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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16198 条记录，以下是4961-4970 订阅

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UCED-Detector: An Ultra-fast Corner Event Detector for Event Camera in Complex Scenes 7

UCED-Detector: An Ultra-fast Corner Event Detector for Event...

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7th international conference on Electronic Information Technology and computer Engineering (EITCE)

作者： Lv, Xinyu Wang, Jianming Sun, Yukuan Tiangong Univ Tianjin Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin Peoples R China Tiangong Univ Ctr Engn Intership & Training Tianjin Peoples R China

ISBN: (纸本)9798400708305

The asynchronous event stream of event camera output overcomes exposure problems caused by dramatic changes in ambient light and motion blur caused by high-speed motion, which are common challenges with traditional cameras. With the increasing number of events per second delivered by event cameras, a faster feature extraction method is necessary to process large amounts of events to take advantage of event cameras for various computer vision tasks. We propose UCED-Detector, an event frame-based corner event detector that can detect features in event streams at three times the speed of the SOTA method. Firstly, we use events captured in the past to remove noise events from the current event stream. The events in the circular mask around the event to be detected are then constructed as event pairs and mark the events whose timestamps are one threshold larger than the other event in the event pair. Finally, the marked adjacent events are connected into arcs, and whether the event to be detected is a feature corner event is judged according to the arc length. To evaluate the performance of our proposed approach, we conducted extensive experiments on datasets of event cameras. The results show that our method reduces the detection time to one-third of the SOTA method and reduces the average processing time per event from 0.15 milliseconds to 0.04 milliseconds.

关键词： SLAM Event-based Camera Corner event detector computer vision

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Intelligent Equipment and Special Robots - Proceedings of the 2nd international conference, ICIESR 2023

Intelligent Equipment and Special Robots - Proceedings of th...

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2nd international conference on Intelligent Equipment and Special Robots, ICIESR 2023

ISBN: (纸本)9781643685144

The proceedings contain 80 papers. The topics discussed include: minibus booming noise reduction based on the driveline system torsional vibration control;design and implementation of multi-function logistics robots for intelligent warehousing;research on stability of integral type second order variable structure control for aircraft pitch channel;design of the slave hand structure and interference detection for the master-slave craniotomy surgical robot;research on speed sensorless vector control of permanent magnet synchronous motor;an intelligent lotus root harvesting equipment;a machine vision-based edge detection method for belt lap of pipe belt conveyor;design of infrared automatic running water alarm device;belt rotation in the pipe conveyor: research on a detection method based on image processing;and design and implementation of post-disaster search and rescue robot based on Beidou navigation and positioning system.

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vision-Based Smart Sprayer for Precision Farming 6th

Vision-Based Smart Sprayer for Precision Farming

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6th Iberian Robotics conference (Robot)

作者： Deguchi, Thaidy Baltazar, Andre Rodrigues dos Santos, Filipe Neves Mendonca, Helio Univ Porto FEUP Fac Engn P-4200465 Porto Portugal INESC TEC Inst Syst & Comp Engn Technol & Sci CRIIS Ctr Robot Ind & Intelligent Syst P-4200465 Porto Portugal

ISBN: (纸本)9783031591662;9783031591679

Since the advent of agriculture, humans have considered phytopharmaceutical products to control pests and reduce losses in farming. Sometimes some of these products, such pesticides, can potentially harm the soil life. In the literature there is evidence that AI and image processing can have a positive contribution to reduce phytopharmaceutical losses, when used in variable rate sprayers. However, it is possible to improve the existing sprayer system's precision, accuracy, and mechanical aspects. This work proposes spraying solution called GraDeS solution (Grape Detection Sprayer). GraDeS solution is a sprayer with two degrees of freedom, controlled by a AI-based algorithm to precisely treat grape bunches diseases. The experiments with the designed sprayer showed two key points. First, the deep learning algorithm recognized and tracked grape bunches. Even with structure movement and bunch covering, the algorithm employs several strategies to keep track of the discovered objects. Second, the robotic sprayer can improve precision in specified areas, such as exclusively spraying grape bunches. Because of the structure's reduced size, the system can be used in medium and small robots.

关键词： precision farming machine learning computer vision robotics

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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional Neural Networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Wave Deep Neural Network for Multi-Class MRI Brain image Classification 2

Wave Deep Neural Network for Multi-Class MRI Brain Image Cla...

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2nd international conference on Edge Computing and Applications, ICECAA 2023

作者： Ramasamy, Viswanathan Vinodhini, V. Saravanan, T.R. Kumar, S. Dinesh Lavanya, S. Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350347579

The human brain is considered to be the most important organ in the body. Since the causes of brain cancer is still unknown, early detection is required for proper treatment. Magnetic Resonance Imaging (MRI) is an imaging technology used to depict inside structure of human body in details. This research study makes a contribution to the development of an image-based classification system as well as to the detection of brain cancer. Texture-based energy descriptors are retrieved using Discrete Wavelet Transform (DWT)-based sparse representation systems. A Convolutional Neural Network (CNN) in wavelet domain is used for further processing in order to improve the classification between normal and abnormal classes of MRI brain images. The performance of the proposed system is assessed by utilizing the standard collection of brain tumor images available in the REMBRANDT database. Results prove that the proposed system provides 98% accurate results for brain cancer classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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CT image Synthesis from MR image Using Edge-Aware Generative Adversarial Network 1

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7th international conference on computer vision and image processing, CVIP 2022

作者： Joseph, Jiffy Prasanth, Rita Maret, Sebin Abraham Pournami, P.N. Jayaraj, P.B. Puzhakkal, Niyas Department of Computer Science and Engineering National Institute of Technology Calicut Kerala Calicut India Department of Medical Physics MVR Cancer Centre and Research Institute Kerala Calicut India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are two widely used medical imaging techniques in radiology to form pictures of the anatomy and the human body’s physiological processes. Radiotherapy planningrequires the use of CT as well as MRimages. The high radiation exposure of CT and the cost of acquiring multiple modalities moti-vateareliableMRI-to-CT ***-to-CTsynthesiserintroduced in this paper implements a deep learning model called Edge-aware Generative Adversarial Network (EaGAN). This model includes edge information into the traditional Generative Adversarial Network (GAN) using the Sobel operator to compute edge maps along two directions. Doing this allows us to focus on the structure of the image and the boundary lines present in the image. Three variants of the EaGAN model are trained and tested. A model called discriminator-induced EaGAN (dEaGAN) that adversarially learns the edge information in the images is proven to generate the best results. It possesses a Mean Absolute Error (MAE) of 67.13HU, a Peak Signal to Noise Ratio (PSNR) of 30.340 dB, and a Structural Similarity Index (SSIM) of 0.969. The proposed model outperforms the state-of-the-art models and generates CT images closerto the ground truth CTs. The synthesised CTs are beneficial in medical diagnostic and treatment purposes to a greater extent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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Bangla Speech-based Emotion Detection using a Hybrid CNN-Transformer Approach 8

Bangla Speech-based Emotion Detection using a Hybrid CNN-Tra...

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8th international conference on Communication, image and Signal processing, CCISP 2023

作者： Ali Shuvo, S.M. Haider Khan, Rahad Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh United International University United City Department of Computer Science and Engineering Madani Ave Dhaka1212 Bangladesh

ISBN: (纸本)9798350305838

Robots are now widely employed in various scenarios to interact with humans. It is vital that the robots understand the speaker's emotion and respond accordingly. Humans possess innate abilities to recognize emotions from speech, but it is challenging to provide machines with the same capabilities to predict emotions from human speech. Recently machine learning and deep learning techniques are employed for emotion recognition tasks. Due to emotions such as angry, surprise etc. being predominant for only few time frames of an example audio of that class, it becomes difficult to detect them and models often have a hard time for these classes. We employ a hybrid CNN-Transformer approach where we attempt to solve this problem by having both locally receptive convolutional features and globally receptive attention features contributing to final classification. We evaluate our approach along with other standard approaches in BanglaSER dataset. We report weighted class accuracy of 92.3% using our proposed approach achieving significant performance gain over current literature. © 2023 IEEE.

关键词： Emotion Recognition

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Influence of Kernel Size and Layer Size for MAE Score and Model Fit in image Denoising 4

Influence of Kernel Size and Layer Size for MAE Score and Mo...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Lairan, Natalia Rezer, Edra Theresa Liang, Ricky Pratama Lucky, Henry Iswanto, Irene Anindaputri School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

The presence of noise in images is one of the challenges that complicate image processing. Numerous methods have been proposed to mitigate the impact of noise on denoising image performance. However, the application of CNN for denoising image performance optimization in the presence of noise is still a new area of research. In this paper, the configuration of kernel size and number of layers will be investigated in image denoising using CNN (Convolutional Neural Networks), specifically, in U-Net architecture. The images used are from BSD500 which consists of 500 natural images. Then, the hyperparameters will be evaluated in terms of many factors, such as mean absolute error (MAE) score and model fit to show its influence. The results show that the average score for MAE is 0.11 based on the model and variation of both kernel size and layer size, with the highest score found at kernel size=2 and layer size=64. The results show that to get a fit model, the experience should use epoch number 10. It shows that the influence of kernel size has a negative influence on MAE whereas layer size has a beneficial effect on model fit. However, there is an optimal point beyond which denoising results can decrease. To achieve better results, future researchers can compare it with other denoising models to ensure our findings. © 2023 IEEE.

关键词： image denoising

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Encoding Semantic Attributes - Towards Explainable AI in Industry 23

Encoding Semantic Attributes - Towards Explainable AI in Ind...

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16th ACM international conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Schneider, Sarah Antensteiner, Doris Soukup, Daniel Scheutz, Matthias Tufts Univ Human Robot Interact Lab Boston MA 02111 USA AIT High Performance Vis Syst Vienna Austria

ISBN: (纸本)9798400700699

The transformation of industrial environments is progressing at a fast pace as more and more autonomous systems are installed and operated. Save and explainable AI algorithms are thus essential, especially for collaborative interactive systems that operate in human spaces. We propose the "Semantic Encoder", a 2D-vision based CNN model trained on a purely synthetic dataset, to address the explainability aspect by extracting semantic descriptions of real objects based on their visual appearances. We can use the extracted semantic information to simply describe depicted samples or to differentiate between normal and anomalous samples, with the possibility to explain what caused the anomaly detection. The semantic description can be further used to sort samples by classifying them or to find a sample with specific semantic properties. We evaluate the Semantic Encoder with respect to its informative power by comparing the computed semantic features with features extracted by a VGG-16 model and classical image processing methods. The results are quantified based on the Generalized Discriminative Value (GDV). We also investigate how accurately anomalous samples are detected by computing ROC and PR curves. We use the semantic parameters to understand what causes good and inaccurate anomaly detection decisions. In addition, we evaluate the quality of the classification based sorting by examining confusion matrices and classification accuracy.

关键词： computer vision neural networks anomaly detection novelty detection machine learning

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Hand Gesture-Based AI System for Accessing Windows Applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

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4th international conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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