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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18111 条记录，以下是161-170 订阅

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SEGGUARD: DEFENDING SCENE SEGMENTATION AGAINST ADVERSARIAL PATCH ATTACK 31

SEGGUARD: DEFENDING SCENE SEGMENTATION AGAINST ADVERSARIAL P...

引用

2024 international conference on Image processing

作者： Gittings, Thomas Schneider, Steve Collomosse, John Univ Surrey Ctr Vision Speech & Signal Proc CVSSP Surrey England Adobe Res San Jose CA USA

ISBN: (纸本)9798350349405;9798350349399

Adversarial Patch Attacks (APAs) induce prediction errors by inserting carefully crafted regions into images. This paper presents the first defence against APAs for deep networks that perform semantic segmentation of scenes. We show that a conditional generator can be trained to produce patches on demand targeting specific classes and achieving superior performance versus conventional pixel-optimised patch attacks. We then leverage this generator along with the segmentation network as part of a generative adversarial network, which trains the model to ignore the adversarial patches produced by the generator, while simultaneously training the generator to produce updated patches to attack the fine-tuned network. We show that our process confers strong protection against adversarial patches, and that this protection generalises to traditional pixel-optimised adversarial patches.

关键词： Semantic segmentation Adversarial Patches Adversarial Attack

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Intelligent patrol wheeled robot based on image recognition 3

Intelligent patrol wheeled robot based on image recognition

引用

3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Wu, Wei Lv, Xinyue Pei, Zeyu An, Changying Ning, Jixiang Gao, Zhenzhen College of Engineering China University of Petroleum-Beijing at Karamay Karamay834000 China

ISBN: (纸本)9781510681880

In recent years, with the development of artificial intelligence technology, intelligent robots are more and more widely used in many fields. In this paper, an intelligent patrol wheeled robot based on image recognition is designed and implemented. The robot utilizes Raspberry PI as its core control unit and integrates sensing equipment such as cameras and ultrasonic sensors. The functions of autonomous navigation, intelligent obstacle avoidance and path tracking are improved through computer vision and machine learning technology. The main technologies include OpenCV for image processing, HOG feature extraction and SVM algorithm for traffic sign recognition, and traffic light detection system based on color recognition. In addition, this paper also discusses the use of positional PID control algorithm to achieve visual lane keeping scheme. Through detailed system design and experimental verification, this paper shows the performance benefits of the intelligent robot in practical applications, and provides a new idea for the future research of intelligent robots. It is expected that the robot has a wide range of application potential in intelligent transportation, public safety and other fields. © 2024 SPIE.

关键词： Intelligent robots

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INTERACTIVE TEACHING FOR FINE-GRANULAR FEW-SHOT OBJECT RECOGNITION USING vision TRANSFORMERS 31

INTERACTIVE TEACHING FOR FINE-GRANULAR FEW-SHOT OBJECT RECOG...

引用

2024 international conference on Image processing

作者： Keller, Philip Jost, Daniel Roennau, Arne Dillmann, Ruediger FZI Res Ctr Informat Technol Karlsruhe Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350349405;9798350349399

In real-world few-shot image classification tasks the lack of abundant data makes training and testing very challenging. The classification model must learn the most meaningful features using only a few sample images without context knowledge. Here, interpretability methods for deep models are helpful for increased comprehensibility and verification. However, these advantages are limited without the ability to correct the model directly. Therefore, we propose an interpretable approach for few-shot object recognition that includes optional interactive teaching to close the feedback loop. We leverage pretrained vision transformers as backbones and a part-based inference particularly favors interpretability. We use a visual concept bank to translate semantic visual features between the human and the model. Even without any human interaction, our model performs competitively compared to state-of-the-art methods in few-shot image classification tasks. Beyond that, we demonstrate the benefits of our interactive interfaces. We show how they can significantly improve the robustness in fine-grained recognition tasks and help to quickly adapt the model without complex fine-tuning.

关键词： Few-Shot Learning Interactive Learning Part-based Recognition vision Transformers

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Age and Gender Prediction using Paralinguistic Features of EEG signals and Machine Learning 6

Age and Gender Prediction using Paralinguistic Features of E...

引用

6th international conference on Communications signal processing and their Applications

作者： Alamr, Reema Alsumari, Walaa Alotaiby, Turky King Saud Univ Coll Comp Sci & Informat Technol Riyadh Saudi Arabia King Abdulaziz City Sci & Technol Inst Artificial Intelligence & Robot Technol Riyadh Saudi Arabia

ISBN: (纸本)9798350384826;9798350384819

The development of brain-computer interfaces (BCI) has sparked significant interest in leveraging electroencephalography (EEG) data for diverse applications. One of the applications is age and gender prediction of individuals. This paper introduces a novel approach that harnesses the potential of machine learning models, specifically Support Vector Machines (SVM) and Random Forest (RF), in conjunction with paralinguistic feature extraction to achieve highly accurate age and gender prediction. This study used a dataset consisting of EEG recordings from 64 subjects in a relaxed position, with both open and closed eyes. By extracting paralinguistic features from the EEG signals, subtle variations in brain activity associated with age and gender differences are captured. Through extensive experimentation and rigorous evaluation, the SVM model demonstrated exceptional performance, achieving an accuracy of 99.61% in both age and gender estimation. These remarkable results highlight the effectiveness of the proposed approach and the potential of paralinguistic feature extraction on EEG data as robust indicators of age and gender.

关键词： Brain-computer interfaces EEG Paralinguistic features Age prediction Gender prediction Support Vector Machines Random Forest

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SC-LSTM based Human Action Recognition

SC-LSTM based Human Action Recognition

引用

9th international conference on signal and Image processing (ICSIP)

作者： Kang, Naixin Yi, Pengfei Geng, Qingshu Dong, Jing Liu, Rui Wang, Ling Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China

ISBN: (纸本)9798350350920

Human Action Recognition is one of the most applied research directions in the field of Computer vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human Action Recognition Machine Learning Computer vision Human-Computer Interaction Security

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Environment-aware Strategy for Multi-sensor Fusion Based on Improved YOLO Networks 8

Environment-aware Strategy for Multi-sensor Fusion Based on ...

引用

8th international conference on Imaging, signal processing and Communications (ICISPC)

作者： Zhao, Zhanfeng Su, Huiying Sun, Mengzhe Harbin Inst Technol Weihai Sch Informat Sci & Engn Weihai Peoples R China

ISBN: (纸本)9798350367164;9798350367157

The environment perception technology of patrol robots within park areas faces challenges such as low accuracy in detecting small-scale targets and high false positive and false negative rates in target detection under low-light conditions. This paper addresses these issues by proposing several effective optimization methods. Extensive extensions and improvements are conducted on mainstream network models using the ApolloScape dataset to further enhance the accuracy of multi-target recognition within park environments. To overcome the limitations of a single sensor, a perception strategy based on the fusion of 3D and 2D detectors' image results is designed. The 3D detector adopts an improved Complex-YOLOv4 network, while the 2D detector uses an enhanced YOLOv5s network, primarily focusing on vehicles and pedestrians. Decision-level fusion of the two sensors effectively reduces false positives and false negatives in target detection under adverse environmental conditions. Experimental tests demonstrate that the proposed perception method achieves good real-time performance and accuracy in normal, adverse, and nighttime conditions, showcasing high robustness to changes in external environments.

关键词： ROS robot Environment Perception YOLO Road Detection Attention Mechanisms

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MVCRACKVIT: ROBUST MULTI-VIEW CRACK DETECTION FOR POINT CLOUD SEGMENTATION USING VIEW ATTENTION 31

MVCRACKVIT: ROBUST MULTI-VIEW CRACK DETECTION FOR POINT CLOU...

引用

2024 international conference on Image processing

作者： Benz, Christian Rodehorst, Volker Bauhaus Univ Weimar Comp Vis Engn Weimar Germany

ISBN: (纸本)9798350349405;9798350349399

Adapting computer vision algorithms for inspecting civil structures brings significant societal benefits. Images captured from civil structures often exhibit distinct overlap, typically to perform 3D reconstruction. In this work, the potential of multiple overlapping views is harnessed for robust multi-view crack detection. A transformer approach, named MVCrackViT, is designed to use attention over multiple views, enabling point cloud crack segmentation from the views directly. To address quality issues such as motion blur, defocus, and low exposure commonly found in real-world data, artificial view corruption is applied to accomplish training from image data alone. With reasonable positional tolerance, a performance of approximately 90% clCloudIoU is achieved on a 3D crack dataset, the first of its kind. The powerful clCloudIoU metric is introduced to evaluate crack detection in 3D space.

关键词： 3D detection Crack detection Point cloud segmentation vision transformer

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Development and Optimization of a Small-Sized Biomimetic Frog robot for Rescue 9

Development and Optimization of a Small-Sized Biomimetic Fro...

引用

9th international conference on Intelligent Computing and signal processing, ICSP 2024

作者： Zhang, Xiwen Zhu, Jiale Chen, Yang Gan, Yu School of Mechanical Engineering and Mechanics Xiangtan University Hunan 411105 China

ISBN: (纸本)9798350376548

Small jumping robots have great potential for application in the fields of rescue, detection and monitoring due to their small size, large load and high flexibility. In this paper, from the perspective of bionics, the frog leg with excellent jumping ability is selected as the research object, and the design scheme of the planar linkage mechanism of the small bionic frog leg rescue robot is proposed. On this basis, through the finite element analysis of the structure of the key components, it is concluded that the materials used can ensure the reliability of the rear leg movement, and the jumping process of the small-sized biomimetic frog rescue robot is simulated dynamically as well as experimentally compared, and it is concluded that the robot jumping ability has stability, which verifies the correctness and feasibility of the scheme. © 2024 IEEE.

关键词： Microrobots

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Method for robot recognition and localization based on third-person perspective 4

Method for robot recognition and localization based on third...

引用

4th international conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Luo, Zongheng Lu, Jun Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

To achieve the recognition and positioning functions of indoor mobile robots under limited computing power conditions, a method based on color recognition for robot recognition and positioning is proposed. The global image of the robot working in the field is collected by a camera outside the field, and the position of the robot is obtained through computer vision processing. Then, the robot is controlled to move according to this information. Experiments conducted within a 2m × 1m area have shown that the maximum error during robot operation is 7.8cm/m, with an average error of 7.0cm/m. The maximum error of the steering angle is 16.6°, with an average error of 7.7°. © 2024 SPIE.

关键词： robot applications

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IMPROVING AUTOMATIC TARGET RECOGNITION WITH INFRARED IMAGERY USING vision TRANSFORMERS AND FOCUSED DATA AUGMENTATION 31

IMPROVING AUTOMATIC TARGET RECOGNITION WITH INFRARED IMAGERY...

引用

2024 international conference on Image processing

作者： Baili, Nada Frigui, Hichem Univ Louisville Comp Sci & Engn Dept 220 Eastern Pkwy Louisville KY 40292 USA

ISBN: (纸本)9798350349405;9798350349399

We propose a data augmentation strategy to improve Automatic Target Recognition (ATR) from Infrared (IR) imagery using vision transformers. Our method leverages external IR image repositories to select relevant samples that can boost the diversity of the training data. By doing so, we improve the model ability to learn from the challenging regions of the training feature space. Our approach uses attention-based explanations to identify under-represented regions in the feature space of the training data. We leverage this information to search for new samples that complement the current training data by covering the sparse gaps in the feature space. We evaluate the proposed approach on a public dataset with IR imagery of multiple targets. We show that our method achieves a significant 2% improvement in ATR performance compared to a baseline model trained without augmentation. We also show that our method outperforms other data augmentation techniques that do not consider under-represented regions. These results demonstrate the effectiveness of our approach in an infrared scenario, where there is high intraclass variance and large training sets are expensive to obtain.

关键词： Automatic Target Recognition Infrared Images Data Augmentation vision Transformer

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