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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241-250 订阅

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Efficient Approximate Vedic Multiplier: Design, Analysis, and Application in image Blending 2

Efficient Approximate Vedic Multiplier: Design, Analysis, an...

引用

2nd ieee international conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Gupta, Sanjiv Kumar Yadav, Nilesh Kumar Dhawan, Amit Tiwari, Manish Jha, Sumit Kumar Motilal Nehru National Institute of Technology Department of Electronics and Communication Engineering Uttar Pradesh Allahabad211004 India

ISBN: (纸本)9798350305142

Approximate computing has become a widely recognized method for designing energy-efficient arithmetic architectures in the context of error-tolerant applications. This paper presents the design and analysis of a 4-bit approximate Vedic multiplier (AVMT) using the Urdhva Tiryagbhyam method. This Vedic approach, involving vertical and crosswise steps, outperforms traditional multiplication in terms of efficiency. An approximate 2-bit multiplier (AVM2) is designed, and an AVMT is proposed using AVM2. The proposed architecture has better propagation delay and less area utilization compared to other conventional multipliers. AVMT has an 11% reduction in area consumption and a 12% increase in processing speed compared to the exact Vedic multiplier. To assess its practicality in real-world scenarios, the proposed multiplier is integrated into an image-blending application. The results indicate that the system achieves a Structural Similarity Index (SSIM) average value of 0.91, which proves to be suitable for error-resilient image processing applications. © 2023 ieee.

关键词： Energy efficiency

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image Completion with Heterogeneously Filtered Spectral Hints 23

Image Completion with Heterogeneously Filtered Spectral Hint...

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23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Xu, Xingqian Navasardyan, Shant Tadevosyan, Vahram Sargsyan, Andranik Mu, Yadong Shi, Humphrey SHI Lab UIUC Urbana IL 61801 USA UO Urbana IL 61801 USA Peking Univ Beijing Peoples R China Picsart AI Res PAIR Beijing Peoples R China

ISBN: (纸本)9781665493468

image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawbacks such as pattern unawareness, blurry textures, and structure distortion remain noticeable, and thus leave space for improvement. To overcome these challenges, we propose a new StyleGAN-based image completion network, Spectral Hint GAN (SH-GAN), inside which a carefully designed spectral processing module, Spectral Hint Unit, is introduced. We also propose two novel 2D spectral processing strategies, Heterogeneous Filtering and Gaussian Split that well-fit modern deep learning models and may further be extended to other tasks. From our inclusive experiments, we demonstrate that our model can reach FID scores of 3.4134 and 7.0277 on the benchmark datasets FFHQ and Places2, and therefore outperforms prior works and reaches a new state-of-the-art. We also prove the effectiveness of our design via ablation studies, from which one may notice that the aforementioned challenges, i.e. pattern unawareness, blurry textures, and structure distortion, can be noticeably resolved. Our code will be open-sourced at: https://***/SHI-Labs/SH-GAN.

关键词： Deep learning computer vision image resolution Codes Filtering Transforms Benchmark testing

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Visualization Analysis and XGBoost-Based Prediction of COVID-19 Mortality

Visualization Analysis and XGBoost-Based Prediction of COVID...

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image processing and computer applications (icipca), ieee international conference on

作者： Zixin Chen Chujing Zhang Ngating Chun School of Biomedical Engineering Southern University of Science and Technology Shenzhen Guangdong China School of International Pharmaceutical Business China Pharmaceutical University Nanjing Jiangsu China School of Software Engineering Shandong University Jinan Shandong China

For the best predictive results of novel coronavirus infection and COVID-19 mortality, this research bases on the XGBoost machine learning algorithm. Through the research of data on related diseases, it not only helps to prevent the infection of COVID-19 effectively, but also gives advice to specific patients who need treatment without delay. The existing machine learning model uses the logistic regression algorithm to train the uneven data but gets an unsatisfying precision. This research improves the result by combining undersampling with XGBoost, with random forest, logistic regression, decision tree, and other models as comparisons. The prediction of COVID-19 mortality has the same accuracy of 91% before and after using the undersampling method, however, the AUC rises about 13% and finally reaches 92%• This research is available for the prediction of prevalence rate and death rate of COVID-19 in people who have basic diseases.

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A Hybrid Approach for Spatial Information Extraction from Natural Language Text 20

A Hybrid Approach for Spatial Information Extraction from Na...

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20th ACS/ieee international conference on computer Systems and applications (AICCSA)

作者： Hassini, Nesrine Mahmoudi, Khaoula Faiz, Sami Univ Carthage Natl Inst Appl Sci & Technol INSAT LTSIRS Lab Carthage Tunisia Tunis El Manar Univ Tunis El Manar Preparatory Engn Inst IPEIEM Tunis Tunisia Univ Carthage Nabeul Fac Econ & Management Nabeul FSEGN Carthage Tunisia Univ Manouba Higher Inst Multimedia Arts Manouba ISAMM Manouba Tunisia

ISBN: (纸本)9798350319439

Commonly spatial data are stored in the Geographic Database (GDB) which is the backbone of Geographic information System (GIS). Setting up such database is a tedious and cost task. Moreover, the overwhelming amount of spatial information, particularly in the textual data is growing continuously. Hence, efforts were devoted to extract such data convoyed in the text streams freely available. In this context, we address this issue by proposing a novel hybrid approach that combines several Natural Language processing (NLP) techniques, rules, and gazetteers to extract spatial named entities and retrieve relationships amongst them.

关键词： Geographic entity Geographic relation Natural language processing Toponym disambiguation Shortest dependency path

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Hierarchical Spatiotemporal Transformers for Video Object Segmentation

Hierarchical Spatiotemporal Transformers for Video Object Se...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Yoo, Jun-Sang Lee, Hongjae Jung, Seung-Won Korea Univ Seoul South Korea

ISBN: (纸本)9798350307443

This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their inductive bias for the spatiotemporal locality, which is essential for temporally coherent VOS. To take full advantage of the image and video features, HST casts image and video features as a query and memory, respectively. By applying efficient memory read operations at multiple scales, HST produces hierarchical features for the precise reconstruction of object masks. HST shows effectiveness and robustness in handling challenging scenarios with occluded and fast-moving objects under cluttered backgrounds. In particular, HST-B outperforms the state-of-the-art competitors on multiple popular benchmarks, i.e., YouTube-VOS (85.0%), DAVIS 2017 (85.9%), and DAVIS 2016 (94.0%).

关键词： Deep learning image processing Transformer Video object segmentation

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PREDICTIVE CODING FOR ANIMATION-BASED VIDEO COMPRESSION 30

PREDICTIVE CODING FOR ANIMATION-BASED VIDEO COMPRESSION

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30th ieee international conference on image processing (ICIP)

作者： Konuko, Goluck Lathuiliere, Stephane Valenzise, Giuseppe Univ Paris Saclay Cent Supelec Lab Signaux & Syst Paris France Inst Polytech Paris Telecom Paris LTCI Paris France

ISBN: (纸本)9781728198354

We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e., each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a dataset of talking-head videos.

关键词： Video compression image animation generative models video conferencing predictive coding

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A data analysis and processing approach for a POI recommendation system 20

A data analysis and processing approach for a POI recommenda...

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20th ACS/ieee international conference on computer Systems and applications (AICCSA)

作者： Kouahla, Med Nadjib Boughida, Adil Boughazi, Akram Univ 08 May 1945 Guelma Dept Comp Sci LabSTIC Lab Guelma Algeria Univ 08 May 1945 Guelma Dept Comp Sci Guelma Algeria

ISBN: (纸本)9798350319439

we focused on exploring novel methods and approaches in data analysis and processing for recommendation systems. To build our recommendation system, we established models for points of interest (POIs) and users. Our solution incorporated three key factors: sentiment analysis, user preferences, and ratings, culminating in the integration of the LightGCN model. The sentiment analysis factor played a crucial role in analyzing user reviews and predicting ratings. The user preference factor enabled us to recommend the most suitable POIs based on individual preferences and interests. The culmination of these factors, along with the preprocessing, filtering, and modelling of the POIs, led to the integration of the LightGCN model. During the experimentation phase, we utilized Yelp datasets to preprocess, filter, and model the POIs, incorporating sentiment analysis of reviews. The recommendation system, developed in the same environment, utilized the combined results of the three factors and the LightGCN model to provide improved POI recommendations for users.

关键词： Recommendation Data Analysis and processing Point Of interest (POI) preference LightGCN Sentiment analysis

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SINGLE-STAGE HEAVY-TAILED FOOD CLASSIFICATION 30

SINGLE-STAGE HEAVY-TAILED FOOD CLASSIFICATION

引用

30th ieee international conference on image processing (ICIP)

作者： He, Jiangpeng Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781728198354

Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images. However, there are two major obstacles to apply food classification in real life applications. First, real life food images are usually heavy-tailed distributed, resulting in severe class-imbalance issue. Second, it is challenging to train a single-stage (i.e. end-to-end) framework under heavy-tailed data distribution, which cause the over-predictions towards head classes with rich instances and under-predictions towards tail classes with rare instance. In this work, we address both issues by introducing a novel single-stage heavy-tailed food classification framework. Our method is evaluated on two heavy-tailed food benchmark datasets, Food101-LT and VFN-LT, and achieves the best performance compared to existing work with over 5% improvements for top-1 accuracy.

关键词： Food classification Heavy-tailed distribution Single-stage image-based dietary assessment

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A Latent Space of Stochastic Diffusion Models for Zero-Shot image Editing and Guidance

A Latent Space of Stochastic Diffusion Models for Zero-Shot ...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Wu, Chen Henry De la Torre, Fernando Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9798350307184

Diffusion models generate images by iterative denoising. Recent work has shown that by making the denoising process deterministic, one can encode real images into latent codes of the same size, which can be used for image editing. This paper explores the possibility of defining a latent space even when the denoising process remains stochastic. Recall that, in stochastic diffusion models, Gaussian noises are added in each denoising step, and we can concatenate all the noises to form a latent code. This results in a latent space of much higher dimensionality than the original image. We demonstrate that this latent space of stochastic diffusion models can be used in the same way as that of deterministic diffusion models in two applications. First, we propose CycleDiffusion, a method for zero-shot and unpaired image editing using stochastic diffusion models, which improves the performance over its deterministic counterpart. Second, we demonstrate unified, plug-and-play guidance in the latent spaces of deterministic and stochastic diffusion models.(1)

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Learning Interpretable Forensic Representations via Local Window Modulation

Learning Interpretable Forensic Representations via Local Wi...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Das, Sowmen Amin, Md. Ruhul Univ Cambridge Cambridge England Fordham Univ Bronx NY 10458 USA

ISBN: (纸本)9798350307443

The majority of existing image forgeries involve augmenting a specific region of the source image which leaves detectable artifacts and forensic traces. These distinguishing features are mostly found in and around the local neighborhood of the manipulated pixels. However, patch-based detection approaches quickly become intractable due to inefficient computation and low robustness. In this work, we investigate how to effectively learn these forensic representations using local window-based attention techniques. We propose Forensic Modulation Network (ForMoNet) that uses focal modulation and gated attention layers to automatically identify the long and short-range context for any query pixel. Furthermore, the network is more interpretable and computationally efficient than standard self-attention, which is critical for real-world applications. Our evaluation of various benchmarks shows that ForMoNet outperforms existing transformer-based forensic networks by 6% to 11% on different forgeries.

关键词： Attention Deepfake image Manipulation image Segmentation Interpretability Media Forensics Transformer

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