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vision-based Human Identification with Face and Nametape Recognition in Aerial Casualty Monitoring System 32

Vision-based Human Identification with Face and Nametape Rec...

引用

32nd IEEE international conference on robot and Human Interactive Communication (RO-MAN)

作者： Lee, Jaeyeon Quist, Ethan Chambers, Jonathan Peel, Justin Roman, Kelly Fisher, Nathan TATRC Ft Detrick MD 21702 USA Arete Arlington VA USA

ISBN: (纸本)9798350336702

In emergency rescue scenarios, rapid identification of human casualties is a critical first step in enhancing emergency medical response. This task can be limited by the physical and cognitive capacity of rescue personnel, who are exposed to significant risk. The use of small unmanned aerial systems (sUAS) equipped with autonomous casualty assessment abilities can reduce these limitations and risks by enabling remote casualty detection, identification, and vitals assessment, providing standoff protection, and eliminating the need for human personnel to access the potentially hazardous scene. This paper presents a vision-based casualty assessment framework and specifically discusses our casualty identification software, which is designed to recognize the faces of casualties and identify their nametapes in images captured by sUAS under realistic conditions. Our approach addresses the limitations of the sUAS-captured long-distance images to enable accurate identification in challenging casualty monitoring situations. The face and nametape recognition algorithms will be integrated into the larger casualty perception framework and embedded into sUAS platforms to assist with emergency rescue operations. The total casualty perception system will detect, identify, and evaluate the condition of casualties from a remote location, providing standoff protection to first responders and rapid information to inform a suitable medical treatment plan.

关键词： Aerial image processing Neural network Face and text recognitions Synthetic text data Human detection identification monitoring Unmanned Aerial Systems

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Unsupervised Action Segmentation of Untrimmed Egocentric Videos 48

Unsupervised Action Segmentation of Untrimmed Egocentric Vid...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Perochon, Sam Oudre, Laurent Université Paris Saclay Université Paris Cité Ens Paris Saclay Cnrs SSA Inserm Gif-sur-YvetteF-91190 France

ISBN: (纸本)9781728163277

The introduction of affordable wearable cameras and eye trackers have led to a massive amount of egocentric (or first-person view) videos, bringing new challenges to the computer vision community for understanding and leveraging the specificities of the egocentric view. This work proposes a novel approach for unsupervised activity segmentation that detects frames corrupted by ego-motion and estimates action boundaries using kernel change-point detection. The approach leverages the visual characteristics of egocentric videos to improve segments' temporal accuracy. We report state-of-the-art performances for unsupervised approaches on two challenging large-scale datasets of untrimmed egocentric videos, EGTEA and EPIC-KITCHEN-55, and on the standard third-person view dataset, 50Salads. © 2023 IEEE.

关键词： Computer vision

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INTERPRETING LATENT REPRESENTATION IN NEURAL RADIANCE FIELDS FOR MANIPULATING OBJECT SEMANTICS 30

INTERPRETING LATENT REPRESENTATION IN NEURAL RADIANCE FIELDS...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Huang, Yu-Shan Huang, Sheng-Yu Hsu, Hao-Yu Wang, Yu-Chiang Frank Natl Taiwan Univ Taipei Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9781728198354

Manipulating 3D objects has been among the active research topic for 3D vision. With the development and success of neural radiance field (NeRF) [1] on scene modeling, synthesizing and manipulating 3D objects using such a representation becomes desirable. In this paper, we introduce a semantic-aware generative NeRF, which is able to interpret the latent representation learned by category-specific generative NeRFs and to achieve editing of particular part attributes. With pretrained generative NeRF, we propose to deploy a semantic segmentor for performing part segmentation on the object category. This allows the rendering of the 2D image and prediction of the corresponding segmentation mask. Our proposed scheme learns to manipulate the resulting latent representation, optimized to edit the object part of interest with varying degrees. We conduct experiments on various object categories on benchmark datasets, and the results successfully verify the effectiveness and practicality of our proposed model.

关键词： 3D computer vision Semantics 3D-aware generative network

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Application Research of Intelligent Educational robot in Interactive Teaching of Secondary Vocational Class

Application Research of Intelligent Educational Robot in Int...

引用

2024 international conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Xiao, Haixian Yancheng Advanced Vocational School of Economics and Trade Yancheng China

ISBN: (纸本)9798331527624

Aiming at the problem of human-computer interaction of teaching robots in secondary vocational schools and aiming to improve the teaching quality, this paper conducts a study on the speech enhancement of secondary vocational school students under the two-level attention mechanism. This paper first conducts theoretical and experimental research on the currently commonly used speech enhancement algorithms. Then, a two-level attention enhancement neural network is established, and the two-level attention enhancement model is added to the conversation of the secondary vocational teaching robot for experimental verification. The experiment proves that compared with the high-resolution channel attention enhancement network, the signal-to-noise comparator (SSNR) of the improved algorithm proposed in this paper can reach 3.15 dB, and the loss value is reduced to 0.985. At the same time, after the noise processing, its time domain waveform is clearer. In short, the speech enhancement algorithm studied in this paper is feasible and effective, which can enable the secondary vocational teaching robot to understand and complete text commands more accurately. © 2024 IEEE.

关键词： Educational robots

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FLOW-BASED ONE-CLASS ANOMALY DETECTION WITH MULTI-FREQUENCY FEATURE FUSION 30

FLOW-BASED ONE-CLASS ANOMALY DETECTION WITH MULTI-FREQUENCY ...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Ma, Wei Lan, Shiyong Huang, Weikang Ma, Yitong Yang, Hongyu Pan, Wei Zheng, Yilin Sichuan Univ Coll Comp Sci Chengdu Peoples R China Natl Key Lab Fundamental Sci Synthet Vision Chengdu Peoples R China

ISBN: (纸本)9781728198354

Anomaly detection in computer vision seeks to identify samples outside of a predefined distribution, including texture defect detection and semantic anomaly detection. However, existing methods are difficult to simultaneously achieve high performance for both types of anomaly detection. To address this issue, we propose a new flow-based anomaly detection method. Firstly, we use semantic features extracted from a pre-trained backbone to learn the distribution of normal data from a semantic perspective. Secondly, we introduce a multi-frequency feature fusion module to aggregate semantic and texture information, which substantially improves performance for both types of anomaly detection at the same time. Extensive experiments on multiple well-known datasets demonstrate that our proposed method performs well in both types of anomaly detection, specially, achieves state-of-the-art performance in one-class anomaly detection. The codes will be available at https://***/SYLan2019/FOADMFFF.

关键词： Semantic anomaly detection Normalizing flow Feature fusion Class attention HiLo attetnion

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Spatiotemporal-Aware Visual Captioning using vision-Language Pre-Training Model

Spatiotemporal-Aware Visual Captioning using Vision-Language...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wu, Shuai Yang, Weidong Wu, Shuyan School of Computer Science Fudan University Shanghai China Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368741

Current visual captioning technologies typically transform 3D/2D visual information into one-dimensional sequential data and employ language models to generate corresponding descriptions. This approach, however, compromises the spatiotemporal information in visual data, making it difficult for models to capture temporal variations and the relative spatial relationships between objects. To address this issue, we propose STPos-VC, a pre-trained vision-language model that maps visual information from the visual vector space to the textual vector space through a visual-text mapper and generates natural language descriptions using a decoder. The mapper incorporates three-dimensional rotational position encoding, which effectively preserves the relative spatiotemporal positional relationships. Furthermore, we pre-train the model on a mixed dataset comprising images and videos through a visual question-answering framework, enabling the model to perform well even with small sample sizes. Experimental results across multiple datasets demonstrate that, compared to existing methods, STPos-VC achieves superior performance in both general-purpose and domain-specific applications. © 2025 IEEE.

关键词： Multimodality Pre-training Spatiotemporal position encoding Visual Language Model

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DLEN: DEEP LAPLACIAN ENHANCEMENT NETWORKS FOR LOW-LIGHT IMAGES 30

DLEN: DEEP LAPLACIAN ENHANCEMENT NETWORKS FOR LOW-LIGHT IMAG...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Wei, Xinjie Chang, Kan Li, Guiqing Huang, Mengyuan Qin, Qingpao Guangxi Univ Sch Comp & Elect Informat Nanning Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781728198354

Enhancing low-light images is challenging as it requires simultaneously handling global and local contents. This paper presents a new solution which incorporates the vision transformer (ViT) into Laplacian pyramid and explores cross-layer dependence within the pyramid. It first applies Laplacian pyramid to decompose the low-light image into a low-frequency (LF) component and several high-frequency (HF) components. As the LF component has a low resolution and mainly includes global attributes, ViT is applied on it to explore the interdependence among global contents. Since there exists strong spatial correlation among different frequency components, the refined features from a lower pyramid layer are used to assist the refinement of upper-layer features. Experiments demonstrate that our approach achieves better performance than state-of-the-art methods, while maintaining a relative small model size and low computational complexity. Our source code and trained model will be released at https://***/Xinjie-Wei/DLEN.

关键词： low-light image enhancement Laplacian pyramid convolutional neural networks vision transformer

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ICEL: Learning with Inconsistent Explanations 48

ICEL: Learning with Inconsistent Explanations

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Liu, Biao Wu, Xiaoyu Yuan, Bo Southern University of Science and Technology Department of Computer Science & Engineering China Rams Lab Huawei China

ISBN: (纸本)9781728163277

Generating the heatmaps is one of the explanation methods to show what regions the model use to predict in vision tasks. GradCAM is a popular approach to provide such heatmaps. However, GradCAM is post-hoc, and its heatmaps are not always meeting human annotations. Inspired by CGC (Contrastive GradCAM consistency), we propose ICEL (InConsistent Explanation Learning) method which introduces inconsistent explanation loss measured by cosine similarity on heatmaps. We show that our method can preserve classification accuracy, while the heatmaps generated by explanation methods are more consistent with human annotations, and the computational complexity is reduced from O(n2) to O(n), compared with CGC. © 2023 IEEE.

关键词： Computer vision

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A Stereo vision-Based Flexible Deflection Measurement System 5

A Stereo Vision-Based Flexible Deflection Measurement System

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5th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Jin, Bohan Huang, Yixiang Wu, Fengqi Zhang, Kaiwen School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai China

ISBN: (纸本)9798350336030

This paper aims at measuring deflection of mainly beam structures in a non-contact, installation-free and marker-less way with the aid of stereo vision, edge detecting, projection transformation, spline interpolation among other data processing methods. A measurement scheme covering the hardware system design and data analyzing is proposed. Things relevant to stereo vision mainly convert a position in image to a real world position. The edge detection is used to recognize features in images that are essential for measuring deflection. The projection to a certain plane is a reasonable design due to the structure of the targeted structures. And spline interpolation compensates for the loss of obtaining the coordinates of discrete points of the target object. Then an experiment is carried out. And the result proves the feasibility of the proposed method. © 2023 IEEE.

关键词： Stereo vision

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JPEG COMPLIANT COMPRESSION FOR DNN vision 30

JPEG COMPLIANT COMPRESSION FOR DNN VISION

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30th IEEE international conference on Image processing (ICIP)

作者： Zheng, Kaixiang Salamah, Ahmed H. Ye, Linfeng Yang, En-Hui University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9781728198354

Conventional image compression techniques are mostly developed for the human visual system. However, with the extensive use of deep neural networks (DNNs), more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we first propose a new distortion measure, dubbed the sensitivity weighted error (SWE). Then, we develop OptS, a DNN-oriented compression algorithm with full JPEG compatibility, which designs optimal quantization tables for DNN models based on SWE. To test the performance of our algorithm, experiments of image classification are conducted on the ImageNet dataset for two prevailing DNN models. Results demonstrate that our algorithm achieves better rate-accuracy (R-A) performance than the default JPEG. For some DNN model, the compression ratio of our algorithm can reach 8.3x(1), reducing the compression rate (bits per pixel, bpp) of the default JPEG by 57.4% with no accuracy loss. Our source code is available at https://***/zkxufo/***.

关键词： Image compression deep learning JPEG quantization table distortion measure

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