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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14659 条记录，以下是701-710 订阅

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DeTrack: In-model Latent Denoising Learning for Visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhou, Xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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Single image Depth Estimation Based on Improved U-NET 3

Single Image Depth Estimation Based on Improved U-NET

引用

3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wang, Yingkang Zhang, Yicheng Zhao, Wei Liu, Congbiao Tianjin Vocational and Technical Normal University National and Local Joint Engineering Laboratory of Automotive Mold Intelligent Manufacturing Technology Tianjin300350 China National-Local Joint Engineering Laboratory of Intelligent Manufacturing Oriented Automobile Die & Mould Tianjin300350 China

ISBN: (纸本)9798350331387

para>In response to the problems of low accuracy and complex network structure in existing deep learning based monocular image depth estimation algorithms, an improved U-NET [1] monocular image depth estimation method based on dense skip connections is proposed. The traditional U-NET network has the problem of depth distortion when processing depth estimation tasks. In response to this problem, this algorithm proposes skip connections to achieve multi-scale feature fusion from encoder to decoder;And introduce attention mechanism into the decoder to further improve the accuracy of depth estimation. The training and verification were carried out on the indoor data set KITTI [2] and the outdoor data set NYU Depth V2 respectively. The experimental results show that compared with the advanced algorithm DORN, all indicators have been improved to varying degrees, in which the root-mean-square error has been reduced by 24.1% and the square relative error has been reduced by 15.2%. © 2023 IEEE.

关键词： Complex networks

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Bacterial colony detection method for microbiological photographic images

Bacterial colony detection method for microbiological photog...

引用

IEEE International conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Gorokhov, Oleg Fazylov, Ramazan Kazachuk, Maria Lazukhin, Ivan Mashechkin, Igor Pankratyeva, Liudmila Popov, Ivan Lomonosov Moscow State Univ MSU Comp Sci Dept Moscow Russia Dmitry Rogachev Natl Med Res Ctr Pediat Hematol Moscow Russia

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

This paper is dedicated to the research and development of automated methods for detecting bacterial colonies on photographic images of Petri dishes. We propose a segmentation algorithm for microbiological photographic images which is based on the convolutional neural network architecture of the encoder-decoder class, trained on artificially generated image data. Our method consists of two stages: sliding window image processing by the proposed neural network to find areas containing isolated objects potentially suitable for further research, and further segmentation of the resulting binary area map into regions containing colonies by classical computer vision algorithms. According to the experimental research, the developed algorithm shows the best results in comparison with the existing neural network algorithms we adapt in this work as well as watershed segmentation algorithms. The work also proposes algorithms for microbiological photographic images artifact searching based on heuristic and neural network approaches (including flare, magnetic balls, barcodes and condensate), the use of which allows to increase the quality of the image segmentation algorithm, as well as to find errors caused by the human factor. The results of this work might be further used for classification algorithms of the bacterial colonies in the segments found, which will allow in some cases fully automated process of visual bacteria recognition on Petri dishes photographic images.

关键词： microbiological analysis bacterial colony detection artifact detection object detection computer vision machine learning data mining image segmentation heuristic algorithms neural networks Petri dish

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Real-time Low Vision Simulation in Mixed Reality 16

Real-time Low Vision Simulation in Mixed Reality

引用

16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Acevedo, Valeria Colantoni, Philippe Dinet, Eric Tremeau, Alain Univ Jean Monnet Univ Lyon Lab Hubert Curien UMR 5516 St Etienne France

ISBN: (纸本)9781665464956

Visual impairments are a global health issue with profound socioeconomic ramifications in both the developing and the developed world. There exist ongoing research projects, that aim to investigate the influence of light in the perception of low vision individuals. But as of today, there is neither clear knowledge nor extensive data regarding the influence of light in low vision situations. This research will address these issues by introducing a methodology and a system to simulate visual impairments. A pipeline based on eye anatomy coupled with real-time image processing algorithms allows to dynamically simulate low vision specific characteristics of selected impairments in mixed reality. An original new approach based on massively parallelized processing combined with an efficient modeling of eye refractive errors aims to improve the accuracy of the low vision simulation.

关键词： mixed reality low-vision simulation parallel programming image processing

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Musa Base - Mobile Application for Banana Farmers to Minimize the Challenges in Banana Production during the Pre-Harvest and Post-Harvest Period

Musa Base - Mobile Application for Banana Farmers to Minimiz...

引用

2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavinda, G.H.M. Dissanayake, D.D. Nanayakkara, K.A.K.K. Swrnakantha, N.H.P. Ravi Supunya Liyanagolla, Mudiyanselage. N. V. K. Kumari, P.K.S. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

This research paper presents an innovative solution to address the multifaceted challenges encountered by banana farmers in Sri Lanka, encompassing the entire spectrum of banana production from pre-harvest to post-harvest stages. The proposed system, titled "Musa-Base,"is a user-friendly mobile application that seamlessly integrates cutting-edge technologies, including machine learning, IoT-based approaches, natural language processing, and image processing methodologies. This holistic approach aims to elevate the quality and productivity of banana cultivation. The Musa-Base system comprises several interlinked modules: crop yield prediction, disease identification through advanced image processing and interactive chatbot interactions, personalized recommendations for optimal watering and fertilizer strategies, and comprehensive guidance on best agronomic practices. The research rigorously employs a diverse range of algorithms, including deep learning models, for accurate crop yield prediction, rapid disease diagnosis, and finely tuned recommendation systems. The system empowers farmers with informed decision-making capabilities, enabling them to mitigate losses, efficiently allocate resources, and ultimately enhance banana production and product quality. The integration of images of banana plants, sensor data, and user input data forms the backbone of the data-driven research methodology that underpins the development of the Banana Mobile Application. The pivotal outcomes of this research include precise disease detection, reliable harvest forecasting, and the dynamic integration of a chatbot for timely assistance, all of which collectively contribute to superior banana crop management, heightened agricultural efficiency, and enhanced sustainability. This comprehensive solution caters to the needs of both smallholder farmers and commercial producers, representing a significant advancement in modernizing banana farming practices and ensuring food security. © 2023 IE

关键词： Banana Farming Banana quality Banana symptoms Disease detection Fertilization technique Harvest prediction image processing IoT-based technologies Machine learning Mobile application post-harvest practices Pre-harvest practices Watering plans

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Application of Color Filtering and image Filtering algorithms in Circuit Diagnosis of Smart Meter Box 22

Application of Color Filtering and Image Filtering Algorithm...

引用

7th International conference on Multimedia systems and Signal processing, ICMSSP 2022

作者： Chen, Ming Ji, Chengyue Mao, Dilin Wang, Yu-Nan Ma, Yuan Cao, Xiu State Grid Shanghai ShiNan Electric Power Supply Company China Fudan University China

ISBN: (纸本)9781450396424

In order to identify the right or wrong wiring of the smart meter box, a hybrid model composed of the object detection model and the image classification model was built to detect the key parts of the smart meter box and identify the right or wrong wiring. The main work of this paper is to introduce color filtering, corrosion, expansion and Gaussian filtering and other image processing algorithms before the photos enters the image classification algorithm to identify the right or wrong wiring to filter out irrelevant components such as air switches and wiring terminals in the meter box photos, and keep the key features of the line. Experiments show that after the image is processed by color filtering and image filtering, the subsequent model training process converges faster, and the recognition accuracy is increased from 93.9% to 98.78%, which effectively improves the efficiency of model training and the accuracy of image recognition. © 2022 ACM.

关键词： image classification

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Chest X-ray image Classification: A Causal Perspective 1

引用

26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Nie, Weizhi Zhang, Chen Song, Dan Bai, Yunpeng Xie, Keliang Liu, An-An Tianjin Univ Tianjin 300072 Peoples R China Tianjin Univ Dept Cardiac Surg Chest Hosp Tianjin 300052 Peoples R China Tianjin Med Univ Clin Sch Thorac Tianjin 300052 Peoples R China Tianjin Med Univ Gen Hosp Dept Crit Care Med Dept Anesthesiol Tianjin 300052 Peoples R China Tianjin Med Univ Gen Hosp Tianjin Inst Anesthesiol Tianjin 300052 Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

The chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous advancements in deep learning-based methods capable of effectively classifying CXR. However, assessing whether these algorithms truly capture the cause-and-effect relationship between diseases and their underlying causes, or merely learn to map labels to images, remains a challenge. In this paper, we propose a causal approach to address the CXR classification problem, which involves constructing a structural causal model (SCM) and utilizing backdoor adjustment to select relevant visual information for CXR classification. Specifically, we design various probability optimization functions to eliminate the influence of confounding factors on the learning of genuine causality. Experimental results demonstrate that our proposed method surpasses the performance of two open-source datasets in terms of classification performance. To access the source code for our approach, please visit: https://***/zc2024/Causal_CXR.

关键词： Medical image processing Causal inference Chest X-ray image classification

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Emotion recognition based on facial gestures and Convolutional Neural Networks 3

Emotion recognition based on facial gestures and Convolution...

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3rd conference on Information Technology and Data Science

作者： Sanchez-Callejas, Francisco Emiliano Cruz-Albarran, Irving A. Morales-Hernandez, Luis A. Autonomous Univ Queretaro Fac Engn Queretaro Mexico

ISBN: (纸本)9798350387896;9798350387889

Humans express emotions verbally and non-verbally through their voice, facial expressions, and body language. Facial expression recognition systems can identify the emotional state of any person by using different intelligent algorithms, such as Support Vector Machines, Hidden Markov Models, and Convolutional Neural Networks, among others. This study focuses on facial expression recognition using eye and mouth regions of images from the FER-2013 dataset by training convolutional neural network (CNN) models. Seven emotional states - happy, sad, fear, anger, disgust, surprise and neutral - were identified. The methodology included segmenting and concatenating the images to form three CNN models. The best-performing model, a four-layer CNN with 8, 16, 32, and 64 filters, achieved remarkable results: 99.05% accuracy, 100.00% precision, 93.75% recall, 96.77% F1-score, 95.95% validation accuracy, and a 0.15 validation loss with a processing time of 3.03 minutes. It was possible to develop a CNN model capable of identifying seven emotional states from only the data of the eye and mouth region using concatenated images.

关键词： Facial gestures emotion recognition data concatenation FER CNN image segmentation

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An algorithm for parallel processing of traffic signs video on a graphics processor

An algorithm for parallel processing of traffic signs video ...

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2022 International conference on Information Science and Communications Technologies, ICISCT 2022

作者： Umarov, Mukhriddin Elov, Jamshid Khalilov, Sirojiddin Narzullayev, Inomjon Karimov, Marat Samarkand Br. of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Software Engineering Samarkand Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Nurafshan Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Information Technologies Tashkent Uzbekistan

ISBN: (纸本)9781665472296

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects. © 2022 IEEE.

关键词： image enhancement

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Underwater image Enhancement Using Pre-trained Transformer 21st

Underwater Image Enhancement Using Pre-trained Transformer

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21st International conference on image Analysis and processing (ICIAP)

作者： Boudiaf, Abderrahmene Guo, Yuhang Ghimire, Adarsh Werghi, Naoufel De Masi, Giulia Javed, Sajid Dias, Jorge Khalifa Univ Abu Dhabi U Arab Emirates Technol Innovat Inst ARRC Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031064333;9783031064326

The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first application of the image transformer-based approach called "Pre-Trained image processing Transformer" to underwater images. This approach is tested on the UFO-120 dataset, containing 1500 images with the corresponding clean images.

关键词： Vision transformer Underwater imaging image enhancement

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