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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是341-350 订阅

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Design of a System for Near-Infrared Composite Spectral image Acquisition and Information processing 2

Design of a System for Near-Infrared Composite Spectral Imag...

引用

2nd international conference on Automation, Robotics and computer Engineering, ICARCE 2023

作者： Zhou, Lin-Mei Cao, Peng Beijing Institute of Graphic Communication Beijing Key Laboratory of High-end Printing Equipment Signal and Information Processing Beijing China

ISBN: (纸本)9798350308341

The NIR camera is a device that captures images within the near-infrared spectral range, typically operating wavelengths from 700nm to 2500nm. The camera can possess exceptional penetrating capability and achieve multi-functions, making it a prominent subject of research and application in the industrial sector. The paper provides the system for near-infrared composite spectral image acquisition and information processing which is based on the Alvium 1800 U-130 VSWIR camera by Allied Vision. The system leverages QT Creator to establish an efficient and robust software platform with a user-friendly interface, facilitating NIR image acquisition and information processing across wavelengths ranging from 400 nm to 1700 nm. It is also equipped with narrow band filters for 940 nm, 1450 nm, and 1550 nm. The system effectively tackles challenges regarding the prevention of illegal copying, counterfeiting and infringement areas, detection of water content, differentiation of similar objects, and penetrability. Moreover, it offers practical applications in areas such as anti-counterfeiting measures, food safety assurance, and industrial processes. © 2023 ieee.

关键词： image acquisition

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KEYPOINTS DICTIONARY LEARNING FOR FAST AND ROBUST ALIGNMENT 30

KEYPOINTS DICTIONARY LEARNING FOR FAST AND ROBUST ALIGNMENT

引用

30th ieee international conference on image processing (ICIP)

作者： Artola, Aitor Kolodziej, Yannis Morel, Jean-Michel Ehret, Thibaud Univ Paris Saclay CNRS Ctr Borelli ENS Paris Saclay Paris France Visionairy Orsay France City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781728198354

Sparse keypoints based methods allow to match two images in an efficient manner. However, even though they are sparse, not all generated keypoints are necessary. This uselessly increases the computational cost during the matching step and can even add uncertainty when these keypoints are not discriminatory enough, thus leading to imprecise, or even wrong, alignment. In this paper, we address the important case where the alignment deals with the same scene or the same type of object. This enables a preliminary learning of optimal keypoints, in terms of efficiency and robustness. Our fully unsupervised selection method is based on a statistical a contrario test on a small set of training images to build without any supervision a dictionary of the most relevant points for the alignment. We show the usefulness of the proposed method on two applications, the stabilization of video surveillance sequences and the fast alignment of industrial objects containing repeated patterns. Our experiments demonstrate an acceleration of the method by 20 factor and significant accuracy gain.

关键词： Keypoints SIFT a contrario detection RANSAC image alignment

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MI-GAN: A Simple Baseline for image Inpainting on Mobile Devices

MI-GAN: A Simple Baseline for Image Inpainting on Mobile Dev...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Sargsyan, Andranik Navasardyan, Shant Xu, Xingqian Shi, Humphrey Picsart AI Res PAIR Miami FL 33142 USA Georgia Tech Oregon & UIUC SHI Labs Urbana IL USA

ISBN: (纸本)9798350307184

In recent years, many deep learning based image inpainting methods have been developed by the research community. Some of those methods have shown impressive image completion abilities. Yet, to the best of our knowledge, there is no image inpainting model designed to run on mobile devices. In this paper we present a simple image inpainting baseline, Mobile Inpainting GAN (MI-GAN), which is approximately one order of magnitude computationally cheaper and smaller than existing state-of-the-art inpainting models, and can be efficiently deployed on mobile devices. Excessive quantitative and qualitative evaluations show that MI-GAN performs comparable or, in some cases, better than recent state-of-the-art approaches. Moreover, we perform a user study comparing MI-GAN results with results from several commercial mobile inpainting applications, which clearly shows the advantage of MI-GAN in comparison to existing apps. With the purpose of high quality and efficient inpainting, we utilize an effective combination of adversarial training, model reparametrization, and knowledge distillation. Our models and code are publicly available at https://***/Picsart-AI-Research/MI-GAN.

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image rain removal algorithm based on conditional diffusion model 2

Image rain removal algorithm based on conditional diffusion ...

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Li, Yuxin Liu, Lijun Ma, Baoying Dalian Minzu University School of Science Dalian China

ISBN: (纸本)9798350331417

Rainfall image restoration is one of the research hotspots in the field of computer vision. For raindrops attached to the camera lens or glass, it will hinder the visualization of the complete image and also lead to image degradation. Most of the existing methods for converting raindrop-degraded images into clean images use generative adversarial neural networks (GANs) to solve this problem. At present, the denoising probability diffusion model has been widely used in various fields. Therefore, this paper proposes a raindrop image restoration algorithm based on conditional diffusion model, which improves the U-Net network used for noise prediction in diffusion model. The model is verified and evaluated on the raindrop removal dataset, and has strong generalization ability for raindrop image restoration in the real world. © 2023 ieee.

关键词： component Conditional diffusion model Raindrop Removal U-Net network

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Design and Analysis of Approximate Multiplier of Majority-Based Imprecise 4-2 Compressor for image processing 23

Design and Analysis of Approximate Multiplier of Majority-Ba...

引用

ieee 23rd international conference on Nanotechnology (NANO)

作者： Zhang, Yongqiang Chen, Xiaoyue Guo, Pixia Xie, Guangjun Hefei Univ Technol Sch Microelect Hefei 230009 Peoples R China

ISBN: (纸本)9798350333466

Approximate computing is an efficient way to lower energy consumption and design complexity. An approximate 4-2 compressor using majority gates is proposed by adding errors to the truth table of the exact compressor. It achieves a considerable improvement in terms of power-delay-product (PDP) and energy-delay-product (EDP). Then an 8x8 Dadda multiplier using the proposed inexact compressor is designed and gives great optimization in compressing partial products stage. Compared to the exact and previous multipliers, synthesis and simulation results indicate that the proposed multiplier realizes a 12% reduction in error rate (ER) together with reductions in PDP and EDP by up to 30% and 35%, respectively. Furthermore, image multiplication and image filtering algorithm are implemented to test the practicability of the proposed multiplier in fault-tolerant applications. The evaluation indicators also show that the designed multiplier obtains better-quality images.

关键词： Approximate computing 4-2 compressor majority gate multiplier image processing

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LIGHTWEIGHT DEEP DEBLURRING MODEL WITH DISCRIMINATIVE MULTI-SCALE FEATURE FUSION 30

LIGHTWEIGHT DEEP DEBLURRING MODEL WITH DISCRIMINATIVE MULTI-...

引用

30th ieee international conference on image processing (ICIP)

作者： Lv, Jiangtao Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728198354

Although existing learning-based deblurring methods achieve significant progress, these approaches tend to require lots of network parameters and huge computational costs, which limits their practical applications. Instead of pursuing larger deep models for boosting deblurring performance, we propose a lightweight deep convolutional neural network with lower computational costs and comparable restoration performance, which is based on a multi-scale framework with an encoder and decoder network architecture. Specifically, we present an effective depth-wise separable convolution block (DSCB) as the fundamental building block of our method to reduce the model complexity. In addition, to better utilize the features from different scales, we develop a simple yet effective discriminative multi-scale feature fusion (DMFF) module for achieving high-quality results. Experimental results on the benchmarks show that our method is about 10x smaller than the state-of-the-art deblurring methods, e.g. MPRNet [1], in terms of model parameters and FLOPs while achieving competitive performance. The training code and models are available at https://***/cslvjt/LightweightDeblur.

关键词： Single image deblurring lightweight network multi-scale feature fusion

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Single Underwater image Enhancement Based on Transmission Map Weighted Fusion and Adaptive Color Correction 2

Single Underwater Image Enhancement Based on Transmission Ma...

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Tang, Xudong Wu, Yajuan China West Normal University College of Computer Science Nanchong China

ISBN: (纸本)9798350331417

The complex underwater environment and the absorption and scattering of light in the water lead to color degradation and loss of detail during the underwater imaging process. To address these problems, we propose a single-shot underwater image enhancement method based on the transfer graph normalization weighted fusion method and adaptive color correction, using a weighted fusion technique to combine the improved transfer graph with a bilateral filtering algorithm, which aims to defog underwater images and improve their contrast. Furthermore, by calculating the relative standard deviation between the three color channels, a novel adaptive correction method is designed to solve the image distortion problem due to light propagation underwater. Experimental results show that this comprehensive method performs well in underwater image enhancement. © 2023 ieee.

关键词： color correction image enhancement underwater image weighted fusion

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Twin Branch Transformers-Based Re-Ranking for image Retrieval 21

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

引用

21st ieee international Symposium on Parallel and Distributed processing with applications, 13th ieee international conference on Big Data and Cloud Computing, 16th ieee international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Ziqi Wang, Wei Institute of Information Engineering Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen China

ISBN: (纸本)9798350329223

image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor;re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric);the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity. © 2023 ieee.

关键词： image retrieval

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Analysis and Study of Conventional and Advanced image Segmentation Techniques for Machine Learning-Based Leaf Disease Detection and Classification 4

Analysis and Study of Conventional and Advanced Image Segmen...

引用

4th ieee international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： John, Sreya Rose, P. J. Arul Leena FSH SRMIST KTR Dept of Computer Science Chennai India

ISBN: (纸本)9798350306118

The demand for agricultural crops is constantly rising throughout time. This increment in demand is coupled with many other challenging factors such as climate change, pollution, urbanization etc that affects the quality and quantity of the crop production which can even lead to an economic breakdown. Lately, many studies were done to put an end to the difficulties faced by the agricultural sector. The application of technology in this area has been a pivotal turning point in the conservation of agricultural crops. Consequently, many computer based automated systems were developed to ease the tasks of farmers. Among these innovations, the application of image processing and machine learning has emerged as a breakthrough. By enabling early detection of plant diseases, these tools significantly contribute to maintaining crop health. Segmentation is an important stage in image processing, that is critical in accurately extracting relevant information from images. In this paper, we delve into the significance and advancements of segmentation, highlighting its major role in various applications. Additionally, we have also done a comparative study based on few experiments and observations in the latest improvements of various segmentation techniques. © 2023 ieee.

关键词： Machine learning

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Research on images Recognition based on Deep Convolution Neural Network 8

Research on Images Recognition based on Deep Convolution Neu...

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8th international conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Chang, Xiaobo Zhao, Linlin Wang, Jinxiang Yanbian University College of Engineering Yanji China Yanbian University Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanji China College of Engineering Yanbian University Computer Science and Technology Yanji China

ISBN: (纸本)9798350302455

image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method. © 2023 ieee.

关键词： Convolution

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