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RepF-Net: Distortion-Aware Re-projection Fusion Network for Object Detection in Panorama image 16th

RepF-Net: Distortion-Aware Re-projection Fusion Network for ...

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16th Asian conference on Computer Vision (ACCV)

作者： Li, Mengfan Meng, Ming Zhou, Zhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Commun Univ China Sch Data Sci & Media Intelligence Beijing Peoples R China

ISBN: (纸本)9783031263125;9783031263132

Panorama image has a large 360 degrees field of view, providing rich contextual information for object detection, widely used in virtual reality, augmented reality, scene understanding, etc. However, existing methods for object detection on panorama image still have some problems. When 360 degrees content is converted to the projection plane, the geometric distortion brought by the projection model makes the neural network can not extract features efficiently, the objects at the boundary of the projection image are also incomplete. To solve these problems, in this paper, we propose a novel two-stage detection network, RepF-Net, comprehensively utilizing multiple distortion-aware convolution modules to deal with geometric distortion while performing effective features extraction, and using the non-maximum fusion algorithm to fuse the content of the detected object in the post-processing stage. Our proposed unified distortion-aware convolution modules can be used to deal with distortions from geometric transforms and projection models, and be used to solve the geometric distortion caused by equirectangular projection and stereographic projection in our network. Our proposed non-maximum fusion algorithm fuses the content of detected objects to deal with incomplete object content separated by the projection boundary. Experimental results show that our RepF-Net outperforms previous state-of-the-art methods by 6% on mAP. Based on RepF-Net, we present an implementation of 3D object detection and scene layout reconstruction application.

关键词： Object detection

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

2nd International Conference on Advances in Data-driven Comp...

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

ISBN: (纸本)9789819995172

The proceedings contain 40 papers. The special focus in this conference is on Advances in data-driven Computing and Intelligent Systems. The topics include: The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification;does the Resilience Learning Game Foster Workforce Open Innovation and Sustainability Attributes? Empirical Evidence from Greek Food Industry;Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks;Parts-of-Speech Tagger in Assamese Using LSTM and Bi-LSTM;Dynamic Retransmission Count Prediction (DRCP) Algorithm for FANET Using Machine Learning Techniques;video-Based Action Recognition of Spatial and Temporal Deep Learning Models;aeroDetect: Real-Time Small Object Detection in Aerial images;integrated Whale Swarm and Neuro-Evolutionary Computing for Large-Scale Sparse Optimization Problems;deep Reinforcement Learning for Credit Card Fraud Detection;influences of Specimen and Fiber Sizes on the Direct Tensile Resistance of Ultra-High-Performance Fiber-Reinforced Concretes;segmentation-Based Transformer Network for Automated Skin Disease Detection;multi-sensor data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System;anti-forensic Analysis for image Splicing Detection Through Advanced Filters;classification of Organic and Recyclable Waste Using a Deep Learning Approach;prediction of Soil Properties for Agriculture Using Ensemble Learning Techniques;enhancing Intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition;detection of Explicit Lyrics in Hindi Music Using Different Machine Learning Algorithms;artificial Intelligence Empowered Language Models: A Review;mass Production Lab;Automatic Multiple Sounds Detection with Recurrent Neural Networks (LSTM);energy Efficiency Techniques in 5G/6G Networks: Green Communication Solutions;assessing the Feasibility and Scalability of Using Spark for Identifying Tip Burn Diseases i

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

2nd International Conference on Advances in Data-driven Comp...

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

ISBN: (纸本)9789819995233

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Articulatory data of Audiovisual Records of Speech Connected by Machine Learning 2

Articulatory Data of Audiovisual Records of Speech Connected...

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2nd IEEE conference on Information Technology and data Science, CITDS 2022

作者： Trencsenyi, Reka Czap, Laszlo University of Debrecen Department of Electrical and Electronic Engineering Debrecen Hungary Institute of Automation and Infocommunication University of Miskolc Miskolc Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

The center of attraction of the present study is the application of neural networks for combining data arising from dynamic audiovisual sources made by ultrasound and magnetic resonance imaging methods, which store image and sound signals recorded during human speech. The objectives of machine learning are tongue contours fitted to the frames of the audiovisual packages by automatic contour tracking algorithms. © 2022 IEEE.

关键词： Magnetic resonance imaging

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AI Models for Ultrasound image Similarity Search: A Performance Evaluation

AI Models for Ultrasound Image Similarity Search: A Performa...

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18th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Takacs, Petra Zsamboki, Richard Kiss, Elod Dhombres, Ferdinand GE HealthCare Hungary Budapest Hungary Sorbonne University INSERM Limics GRC26 Armand Trousseau Hospital APHP Paris France

Querying similar images from a database to a reference image is an important task with multiple possible use- cases in healthcare industry, including improving labelling processes, and enhancing diagnostic support to medical professionals. The aim of this work is to measure the performance of different artificial neural networks, comparing their ability to identify clinically relevant similar images based on their generated feature sets. To measure the clinical relevance, metrics using expert labels of organs and diagnoses on the images were calculated, and image similarity was further confirmed by pixel metrics. images with organ and diagnosis labels were selected from a dataset of early-stage pregnancy and 2nd -3rd trimester pregnancy ultrasound images respectively for the measurements. The networks were chosen from state-of-the-art foundational models trained on natural images, DINO and DINOv2, SAM2, and DreamSim. The best performing model based on our experiments is DreamSim for organ matches, and DINO for diagnosis matches. A simple ResNet trained on the mentioned early pregnancy dataset for organ classification was also added to the selection. ResNet performs best for early pregnancy organ matches, therefore finetuning a robust encoder on our own dataset is a promising future step to further enhance medically relevant similar image search. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Obstetrics

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PATH PLANNING SIMULATION OF A DIFFERENTIAL WHEEL ROBOT BASED ON THE RRT ALGORITHM 2

PATH PLANNING SIMULATION OF A DIFFERENTIAL WHEEL ROBOT BASED...

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2nd International conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Yu, Mingyan Department of Mechanical Engineering University of Alberta EdmontonT6G2R3 Canada

ISBN: (纸本)9781837242672

This research aims to overcome the constraints of current image-processing techniques and path-planning algorithms that hinder traditional navigation systems from reliably recognizing obstacles and designing effective paths. The study examines the progress of sophisticated path planning and tracking algorithms for differential wheel robots that operate in interior situations. The study utilizes MATLAB-based image processing techniques to turn real-world photos into binary maps, enabling efficient obstacle identification. The rapid-exploring random tree (RRT) technique is used to plan paths by exploring random nodes and avoiding barriers to find the most efficient pathways. Moreover, the implementation of the pursuitprediction (PP) tracking algorithm enhances the robot's capacity to accurately follow the intended path by dynamically modifying its trajectory using real-time data. The results indicate that the suggested methods greatly enhance the precision of path planning and navigation, allowing the robot to effectively navigate through intricate surroundings containing diverse impediments, such as tables and chairs. This work provides useful insights into autonomous navigation systems, specifically for differential wheel robots, expanding their potential uses in indoor environments such as restaurants and improving their overall operational efficiency and dependability. © The Institution of Engineering & Technology 2024.

关键词： MATLAB

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Research on Coastline Detection Method of SAR image Based on Seed Point Growth 2

Research on Coastline Detection Method of SAR Image Based on...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Yue, Guanyu Wei, Xueyun Liu, Xudong Jiangsu University of Science and Technology Ocean College Zhenjiang Jiangsu212000 China State Grid Jurong County Electric Power Supply Company Jurong212400 China

ISBN: (纸本)9798350323818

In recent years, more and more SAR image technology has been used in the field of sea area management. Among them, the extraction of coastline is one of the hot research issues. During the propagation of electromagnetic waves, due to the inhomogeneity, discontinuity, anisotropy and loss of the medium. Satellite remote sensing technology has the following advantages. First of all, compared with traditional manual operations, satellite remote sensing has the ability of fast collection and wide search range, and can simultaneously observe multiple areas in a relatively short period of time, effectively grasping the instantaneous status of ground or space targets. Although not affected by light intensity, SAR images suffer from inherent speckle noise. Different land geological structures and ground features have complex scattering characteristics. On the ocean, sea wind, whirlpools, tides, etc. will also change the reflection of radar waves. Therefore, it is necessary to use tools such as computers to accelerate the processing of data through automatic identification technology and target feature extraction technology. SAR image processing is such a technology that meets the requirements of large-scale data processing. On land, the radio wave scattering characteristics of different regions will be different, resulting in rich gray levels of land targets. Because the sea area obtained by the algorithm based on seed point growth is connected, it can effectively avoid the interference of noise spots in SAR image, so as to make the detection more stable and robust. On the ocean, due to the factors such as internal wave, vortex and ocean current caused by sea wind, the sea surface presents varying degrees of roughness, resulting in large differences in reflection coefficients. In this way, if a simple method is used to detect the coastline, it is bound to cause great difficulties. Aiming at the idea of seed point growth, this paper presents a fast coastline detection algorit

关键词： Synthetic aperture radar

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image Encryption Using Quadrant Level Permutation and Chaotic Double Diffusion 2nd

Image Encryption Using Quadrant Level Permutation and Chaoti...

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2nd International conference on Advanced Engineering, Technology and Applications, ICAETA 2023

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India City University Petaling Jaya Malaysia Laboratoire de Vison et d’intelligence Artificielle Université Larbi Tebessi Tébessa Algeria

ISBN: (纸本)9783031509193

Information security is becoming more and more important as information technology advances. In the fields of telecommunication networks, diagnostic imaging, and multimedia applications, information security is crucial. The use of images as a medium for the conveyance of information is integral to the information age. However, image data differs from traditional text-based information in that it is dense and has strong pixel correlation. As a consequence, image encryption no longer works with traditional encryption techniques. Consequently, a safe and difficult-to-crack image encryption technique is required. In this research, a new symmetric image encryption technique based on improved Henon chaotic system with byte sequences and a unique method of shuffling a picture’s pixels is suggested. This algorithm produces effective and efficient encryption of images. The suggested image encryption technique created a new dimension for safe image transfer in the realm of digital transmission by causing more confusion and diffusion, according to statistical analysis and experimental key sensitivity analysis. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chaotic image Encryption diffusion using henon map image Encryption two level permutation

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VGG-based discrimination for pneumonia images enhanced by Generative Adversarial Network 2

VGG-based discrimination for pneumonia images enhanced by Ge...

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2nd International conference on Computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Zeng, Xiyin The Department of Aberdeen Data Science and Artificial Intelligence South China Normal University Guangdong Foshan528200 China

Due to problems such as blurring of chest X-ray image and double shadow of heart and lung, the accuracy of judging whether there is COVID-19 is not high. This study intends to use Generative Adversarial Network (GAN) to segment images, and then put the segmented image set into the classification algorithm to determine whether it is infected by the pneumonia. The data set is divided into "normal"and "pneumonia". In order to remove the interference of rib and heart on lung contour in X-ray image, GAN technology is used to segment the original data set. The segmented image only retains the most important lung silhouette, which is convenient for the next step of recognition and information extraction. The classification algorithm uses Visual Geometry Group (VGG)-16. During the training, only fully connected layer 1 and fully connected layer 2 layers were trained, and the rest layers were frozen. When the model loads the original weight, the top level of the model will be replaced by the head model. The head model is placed on the basic model and becomes part of the actual training model to determine the best weight. After the original X-ray image is processed by GAN, the accuracy of the segmented lung image is improved from 84% to 98%. Loss decreased from 17% to 3%, and the accuracy of classification results was greatly improved. Therefore, it is proved that the segmented image can effectively remove irrelevant parts in the recognition process, and the accuracy is improved due to the removal of noise interference and clearer lung contour. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Heterogeneous Few-shot Learning with Knowledge Distillation for Hyperspectral image Classification 2

Heterogeneous Few-shot Learning with Knowledge Distillation ...

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2nd International conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Hu, Yanfang Huang, Yalan Wei, Guoqing Zhu, Kai School of Information Engineering Jiangxi University of Science and Technology Ganzhou China School of Physics and Electronics Gannan Normal University Ganzhou China

ISBN: (纸本)9781665408868

Hyperspectral image (HSI) classification is one of the most popular applications in remote sensing. In practice, due to the high cost of manual labeling, only a few hyperspectral image samples with labels can be obtained. A small number of labeled training samples tend to overfit the deep network method, resulting in a sharp decline in classification accuracy. In order to solve this problem, this paper proposes a classification method for hyperspectral images based on knowledge distillation and heterogeneous few-shot learning. Firstly, the model pretrain the feature extraction network on miniimageNet, a small sample natural image dataset with abundant labeled images, and introduces knowledge distillation to improve the feature expression capability of shallow network in small sample. Then, effective knowledge transfer is carried out between two heterogeneous data sets, and the weights obtained from the model on the natural data set are transferred to the backbone network of hyperspectral image classification to improve the accuracy of HSI classification. Finally, the classifier is fine-tuned on HSI using the paradigm of small sample learning to extract discriminative hyperspectral image features and further enhance the model's detail expression. Experimental results on two hyperspectral image classification datasets show that the proposed method can effectively improve the accuracy of small sample hyperspectral image classification. © 2022 IEEE.

关键词： Distillation

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