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Research on Weakly Supervised Target Localization in Sonar Imagery Based on GradCAM 4

Research on Weakly Supervised Target Localization in Sonar I...

引用

4th International conference on computer vision and Pattern Analysis, ICCPA 2024

作者： Wei, Guangze Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Xiao Xiong, Minglei College of Artificial Intelligence Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China College of Engineering Peking University Beijing China Robot Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510682528

In the unique marine environment, sonar, as a crucial tool for navigation and ranging using sound waves, plays a key role in the recognition and localization of targets in sonar imagery, which is critical to the performance of underwater equipment. In practical applications, fully supervised object detection methods are commonly used to accurately determine the categories and locations of targets in sonar images. However, these methods require laborious individual annotation of target positions and categories. Addressing this challenge, this paper proposes a weakly supervised target localization method for sonar imagery based on Grad-CAM technology. Initially, the method employs the Balanced Ensemble Transfer Learning (BETL) algorithm for target classification in sonar images, utilizing deep transfer learning. Subsequently, the GradCAM technique is applied to the task of sonar image target classification, using the generated heat maps for target localization. this aims to enhance the accuracy and efficiency of sonar image recognition and localization while reducing the burden of annotation work. © 2024 SPIE.

关键词： Image enhancement

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Fast Point Cloud to Mesh Reconstruction for Deformable Object Tracking 4th

Fast Point Cloud to Mesh Reconstruction for Deformable Objec...

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4th International conference on robotics, computer vision and Intelligent Systems (ROBOVIS)

作者： Mansour, Elham Amin Zheng, Hehui Katzschmann, Robert K. Swiss Fed Inst Technol Soft Robot Lab Zurich Switzerland ETH AI Ctr Zurich Switzerland

ISBN: (纸本)9783031590566;9783031590573

the world around us is full of soft objects we perceive and deform with dexterous hand movements. For a robotic hand to control soft objects, it has to acquire online state feedback of the deforming object. While RGB-D cameras can collect occluded point clouds at a rate of 30Hz, this does not represent a continuously trackable object surface. Hence, in this work, we developed a method that takes as input a template mesh which is the mesh of an object in its non-deformed state and a deformed point cloud of the same object, and then shapes the template mesh such that it matches the deformed point cloud. the reconstruction of meshes from point clouds has long been studied in the field of computer graphics under 3D reconstruction and 4D reconstruction, however, both lack the speed and generalizability needed for robotics applications. Our model is designed using a point cloud auto-encoder and a Real-NVP architecture. Our trained model can perform mesh reconstruction and tracking at a rate of 58 Hz on a template mesh of 3000 vertices and a deformed point cloud of 5000 points and is generalizable to the deformations of six different object categories which are assumed to be made of soft material in our experiments (scissors, hammer, foam brick, cleanser bottle, orange, and dice). the object meshes are taken from the YCB benchmark dataset. An instance of a downstream application can be the control algorithm for a robotic hand that requires online feedback from the state of the manipulated object which would allow online grasp adaptation in a closed-loop manner. Furthermore, the tracking capacity of our method can help in the system identification of deforming objects in a marker-free approach. In future work, we will extend our trained model to generalize beyond six object categories and additionally to real-world deforming point clouds.

关键词： Deformation Manipulation Reconstruction Tracking

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Deep Learning-Based Multimodal Sensing Framework for Anti-spoofing Systems 4th

Deep Learning-Based Multimodal Sensing Framework for Anti-s...

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4th International conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Velesaca, Henry O. Vulgarin, Jorge Vintimilla, Boris X. Antens, Coen Perez, Alberto Rubio Escuela Superior Politécnica del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación CIDIS Campus Gustavo Galindo Guayaquil09-01-5863 Ecuador Software Engineering Department University of Granada Granada18014 Spain Computer Vision Center Universidad Autónoma de Barcelona Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789819769940

this paper presents a multimodal facial anti-spoofing framework based on a Deep Learning approach using a stereo vision and voice sensing system. this framework is part of a project for an intelligent receptionist robot and its function is to authenticate people by applying a face recognition redundancy process that prevents the identity theft of a person. the proposal consists of a smart screen that has a multimodal sensing system including cameras and audio. the redundancy approach pipeline for face recognition contains a face and voice recognition module and a 3D face verification module to determine whether the person is real. the system initially acquires and processes a stream of images and audio in real-time, using deep neural networks it detects and recognizes the face and voice of a person, then aligns the images from two cameras and obtains the 3D point cloud of the detected human face in the scene, and finally uses this 3D information to verify whether the recognized person is real. the proposed solution is evaluated with different people to validate the effectiveness of the system. the dataset will be available for the research community. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Research on Obstacle Avoidance and Image Registration Technology Based on Binocular vision 4

Research on Obstacle Avoidance and Image Registration Techno...

引用

4th International conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Chen, Zihong Zhang, Xing Anhui Sanlian University Hefei230601 China

ISBN: (纸本)9798350308082

To enable robots to navigate and avoid obstacles in uncertain environments, a 3D coordinate system was constructed based on visual information decision-making and camera calibration results. A region-based stereo matching algorithm was used to extract depth information from the binocular stereo images to improve the reliability of the disparity map and to enable the robot to achieve local obstacle avoidance. the algorithm works by first selecting a point that does not belong to any growing region, then using an energy function to determine its disparity and setting it as the root disparity, and calculating the error of adjacent points based on the root disparity to determine the region correlation. this process is repeated until all points in the image have been processed. Building a 3D coordinate system based on visual information decision-making and camera calibration results enables the robot to autonomously navigate and avoid obstacles. It allows the robot to accurately perceive its location and direction in uncertain environments, and adjust its trajectory in real-time based on environmental changes. Simulation analysis shows that using binocular recognition and Bug2 algorithm can improve the reliability of the disparity map and enable the robot to achieve local obstacle avoidance. © 2023 IEEE.

关键词： Decision making

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Human Activity Recognition using Eye Movements: A review 4

Human Activity Recognition using Eye Movements: A review

引用

4th International conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Modi, Nandini Pandit Deendayal Energy University Computer Science Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350369786

Human activity recognition represents a prominent and formidable challenge within the domain of computer vision. this problem holds substantial relevance across various domains, encompassing robotics, gaming, therapeutic interventions, human-robot interaction, athletic performance analysis, healthcare monitoring, and video surveillance. Due to the complicated human pose variations and group interactions, human activity recognition is difficult. Some of these inherent difficulties could be solved using sensors with several modalities. An RGB-D camera, thermal sensors, infrared sensors etc. might all be examples of such sensors. Due to intrusive nature of these sensing modalities it may cause user inconvenience which limits its utility to a broader audience. To overcome this, in this work, we explore eye movement analysis for sensing activity recognition. Eye movement analysis has become a promising method for identifying human activities across a variety of fields. this paper provides a review of techniques and applications for eye movement analysis in human activity recognition. the study examines the possibility of detecting human activity using a low-cost, camera based eye gaze tracking device and opens up discussion on the wider applicability of eye movements based activity recognition that are difficult, or even impossible, to detect using common sensing modalities. © 2023 IEEE.

关键词： Eye movements

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Determination of Protein Content of Wheat Using Partial Least Squares Regression Based on Near-Infrared Spectroscopy Preprocessing 4

Determination of Protein Content of Wheat Using Partial Leas...

引用

4th International conference on robotics and computer vision, ICRCV 2022

作者： Liu, Ruirui Liu, Jun Liu, Chuang Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

When using near-infrared spectral modeling to determine the protein content of wheat, spectral instrument and background noise will reduce the prediction accuracy of the model. In order to make the prediction results not affected by the data itself, this paper study the effect of the data preprocessing method on modeling. the experiment shows that the best results can be obtained using the KS-MC-PLSR algorithm on this dataset, the R2 and RMSE of the experimental test set are 0.9902 and 0.1685, respectively. © 2022 IEEE.

关键词： Proteins

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4th International conference on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

4th International Conference on Recent Trends in Advanced Co...

引用

4th International conference on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

ISBN: (纸本)9789811971686

the proceedings contain 30 papers. the special focus in this conference is on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals. the topics include: Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis;Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition;high Performance Classifier for Brain Tumor Detection Using Capsule Neural Network;Mining Suitable Symptoms to Identify Disease Using Apriori and NBC;background Features-Based Novel Visual Ego-Motion Estimation;livspecs: Design and Implementation of Smart Specs for Hearing and Visually Challenged Persons;Self-balancing robot Using Arduino and PID Controller;a Survey Based on Online Voting System Using Blockchain Technology;convergence Perceptual Model for Computing Time Series Data on Fog Environment;survey on Collaborative Filtering Technique for Recommender System Using Deep Learning;a Survey on Power Consumption Indicator Using Machine Learning-Based Approach;a Novel Hand Gesture Recognition for Aphonic People Using Convolutional Neural Network;comprehensive Analysis of Defect Detection through Image Processing and Machine Learning for Photovoltaic Panels;covid Analysis Prediction Using Densenet Method in Deep Learning;Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset;memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks;Enhancing Protection Against Scalper Bots with ML;An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN;implementation of Machine Learning Methods on Data to Analyze Emotional Health;SMS Spam Classification Using PSO-C4.5.

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Minimize Collision Risk Global Planner Based Voronoi Graph for Mobile robot 4

Minimize Collision Risk Global Planner Based Voronoi Graph f...

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4th International conference on robotics and computer vision, ICRCV 2022

作者： Zhihui, Liu Wei, Wang Zhen, Huang Jinpeng, Xu Guangdong University of Technology School of Electrical and Mechanical Engineering Guangzhou China FAW-Volkswagen Automobile Co. Foshan Branch of FAW-Volkswagen Automobile Co. Changchun China Jihua Laboratory Engineering Research Center for Intelligent Robotics Foshan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Ensure safe, reliable, fast and stable robot navigation which requires stable and efficient motion planning algorithm support. In order to reduce the collision risk during robot navigation, we propose minimize collision risk global planner for mobile robot, based on voronoi graph to extract Tyson polygon vertices. Using multiple vertices to form a planning search graph, the collision risk of each edge is calculated i.e. the integral of the distance to the obstacle is used as the weighted value of the edge, and the search is performed using A∗ to obtain the planning path with the lowest collision risk globally at the current moment. In order to make the planned path more consistent with robot kinematics, we designed the collision and smooth cost function to optimize the path using gradient descent, and the searched path is passed to the local planner after path smooth. the test results on the actual robot show that the robot can effectively avoid narrow areas and effectively reduce the collision rate and replanning times in dynamic environments. © 2022 IEEE.

关键词： Mobile robots

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HTD: A Fast Human-centered Text-locating Method for Auxiliary Reading 4

HTD: A Fast Human-centered Text-locating Method for Auxiliar...

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4th International conference on robotics and computer vision, ICRCV 2022

作者： Yu, Zhenghong Wang, Hao Yang, Hang Zhou, Huabing Guangdong Polytechnic of Science and Technology School of Robotics Zhuhai China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology College of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

As computer vision and human-computer interaction technologies are getting mature, vision-based auxiliary text reading has become the mainstream method to optimize learning and reading experience. To overcome the drawbacks of the common multi-step detection way and further improve the text reading experience, we propose a new human-centered fast text-locating method, named HTD. It transforms the relatively time-consuming serial procedure to a hybrid object detection problem, utilizing the hand-text hybrid object detection model to instantly locate the text of interest to readers. Considering the particularity of this task, we choose to perform the weighted NMS to infer final predicted bounding box. To verify the effectiveness of the proposed method, we tested the performance on the homemade hand-text hybrid object detection dataset HTDD. the experiment indicated that our proposed method is more efficiency and can produce satisfying results, which could meet the needs of human-computer interactive auxiliary reading in text reading scenarios. © 2022 IEEE.

关键词： Object detection

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A Fine three-dimensional Adaptive Color Segmentation Algorithm for Real-time vision System of Soccer robot 4

A Fine Three-dimensional Adaptive Color Segmentation Algorit...

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4th International conference on Communications, Information System and computer Engineering, CISCE 2022

作者： Sun, Yan Dang, Lingwei School of Computer Engineering & Science Shanghai University Shanghai China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

the aim of the vision system for small soccer robots is to recognize and track robots and soccer ball in a semi-open environment. Traditional color segmentation methods are usually sensitive to external conditions, for example light changes or uneven distribution. Machine learning-based methods usually require advanced hardware as well as huge amount of data. To overcome these limitations, we propose a fine three-dimensional adaptive color classification method in this paper. the presented method determines the thresholds in color space dynamically. For distinguishing the overlapping area in color space, Naive Bayes classifier is used to classify the color categories which obey the Multivariate Normal Distribution. Dynamic adaptive lighting is introduced to enhance the performance, and the robustness of classification is increased by combining multiple brightness separable color space. this method is embedded in a vision system for Soccer robot to enhance the processing performance. the experiments show that the system is robust and achieves high precision. It can process the soccer video in real-time with detection rate of 99.68 % and precision of 99.92 %. © 2022 IEEE.

关键词： Color

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