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Hearing Anything Anywhere

Hearing Anything Anywhere

引用

IEEE/CVF conference on Computer vision and Pattern Recognition (CVPR)

作者： Wang, Mason Long Sawata, Ryosuke Clark, Samuel Gao, Ruohan Wu, Shangzhe Wu, Jiajun Stanford Univ Stanford CA 94305 USA Sony AI Tokyo Japan Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However, alongside immersive visual experiences, immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper, we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene, a setup that is easily achievable by ordinary users. To this end, we introduce DIFFRIR, a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene, including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method, we collect a dataset of RIR recordings and music in four diverse, real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations, and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.

关键词： Audio Binauralization Differentiable Audio Rendering image-Source Inverse Rendering machine Learning Physics-Based Models Room Acoustics Room Impulse Response Signal processing Sound Sound Dataset Soundfield Interpolation Spatial Audio

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Virtual Umpiring and Ball Tracking System by image processing using Resnet-18 15

Virtual Umpiring and Ball Tracking System by Image Processin...

引用

15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hemanathan, D. Vanitha, R. Vishwa, R. Shalini, S. Department of CSE IFET College of engineering Villupuram India

ISBN: (纸本)9798331300579

Cricket, a highly captivating sport, particularly popular in South Asian countries, heavily relies on umpires to make critical decisions in accordance with the cricket *** the fallibility of human judgment, errors occasionally occur, leading to time-consuming reviews by the third umpire to ensure *** address this challenge, We introduce an AI-based umpiring system aimed at refining decision-making precision and fairness in cricket *** the integration of computer vision and machine learning, our system scrutinizes live video feeds to provide umpires with instantaneous decision-making *** events like ball trajectory and player movements are identified by computer vision algorithms, offering umpires immediate *** system's capabilities evolve through continuous machine learning, leveraging historical match data to enhance accuracy progressively.A key feature includes instant replay provisions, empowering teams to contest decisions and fostering *** study underscores the transformative role of AI in sports officiating, presenting a blueprint for its adoption across various sporting domains. © Grenze Scientific Society, 2024.

关键词： Decision making

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A novel method of UAV autonomous precise landing by machine vision

A novel method of UAV autonomous precise landing by machine ...

引用

2023 International conference on Optics and machine vision, ICOMV 2023

作者： Su, Junlong Ling, Fei Zhou, Miaolin Chen, Xiaojian Yang, Yongwen Wuhan University Wuhan430072 China Guangdong Digital Ecological Technology Co. Ltd. Shaoguan512026 China

ISBN: (纸本)9781510664777

GPS and machine vision are commonly used in traditional UAV landings. Due to the influence of meteorological environment and positioning deviation, as well as the limitation of the detection accuracy and efficiency of visual landing signs, the UAV cannot accurately land on the target position. Aiming at this problem, a novel method of UAV autonomous precise landing by machine vision was proposed. First, a "Hui" shaped UAV landing sign was designed. Then, image processing algorithm was used to identify and locate the landing sign of UAV. The relative position was calculated between the UAV and the center point of the landing sign to control the landing of UAV by PID algorithm. Finally, an autonomous landing test of UAV was conducted. The test results showed that the method could achieve the accurate landing of UAV at 40-50 meters. The average detection time of the algorithm was 3.58 ms, and the average accuracy was 102 mm. © 2023 SPIE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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AI in Ophthalmic Imaging: Enhancing Retina Analysis for Early Disease Detection

AI in Ophthalmic Imaging: Enhancing Retina Analysis for Earl...

引用

2024 IEEE International conference on Computer vision and machine Intelligence, CVMI 2024

作者： Chandrakantha, T.S. Jagadale, Basavaraj N Madhuri, G.R. Abhishek, T.E. Kuvempu University Research in Electronics Department of Post-Graduate Studies Karnataka Shankaraghatta India

ISBN: (纸本)9798350376876

In this review, we analyze the AI-based methods for retina segmentation and classification in ophthalmology/medical imaging with depth. This study comprises the whole research pipeline, including data retrieval and preprocessing, feature extraction classification as well as segmentation. It explores a mixture of machine and deep learning algorithms for these tasks, monitoring their performance using relevant metrics. Illustrating the stakes using early-onset disease detection in retinal image analysis, this review describes manual annotation limitation and provides outlines for AI solutions. It provides information on datasets used in previous works, describing their features and preprocessing procedures. This review details feature extraction from traditional image processing to deep learning, focusing on characteristic features for differentiating retinal abnormalities. The classification part describes several models used to classify the retinal images and its evaluation in terms of Accuracy, Precision, Recall and F1-score. The proposed work also shows approaches to segment the region of interest in retina images and scoring mechanism such as Dice Coefficient, Intersection Over Union for evaluating segmentation. The article closes by reviewing summary points, stating limitations and directions for future research. To sum up, this all-in-one article is beneficial for academia and industry professionals not only to reveal the state-of-the-art techniques but also provide a guideline toward future research in AI-based solutions with improved diagnosis & treatment of retinal diseases. © 2024 IEEE.

关键词： image segmentation

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Terrain image Classification Based on vision Transformer Deep Learning Algorithm 7

Terrain Image Classification Based on Vision Transformer Dee...

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7th International conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Ren, Shiqi Zhang, Yu College of information engineering College of artificial intelligence Yangzhou university Jiangsu Yangzhou225000 China College of Computing Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798331529314

Terrain image classification is an important research direction in the field of remote sensing and computer vision, aiming to realize automatic recognition and classification of different landform features through the analysis and processing of terrain images. In this paper, a deep learning algorithm based on vision Transformer (ViT) is used to classify terrain images, and the performance of the algorithm in this task is systematically evaluated. In the process of model construction, we first imported the vision Transformer model and made corresponding parameter Settings to ensure its adaptability and effectiveness. After training, it is observed that the loss function of the training set decreases from the initial value of 2.84 to 0.35, a decrease of 2.49, indicating that the model tends to converge in continuous optimization. At the same time, the accuracy is also significantly improved, from 55.9% to 86.8%, an increase of 30.9%, showing the enhancement of the model's learning ability. For the validation set, loss also decreased from 0.78 to 0.47, a decrease of 0.31, while accuracy increased from 60.1% to 83.5%, an increase of 23.4%. These results further prove the good performance of the model on different data sets and its convergence trend. In addition, through the evaluation of the test set, we get more specific performance indicators: The accuracy of the terrain image classification model based on vision Transformer on the test set reaches 89.9%, the Precision is 0.9615, the Recall is 0.9494, and the F1-score is 0.9554. These indicators show that the model not only has high classification accuracy, but also performs well in generalization ability. To sum up, this research demonstrates the effectiveness of vision Transformer deep learning algorithm in terrain image classification, and provides a new solution idea and method for related fields. Through continuous optimization and adjustment, the algorithm is expected to achieve more extensive promotion in pract

关键词： machine vision

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A Systematic Review: How Computer vision is Transforming Agriculture in Economic Growth 1

引用

3rd International conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (数字)9783031686177

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation machine Learning

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Application Research of Operation and Maintenance Scene Fusion and Intelligent Knowledge Assistant 5

Application Research of Operation and Maintenance Scene Fusi...

引用

5th International conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Wang, Feifeng Lin, Zhengping Wang, Xin Li, Dajian Meng, Chunzhi Qin, Jian Guangxi Power Grid Co. Ltd. Electric Power Science Research Institute Nanning China Electric Power Research Institute of China Southern Power Grid Company Guangzhou China Guangxi Power Grid Co. Ltd. Nanning China Digital Intelligence Operation Center of Guangxi Power Grid Co. Ltd. Nanning China

ISBN: (纸本)9798331527662

In order to improve the intelligent level of power measuring equipment operation and maintenance, this paper introduces intelligent knowledge assistance technology into the reconstruction of power measuring equipment maintenance and operation scenario. The scene view of power measuring equipment operation is fused by vector library. A 3D vision imaging model based on machine vision was established. The feature of operation and maintenance scene is extracted by using fuzzy degree detection and visual reconstruction, and three-dimensional reconstruction is carried out by intelligent knowledge assistant technology. This paper uses pixel-based image fusion and virtual reality rendering techniques to enhance visual information in the operating environment. Multiple view images are encoded with the corresponding camera parameters and attitude information. According to the power metering device operation and maintenance scenario, the spatial distribution set is reorganized to achieve 3D VR vision reconstruction optimization. Simulation results show that the algorithm proposed in this paper can reproduce the running state of electric energy measuring equipment quickly and accurately. © 2024 IEEE.

关键词： machine vision

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

引用

5th International conference on Data Science, machine Learning and Applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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City-scale Ground Segmentation of Aerial LiDAR by image-analogous Gradients 15

City-scale Ground Segmentation of Aerial LiDAR by Image-anal...

引用

15th International conference on machine vision (ICMV)

作者： Azam, Muhammad Jacoby, Derek Coady, Yvonne Univ Victoria Victoria BC Canada

ISBN: (纸本)9781510666184;9781510666191

LiDAR Point Cloud segmentation is a key input to downstream tasks such as object recognition and classification, obstacle avoidance, and even 3D reconstruction. Akey challenge in the segmentation of large city-scale datasets is uneven distribution of points to specific classes and significant class imbalances. As highly detailed point cloud datasets of urban environments become available, neural networks have shown significant performance in recognizing large well-defined objects. However, data is fed into these networks in chunks and the scheme by which data is presented for training and evaluation can have a significant impact on performance. In this work, we establish a method analogous to gradients in image processing to segment the ground in point clouds, achieving an accuracy of 91.4% on the Sensaturban dataset. By isolating the ground, we reduce the quantity of classes that need to be segmented from structures in urban LiDAR and improve data partitioning schemes when combined with random/grid down-sampling techniques for neural network inputs.

关键词： LiDAR segmentation urban point cloud neural networks

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PPIR-Net: An Underwater image Restoration Framework Using Physical Priors 29th

PPIR-Net: An Underwater Image Restoration Framework Using Ph...

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29th International conference on Neural Information processing

作者： Zhang, Changhua Yang, Xing Chen, Zhongshu Luo, Shimin Luo, Maolin Yan, Dapeng Li, Hao Wang, Bingyan Univ Elect Sci & Technol China Chengdu Peoples R China Nucl Power Inst China Chengdu Peoples R China

ISBN: (纸本)9789819916443;9789819916450

In recent years, underwater image processing has been a hot topic in machine vision, especially for underwater robots. A key part of underwater image processing is underwater image restoration. However, underwater image restoration is an essential but challenging task in the field of image processing. In this article, we propose an underwater image restoration framework based on physical priors, called PPIR-Net. The PPIR-Net combines prior knowledge with deep learning to greatly improve the structural texture and color information of underwater images. The framework estimates underwater transmission maps and underwater scattering maps through the structure restoration network (SRN). Moreover, the color correction network (CCN) is used to achieve image color correction. Extensive experimental results show that our method exceeds state-of-the-art methods on underwater image evaluation metrics.

关键词： Underwater image restoration Physical prior knowledge Deep learning

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