformers Discrete cosine transforms Background noise Task analysis Semantics visualization Image-text matching data augmentation adversarial learning transformer DCT

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Coarse-to-Fine Feedback Guidance Based Stereo Image Quality Assessment Considering Dominant Eye Fusion

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 8855-8867页

作者： Chang, Yongli Li, Sumei Liu, Anqi Jin, Jie Xiang, Wei Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China La Trobe Univ Sch Engn & Math Sci Melbourne Vic 3086 Australia

Considering that the human brain always follows a coarse-to-fine (low-to-high spatial frequency) visual processing and fusion mechanism, we propose a coarse-to-fine feedback guidance based stereo image quality assessment (SIQA) network which considers a coarse-to-fine feedback guidance and adaptive dominant eye mechanism. The proposed network consists of two main sub-network streams, each of which has three branches to extract low, middle and high spatial frequency information in parallel. To better realize the guidance of the high-level features in the low spatial frequency branch to the low-level features in the high spatial frequency branch, an information feedback guidance module (IFGM) is proposed, which realizes a top-down guidance mechanism in each sub-network stream. Simultaneously, according to the theory of ocular dominance in human visual system (HVS), we design an adaptive bi-directional parallax-based binocular fusion module (BPBFM), which synthesizes two types of fusion feature by taking the left and right view features as dominant eye input. Furthermore, in order to obtain the better perceptual quality of stereo images, we design a weighted fusion strategy to weigh the quality scores from the two types of fusion features obtained by using an ensemble model with two multi-layer perceptrons (MLPs). The experimental results on four public stereo image datasets show that the proposed method is superior to the mainstream metrics and achieves an excellent performance.

关键词： visualization Feature extraction Image quality Adaptive systems Streaming media data mining Bidirectional control Stereo image quality assessment coarse-to-fine feedback guidance bi-directional parallax attention mechanism convolutional neural network

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Design of 3D Reconstruction and FDM Printing Testbed for Agricultural Products

Design of 3D Reconstruction and FDM Printing Testbed for Agr...

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2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Gao, Zihang Zhao, Nannan Wenzhou Vocational College of Science and Technology Wenzhou China

ISBN: (纸本)9798350359244

Computer vision detection is widely used in the field of agricultural product variety identification and quality inspection. However, the traditional agricultural product phenotypic detection method based on two-dimensional (2D) images lacks spatial dimension information and is difficult to extract comprehensive agricultural product phenotypic parameters. Therefore, it is of great significance to establish a three-dimensional (3D) model of agricultural products and extract comprehensive phenotypic parameters of agricultural products. At present, the construction method of 3D models of agricultural products is single, the molding efficiency is low and the degree of visualization is limited, making it difficult to achieve accurate evaluation of system performance. We designed a testbed for agricultural product that combines 3D reconstruction and FDM printing technology. Experimental results show that the testbed can achieve 3D reconstruction of agricultural product data information and achieve accurate evaluation of FDM 3D printing performance. © 2023 ieee.

关键词： Agricultural products

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Fine-Grained Feature Generation for Generalized Zero-Shot Video Classification

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ieee TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1599-1612页

作者： Hong, Mingyao Zhang, Xinfeng Li, Guorong Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China

Generalized zero-shot video classification aims to train a classifier to classify videos including both seen and unseen classes. Since the unseen videos have no visual information during training, most existing methods rely on the generative adversarial networks to synthesize visual features for unseen classes through the class embedding of category names. However, most category names only describe the content of the video, ignoring other relational information. As a rich information carrier, videos include actions, performers, environments, etc., and the semantic description of the videos also express the events from different levels of actions. In order to use fully explore the video information, we propose a fine-grained feature generation model based on video category name and its corresponding description texts for generalized zero-shot video classification. To obtain comprehensive information, we first extract content information from coarse-grained semantic information (category names) and motion information from fine-grained semantic information (description texts) as the base for feature synthesis. Then, we subdivide motion into hierarchical constraints on the fine-grained correlation between event and action from the feature level. In addition, we propose a loss that can avoid the imbalance of positive and negative examples to constrain the consistency of features at each level. In order to prove the validity of our proposed framework, we perform extensive quantitative and qualitative evaluations on two challenging datasets: UCF101 and HMDB51, and obtain a positive gain for the task of generalized zero-shot video classification.

关键词： visualization Semantics Task analysis Training Generative adversarial networks Feature extraction data models Zero-shot learning feature generation video classification

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Dual Relation Network for Scene Text Recognition

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 4094-4107页

作者： Li, Ming Fu, Bin Chen, Han He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Hong Kong 518055 Guangdong Peoples R China Shanghai AI Lab Shanghai 518055 Peoples R China

Local visual and long-range contextual features yield two complementary cues for human reading text in natural scene. Existing scene text recognition methods mainly extract local features at a low level and then model long-range dependencies at a high level, this sequential pipeline may be sub-optimal to construct complete and effective representation. Except for high-level features, long-range contextual relation is of importance in low-level features as well since it can help separate different characters based on the intervals between characters and thus enhance the character features. To address this issue, we develop a dual relation module to extract complementary features in a parallel manner for scene text recognition, which consists of a local visual branch and a long-range contextual branch. The local visual branch employs a topological-aware operation to model intra-character characteristic and extract discriminative features of different characters. Meanwhile, the long-range contextual branch utilizes a simple but effective strategy to incorporate inter-character relations into feature maps. Our dual relation module is a plug-and-play block which can be easily incorporated into modern deep architectures. Experimental results demonstrate that our methods achieved top performance on several standard benchmarks. Code and models will become publicly available in the future.

关键词： Feature extraction Text recognition visualization Task analysis Context modeling data mining Convolution Scene text recognition scene optical character recognition deep learning

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UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

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ieee/CAA Journal of Automatica Sinica 2023年第3期10卷 632-645页

作者： Xiaoyu Liu Ting Ma Zhixuan Tang Xiaohan Qin Haibo Zhou Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada IEEE

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication ***,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance *** this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented ***,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully *** characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol ***,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit ***,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over ***,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic *** simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

关键词： Discrete event simulation(DES) mega-constellation network dynamics performance evaluation simulation architecture design

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