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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery

Improving Lens Flare Removal with General-Purpose Pipeline a...

引用

ieee/CVF International conference on computer vision (ICCV)

作者： Zhou, Yuyan Liang, Dong Chen, Songcan Huang, Sheng-Jun Yang, Shuo Li, Chongyi Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Pattern Anal & Machine Intelligence Coll Comp Sci & Technol Nanjing Peoples R China DJI Innovat Co Ltd Imaging Technol Grp Shanghai Peoples R China Nankai Univ Sch Comp Sci Tianjin Peoples R China

ISBN: (纸本)9798350307184

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.

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Trustworthy vision for Autonomous Vehicles: A Robust Logic-Infused Deep Learning Approach 27

Trustworthy Vision for Autonomous Vehicles: A Robust Logic-I...

引用

27th ieee International conference on Intelligent Transportation Systems, ITSC 2024

作者： Chaghazardi, Zahra Fallah, Saber Tamaddoni-Nezhad, Alireza University of Surrey Dept. of Computer Science United Kingdom University of Surrey Mechanical Engineering Sciences United Kingdom

ISBN: (纸本)9798331505929

Deep learning constitutes a fundamental pillar in the field of image recognition within autonomous vehicles (AVs), facilitating precise predictions based on unprocessed data. However, unlike human cognition, deep learning models are susceptible to adversarial attacks. This paper proposes a novel approach, termed the Robust Logic-infused Deep Learning (RLDL) Approach, designed for traffic sign recognition. RLDL employs Inductive Logic Programming (ILP) to derive logical rules from a combination of positive and negative examples. These rules are subsequently transformed into a matrix of logical constraints, allowing for the assessment of logical consistency in predictions. Then, this logical consistency is incorporated into the neural network through the loss function. This study explores the impact of integrating logical constraints into deep learning models on the reliability of vision tasks in AVs. Our experiments demonstrate that the proposed method substantially enhances the accuracy of recognising traffic signs under adversarial attacks. © 2024 ieee.

关键词： Contrastive Learning

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An EEG Signal Analysis Method Based on Common Spatial pattern and Support Vector Machine Algorithm 7

An EEG Signal Analysis Method Based on Common Spatial Patter...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Wang, Yuxin Liu, Bao Tang, Yuqi Qingdao China The 22nd Research Institute of China Electronics Technology Group Corporation Qingdao China

ISBN: (纸本)9798350350890

To solve the problem of EEG signal on feature extraction and low recognition rate, we analyze the characteristics of EEG signal and propose an EEG signal analysis method based on common spatial pattern (CSP) and support vector machine (SVM) algorithms. In this method, we first preprocess the EEG signal by wavelet packet transform (WPT) and extract the specific frequency band signal in the EEG signal. Then the features of the EEG signal are extracted by constructing pair-wise CSP (PW-CSP) filters. Finally, the recognition of the EEG signal was realized by the support vector machine algorithm which optimizes parameter selection by particle swarm optimization (PSO), and the 2008 BCI competition 2A data set was used to verify the classification effect of the algorithm. The experimental results indicated that the classification accuracy of the method may reach up to 93.07%, and the average accuracy is higher than the results of the top two in the 2008 brain-computer interface (BCI) competition. The EEG signal features extracted by this method can reflect the signal characteristics of EEG well, and improve the accuracy of classification, which may assist in the development and application of BCI. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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Temporal Hierarchical Quick Spatial Attention Mechanism for Heart Rate Measurement 5

Temporal Hierarchical Quick Spatial Attention Mechanism for ...

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5th International conference on computer vision, Image and Deep Learning, CVIDL 2024

作者： Ji, Minghao Hu, Min Wang, Xiaohua He, Lei Hefei University of Technology School of Computer and Information Hefei China Hefei University of Technology School of Mathematics Hefei China

ISBN: (纸本)9798350373820

Remote photoplethysmography (rPPG) is a promising non-contact method for measuring heart rate (HR) and physiological signals. However, current deep learning approaches in this field primarily focus on extracting subtle rPPG cues using convolutional neural networks with limited spatio-temporal receptive fields. This approach overlooks the importance of long-distance temporal perception and interaction, which are crucial for accurate rPPG modeling. In this paper, we present an end-to-end temporal hierarchical quick spatial attention (THQSA) network to adaptive aggregate the local and global temporal features through multi-scale temporal slice perception and interaction. Specifically, the Temporal Hierarchical Block (THB) is firstly constructed to incorporate the global periodic rPPG attention features across multi-scale temporal slices attention mechanism. And then THB refines local temporal slice representations to account for disturbances. Quick spatial attention is further developed to ensure the model obtains better global and local feature representation by avoiding irrelevant features. Additionally, we apply the rPPG signal to a 1-D convolutional network for emotion recognition. Promising results are achieved in the emotion test. To demonstrate the effectiveness of our approach, extensive experiments conducted on two benchmark datasets showcase an outperformance over existing state-of-the-art methods. © 2024 ieee.

关键词： Emotion recognition

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Dynamic Color Identification Using computer vision: OpenCV with Pandas 4

Dynamic Color Identification Using Computer Vision: OpenCV w...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mohanraj, E. Kirubashini, V. Gobika, B. Archana, A.M. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798331523923

computer vision, the cornerstone of modern artificial intelligence. Moving forward with the development of new tools and techniques, OpenCV (Open Source computer vision Library) coupled with pandas' powerful data management capabilities has emerged as a versatile framework for real-world image processing tasks. This paper explores the application of OpenCV and pandas for real-time color recognition and OpenCV's enhanced functionality, such as video stream processing taking advantage of Pandas with efficient data management. The proposed system captures real-time images and processes user-defined interactions to identify the color. By combining pandas, the system optimizes access to the predefined color data stored in a CSV file, allowing for seamless color matching. This OpenCV and pandas together enables the system to dynamically display color names, RGB components and hex values in real time enhancing user interaction by double clicking. The combination of these technologies represents a powerful method for solving image-based problems for innovation and efficiency. Real world applications demonstrate their capabilities. © 2025 ieee.

关键词： Information management

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NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces

NewtonianVAE: Proportional Control and Goal Identification f...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jaques, Miguel Burke, Michael Hospedales, Timothy Univ Edinburgh Edinburgh Midlothian Scotland Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

Learning low-dimensional latent state space dynamics models has proven powerful for enabling vision-based planning and learning for control. We introduce a latent dynamics learning framework that is uniquely designed to induce proportional controlability in the latent space, thus enabling the use of simple and well-known PID controllers. We show that our learned dynamics model enables proportional control from pixels, dramatically simplifies and accelerates behavioural cloning of vision-based controllers, and provides interpretable goal discovery when applied to imitation learning of switching controllers from demonstration. Notably, such proportional controlability also allows for robust path following from visual demonstrations using Dynamic Movement Primitives in the learned latent space.

关键词： Visualization computer vision Proportional control Cloning Switches Aerospace electronics Planning

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Research on intelligent insect species identification based on computer vision 7

Research on intelligent insect species identification based ...

引用

7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Huang, Bin Nanchang Institute of Technology Jiangxi Nanchang330044 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

Image-based insect species identification is a comprehensive application of computer vision technology, image processing technology and pattern recognition technology to realize insect species identification. It is of great significance to find and identify crop insects in time and quickly, so as to take corresponding measures to prevent and control pests in time and reduce the losses caused by pests. In this paper, intelligent insect species identification based on computer vision is studied. For insect images, using feature fusion technology will obtain more comprehensive insect image features, which will greatly improve the recognition accuracy and improve the recognition efficiency of the system. In the learning process, firstly, a certain number of insect images are selected as training samples, and the feature vectors of insects are extracted as the input vectors of SVM(support vector machine) training, and the input vectors are learned to get the best classification model. The experimental results show that the average recognition rate of insects by feature fusion is 94.747%. The experimental results show that the research results of this paper provide theoretical and practical basis for intelligent insect species recognition technology based on computer vision. © 2023 SPIE. All rights reserved.

关键词： Support vector machines

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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A Review on Facial Emotion recognition of Subjects Using Deep Learning Techniques 1

A Review on Facial Emotion Recognition of Subjects Using Dee...

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1st International conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE)

作者： Kumar, P. Siva Nikhil, M. Rudresh, P. H. S. Kiran, K. Sai Charan, K. Prasad, K. S. N. Sasi Inst Technol & Engn Comp Sci & Engn Tadepalligudem Andhra Pradesh India

ISBN: (纸本)9798350360806;9798350360790

The process of recognizing human emotion is called emotion recognition. The degree to which people can accurately identify the emotions of others varies greatly. The field of study on using technology to assist individuals in recognizing emotions is still in its infancy. The significance of facial emotion recognition lies in its ability to assist computers in deciphering human emotions from our faces. Its relevance can be summed up as follows: it can aid with mental health, make technology friendlier and more helpful, and even enhance products like video games and manufacturing processes. This approach analyzes facial expressions and characteristics using computer vision techniques to determine emotions. Commonly accepted basic emotion categories include happiness, anger, fear, disgust, and surprise. Ensuring optimal functionality of the technology for users from diverse backgrounds.

关键词： Emotion recognition Emotion AI Electroencephalogram (EEG) Human-computer Interaction (HCI)

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Efficient Pest Classification Using Lightweight Neural Networks for Sustainable Pest Control 4

Efficient Pest Classification Using Lightweight Neural Netwo...

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4th International conference on computer Communication and Artificial Intelligence (CCAI)

作者： Yang, Jiaming Zhou, Yinghua Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9798350362770;9798350362763

The existence of pests and diseases threatens the agricultural production and brings serious economic losses to countries whose economy is based on agriculture. Traditional agricultural production methods require a lot of manpower to identify and detect pests and diseases, which will increase the cost of agricultural production. Now, with the development of deep learning technology, computer vision based approaches can help reduce costs and improve efficiency. Through the deep learning model based on CNN, insects and pests can be accurately detected and recognized. This paper proposes an improved recognition model, MFnet, based on the lightweight deep neural network MobileNetV3. A new activation function, MLU6, is introduced in the proposed MFnet model. At the same time, we use the PolyLoss function to replace the cross-entropy loss function when training the proposed model. We also use transfer learning to improve the efficiency of network optimization and prevent overfitting through data augmentation and regularization. Ablation and comparative experiments are carried out and the IP102 pest dataset is used in the experiments. The experimental result shows that MFnet outperforms the original Mobilenetv3 and the other classic models in terms of classification accuracy, at the cost of increased number of parameters compared to the original Mobilenetv3. However, the parameter number, training time and classification time of the proposed model are still much less than those of the other classic models.

关键词： agriculture pest recognition computer vision image classification convolutional neural networks

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