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检索条件"任意字段=Conference on Real-Time Image Processing 2006"

共 25734 条记录，以下是401-410 订阅

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Non Homogeneous realistic Single image Dehazing

Non Homogeneous Realistic Single Image Dehazing

引用

23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Vinay, P. Abhisheka, K. S. Shetty, Lithesh Kushal, T. M. Shylaja, S. S. PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350320565

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be dehazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.

关键词： Computer vision image color analysis conferences Moisture Object detection Computer architecture real-time systems

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A New Hybrid Pupil Detection Algorithm for real time Applications 11th

A New Hybrid Pupil Detection Algorithm for Real Time Applica...

引用

11th International conference on E-Health and Bioengineering (EHB)

作者： Bonteanu, Gabriel Bonteanu, Petronela Cracan, Arcadie Bozomitu, Radu Gabriel Gheorghe Asachi Tech Univ Iasi Fundamentals Elect Dept Iasi Romania Gheorghe Asachi Tech Univ Iasi Telecommun & IT Dept Iasi Romania

ISBN: (纸本)9783031625220;9783031625237

This paper presents a new hybrid pupil detection algorithm based on both AI and classical image processing techniques. The problem addressed by the AI technique of neural networks is the detection of the binarization threshold. Thus, using the percentile function calculated in a sufficiently large number of points, a convolutional neural network with a reduced number of layers is used to determine the appropriate threshold necessary to obtain the binarized eye image. Furthermore, classic image processing solutions (morphological operations, Laplacian based edge detection, convexity correction, ellipse fitting) are used to detect the center of the pupil. Due to a good immunity to variable and non-uniform lighting conditions, the proposed algorithm shows a high detection rate when tested on three representative databases.

关键词： Convolutional Neural Network Percentile Pupile Detection Algorithm Eye Tracking image processing

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CMFNet: An End-to-End Ultra-Lightweight Underwater image Enhancement Network 7

CMFNet: An End-to-End Ultra-Lightweight Underwater Image Enh...

引用

7th IEEE Information Technology, Networking, Electronic and Automation Control conference, ITNEC 2024

作者： Li, Shiyan Yang, Xiaolong Han, Huajin Meng, Qingbo Tianjin Navigation Instruments Research Institute Tianjin China Tianjin Qisuo Precision Electromechanical Technology Co. Ltd Tianjin China Tianjin Key Laboratory of Special Severe Enviroment Computer Tianjin China

ISBN: (纸本)9798350370782

As the main carrier of underwater information, underwater video images play a vital role in human exploration and development of the ocean. However, due to the optical properties of water and the complex and variable underwater environmental factors, the quality of observed underwater images is severely degraded, affecting the execution of underwater equipment tasks. Moreover, most current underwater image enhancement methods have high computational costs, which limits their application in real-time large-scale underwater image processing. In order to solve this problem, we propose the CMFNet, a new lightweight network can efficiently enhance underwater images in real time. This network processes through a color recovery module, a multi-scale feature extraction module, and a feature fusion module to improve image color representation while maintaining content style and spatial texture. To the best of our knowledge, CMFNet is the only network that can enhance 2K resolution images in real time. Extensive experiments and visual comparisons have shown that CMFNet can achieve optimal or suboptimal results with fewer parameters and computational complexity compared to other advanced underwater image enhancement methods, making it valuable for practical applications. © 2024 IEEE.

关键词： Underwater imaging

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A real-time JPEG image compression hardware design architecture 18

A real-time JPEG image compression hardware design architect...

引用

18th International conference on Advanced Computing and Analytics, ACOMPA 2024

作者： Khai, Lam Duc Van Quang, Tran Luan, Pham Hoai University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Nara Institute of Science and Technology Ikoma Nara630-0192 Japan

ISBN: (纸本)9798331542467

In technology 4.0, all data is put on the Cloud for transmission and processing. images are one of the most important data and are quite large because they are created every day. Therefore, the increased demand for image storage and transmission urges scientists to research image compression methods to reduce the excess image data and it can solve the above problem. This paper presents the Field Programmable Gate Array (FPGA) hardware architecture of the JPEG image compression method (the world's most popular image compression method) using FDCT numerically calculated Floating Point combined with Pipeline. The main goal of the proposed method is to increase the processing speed and compression performance of the image. The tests were performed on Red-Green-Blue (RGB) standard images. Evaluated by execution time, mean error value (MSE), top noise ratio (PSNR), and compression ratio (CR). This complete design is synthesized on the Vitex7-VC709 board with a frequency of 100.7 Mhz, throughput of 250 FPS, and power consumption of 2.401 Watts. © 2024 IEEE.

关键词： image compression

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Test-time Training for Matching-based Video Object Segmentation 37

Test-time Training for Matching-based Video Object Segmentat...

引用

37th conference on Neural Information processing Systems (NeurIPS)

作者： Bertrand, Juliette Kordopatis-Zilos, Giorgos Kalantidis, Yannis Tolias, Giorgos Czech Tech Univ VRG FEE Prague Czech Republic NAVER LABS Europe Meylan France

ISBN: (纸本)9781713899921

The video object segmentation (VOS) task involves the segmentation of an object over time based on a single initial mask. Current state-of-the-art approaches use a memory of previously processed frames and rely on matching to estimate segmentation masks of subsequent frames. Lacking any adaptation mechanism, such methods are prone to test-time distribution shifts. This work focuses on matching-based VOS under distribution shifts such as video corruptions, stylization, and sim-to-real transfer. We explore test-time training strategies that are agnostic to the specific task as well as strategies that are designed specifically for VOS. This includes a variant based on mask cycle consistency tailored to matching-based VOS methods. The experimental results on common benchmarks demonstrate that the proposed test-time training yields significant improvements in performance. In particular for the sim-to-real scenario and despite using only a single test video, our approach manages to recover a substantial portion of the performance gain achieved through training on real videos. Additionally, we introduce DAVIS-C, an augmented version of the popular DAVIS test set, featuring extreme distribution shifts like image-/video-level corruptions and stylizations. Our results illustrate that test-time training enhances performance even in these challenging cases. Project page: https://***/test-time-training-vos/

关键词： image segmentation

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Colour Correction and Detail Enhancement in Underwater images using Hybrid real-ESRGAN 3

Colour Correction and Detail Enhancement in Underwater Image...

引用

3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandar, Vijay Nair, Vishnu G Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals. © 2023 IEEE.

关键词： Batch processing image Enhancement image Stretching MSI PSNR real-ESRGAN

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AI-based image processing Technique for Dielectrophoresis (DEP) in BioMEMs Applications 16

AI-based Image Processing Technique for Dielectrophoresis (D...

引用

16th IEEE International conference on Semiconductor Electronics (ICSE)

作者： Tee, Clarence Augustine T. H. Majlis, Burhanuddin Yeop Li, Sheng Yu, En Hao Buyong, Muhamad Ramdzan Song, Le Yeo, W. P. Zain, Ahmad Rifqi Md Zheng, Yelong Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Zhejiang Peoples R China Univ Kebangsaan Malaysia Inst Microengn & Nanoelect IMEN Ukm Bangi 43600 Selangor Malaysia Zhejiang Inst Optoelect Jinhua 321004 Zhejiang Peoples R China Zhejiang Normal Univ Photon & Sensors Key Res Lab Jinhua 321004 Zhejiang Peoples R China Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin 300072 Peoples R China

ISBN: (纸本)9798350378313

The integration of artificial intelligence (AI) methodology in the flexible electronics, BioMEMs and Dielectrophoresis (DEP) system provides an accurate, comprehensive, simple and cost-effective approach especially in the real time cell sorting, target identification, monitoring, biological entities separation, analysis and postulation. Here, a novel approach integrating AI algorithms with DEP technology for BioMEMs system has been demonstrated in real-time bacteria identification via detecting, identifying simultaneously, lock-in monitoring bacterial movement trajectories, analyzing, measuring and calculating bacteria's velocities of different frequencies. The AI-based bacterial recognition system creates real-time, fast, accurate, unlimited bacteria lock-in monitoring with high accuracy. Furthermore, the bacteria can be tracked, monitored, and analyzed using big data analysis and Deep Learning (DL) in real-time via a visual control interface system, as part of the Internet of Things (IoT) and intelligent information sharing between BioMEMS sensors and IoT networks.

关键词： AI-based fractional-order differential algorithm Dielectrophoresis (DEP) image processing BioMEMs Artificial Intelligence-Deep Learning (AI-DL)

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SARViT: VISION TRANSFORMER FOR SAR image INTERPRETATION WITH EFFICIENT MODEL COMPRESSION FOR time-real processing

SARViT: VISION TRANSFORMER FOR SAR IMAGE INTERPRETATION WITH...

引用

IET International Radar conference 2023, IRC 2023

作者： Ma, Jian Zhang, Hao Wang, Zhengjue Wang, Zhibing Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China School of Electronic Engineering Xidian University Xiaan China

ISBN: (纸本)9781839539954

The synthetic aperture radar (SAR) images provide all-time and all-weather observation for the earth, which has become one of important techniques for remote sensing (RS). Recently, the large-scale vision transformers (ViT) have made significant progress for natural image processing due to their good scalability and representations. However, how to deploy the ViT for SAR image processing has not been explored. In this paper, we firstly resort to ViT with large-scale SAR images for model pretraining and then attempt to fine-tune the model tailored to downstream tasks for investigating how such large models perform. Moreover, in order to meet the requirement of online time-real processing on the satellite, we then develop a method of the knowledge distillation for model compression from 320M to 36M, which can significantly reduce the computational cost and memory footprint while reserve good representations. The experimental results of downstream tasks including scene classification, object detection and building segmentation show state-of-the-art performance compared to existing methods. Further experiments for compressed models also show the competitive results in terms of accuracy and computational complexity. © The Institution of Engineering & Technology 2023.

关键词： image compression

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Plant Disease Detection using CNN and image processing 15

Plant Disease Detection using CNN and Image Processing

引用

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

作者： Dhivinkumar, A.J. Sophia, S. Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331300579

Plant disease outbreaks have been more common in recent years, which has put food security and agricultural output at serious risk. timely intervention and the avoidance of significant losses are contingent upon the swift and precise identification of these illnesses. Convolutional neural networks (CNN) combined with image processing techniques have become a potent tool for automated plant disease diagnosis in response to this difficulty. This study uses CNN and image processing to provide a novel method for plant disease identification. The suggested approach is taking pictures of plants, enhancing disease signs in the photos by pre-processing, and then using a CNN model that has been trained to classify diseases. A substantial dataset of plant photos, including both healthy and sick examples, is used to train the CNN model. The stages of feature extraction, feature mapping, and classification in the training process let the model recognize and identify various illness patterns. The suggested approach is effective, as evidenced by the experimental results, which show great accuracy and robustness in plant disease identification. In addition to improving illness diagnosis accuracy, CNN and image processing techniques provide a real-time, affordable solution that may be easily applied in the field. The findings of this study aid in the creation of automated plant disease management systems, which help farmers and other agricultural professionals identify and treat plant illnesses quickly, increasing crop yields and sustainability. © Grenze Scientific Society, 2024.

关键词： image enhancement

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PhISH-Net: Physics Inspired System for High Resolution Underwater image Enhancement

PhISH-Net: Physics Inspired System for High Resolution Under...

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IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Chandrasekar, Aditya Sreenivas, Manogna Biswas, Soma Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9798350318920;9798350318937

Underwater imaging presents numerous challenges due to refraction, light absorption, and scattering, resulting in color degradation, low contrast, and blurriness. Enhancing underwater images is crucial for high-level computer vision tasks, but existing methods either neglect the physics-based image formation process or require expensive computations. In this paper, we propose an effective framework that combines a physics-based Underwater image Formation Model (UIFM) with a deep image enhancement approach based on the retinex model. Firstly, we remove backscatter by estimating attenuation coefficients using depth information. Then, we employ a retinex model-based deep image enhancement module to enhance the images. To ensure adherence to the UIFM, we introduce a novel Wideband Attenuation prior. The proposed PhISH-Net framework achieves real-time processing of high-resolution underwater images using a lightweight neural network and a bilateral-grid-based upsampler. Extensive experiments on two underwater image datasets demonstrate the superior performance of our method compared to state-of-the-art techniques. Additionally, qualitative evaluation on a cross-dataset scenario confirms its generalization capability. Our contributions lie in combining the physics-based UIFM with deep image enhancement methods, introducing the wideband attenuation prior, and achieving superior performance and efficiency.

关键词： Algorithms Algorithms Computational photography image and video synthesis Low-level and physics-based vision

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