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共 4633 条记录，以下是251-260 订阅

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SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 IEEE.

关键词： Remote sensing

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Feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection 4th

Feature Extraction and Dimensionality Reduction to Evaluate ...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. This paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. The hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. The results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. Therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： Computer-Aided Diagnosis Diabetic Retinopathy Machine learning Deep learning Ensemble learning image processing

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RGFormer: Residual Gated Transformer for image Captioning 23rd

RGFormer: Residual Gated Transformer for Image Captioning

引用

23rd Asia Simulation conference, AsiaSim 2024

作者： Jin, Zehui Chen, Kai Fang, Guoyu Tang, Dunbing Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819772247

image captioning is a cross-modal task that combines computer vision and natural language processing. The model is required to generate an appropriate caption for the given image. To address this challenge, we proposed a Residual Gated Transformer, RGFormer, as an enhancement of Transformer architecture. The model based on CLIP and RGFormer, CRM, is then proposed for image captioning. CRM utilizes the encoder of CLIP to extract image features as a prefix to the caption. The prefix is projected into language space using RGFormer, a lightweight mapping network, and then fed into GPT-2 to generate captions. CLIP was trained on an extensive dataset comprising image-text pairs, which contains rich visual and semantic information and is exceptionally well-suited for vision-language tasks. The core idea of CRM is to reduce the disparity between visual and textual representations by using RGFormer to accomplish the cross-modal task. CRM could generate meaningful captions for diverse and large-scale datasets in a short training time without additional annotations or pre-training. Quantitative evaluation experiments show that CRM achieves results comparable to some advanced models on the COCO Caption dataset more efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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The Potential Utility of image Descriptions: User Identity Linkage across Social Networks Based on MultiModal Self-Attention Fusion

The Potential Utility of Image Descriptions: User Identity L...

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IEEE International Performance, Computing, and communications conference (IPCCC)

作者： Li, Yunfeng Gou, Gaopeng Xiong, Gang Li, Zhen Cui, Mingxin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350302936

The task of user identity linkage across social networks aims to predict whether users from different social networks refer to the same person. This task plays a crucial role in cross-social network information dissemination and intelligent recommendations. However, existing user identity linkage tasks suffer from several challenges: 1) excessive reliance on social network topology, neglecting users' visual modality information;2) inadequate handling of noise in user feature data;and 3) ineffective fusion of users' multimodal information. To address these issues, we investigated a method that utilizes heterogeneous multimodal posts, including user-generated text, images, and check-in messages, to achieve user identity linkage across social networks. We innovatively leveraged a pre-trained model for image-to-text conversion to further explore users' image data and proposed an adversarial learning model based on the multimodal self-attention mechanism (AMSA). The AMSA model consists of four components: user feature extraction, user feature processing, user feature fusion, and adversarial learning. Specifically, AMSA initially employed advanced pre-trained models to extract features from multiple modalities of users, including images and text. Subsequently, it utilized multiple mechanisms, such as multi-head self-attention, to process data from each modality separately and then fused them into user representation vectors. Finally, AMSA employed adversarial learning to enhance the model's learning capacity and mitigate semantic disparities in user information across different platforms. We conducted model performance evaluations on publicly available datasets, and experimental results demonstrated the superiority of the proposed AMSA model.

关键词： User Identity Linkage Deep learning Multi-Modal Learning Self-Attention

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Fast detection of sulcal regions for classification of Alzheimer's disease and Mild Cognitive Impairment 14

Fast detection of sulcal regions for classification of Alzhe...

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14th IEEE International conference on Signal processing and communications (SPCOM)

作者： Dhere, Abhinav Vazhayil, Vikas Sivaswamy, Jayanthi Int Inst Informat Technol Hyderabad Ctr Visual Informat Technol Hyderabad India Natl Inst Mental Hlth & Neurosci Dept Neurosurg Bangalore Karnataka India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are neurogenerative impairments with similar symptoms and risk factors. Sulcal width and depth are known biomarkers for discriminating between AD and MCI. This paper presents a novel 2D image representation for a brain mesh surface, called a height map. The basic idea behind the height map is to represent the surface as a function of spherical coordinates of the mesh vertices. We present a method to derive a height map from a given neuroimage (MRI) and extract sulcal regions from the height map. We demonstrate the height map's utility for classifying a given neuroimage into healthy, MCI and AD classes. Two approaches for extracting sulcal regions are explored. The proposed method is computationally light, and obtaining sulcal regions from a brain surface mesh takes about 24 seconds on a standard Intel i5-7200 CPU. The proposed method achieves 76.1% accuracy, and 76.3% F1-score for healthy, MCI, AD classification on a publicly available dataset.

关键词： visualization Brain Magnetic resonance imaging conferences Signal processing image representation Biomarkers

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T2CI-GAN: Text to Compressed image Generation Using Generative Adversarial Network 7th

T2CI-GAN: Text to Compressed Image Generation Using Generati...

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7th International conference on Computer Vision and image processing, CVIP 2022

作者： Rajesh, Bulla Dusa, Nandakishore Javed, Mohammed Dubey, Shiv Ram Nagabhushan, P. Department of IT IIIT Allahabad U.P Prayagraj211015 India Department of CSE Vignan University A.P Guntur522213 India

ISBN: (纸本)9783031314162

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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OPTICS LENS DESIGN FOR PRIVACY-PRESERVING SCENE CAPTIONING 29

OPTICS LENS DESIGN FOR PRIVACY-PRESERVING SCENE CAPTIONING

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IEEE International conference on image processing (ICIP)

作者： Arguello, Paula Lopez, Jhon Hinojosa, Carlos Arguello, Henry Univ Ind Santander Dept Comp Sci Bucaramanga 680002 Colombia

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

image captioning is a challenging task that connects two major artificial intelligence fields: computer vision and natural language processing. image captioning models use traditional images to generate a natural language description of the scene. However, the scene could contain private information that we want to hide but still generate the captions. Inspired by the trend of jointly designing optics and algorithms, this paper addresses the problem of privacy-preserving scene captioning. Our approach promotes privacy preservation, by hiding the faces in the images, during the acquisition process with a designed refractive camera lens while extracting useful features to perform image captioning. The refractive lens and an image captioning deep network architecture are optimized end-to-end to generate descriptions directly from the blurred images. Simulations show that our privacy-preserving approach degrades private visual attributes (e.g., face detection fails with our distorted images) while achieving comparable captioning performance with traditional non-private methods on the COCO dataset.

关键词： image Captioning Privacy-preserving Lens Design Deep Optics Computational Optics

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Dynamic Template Update Mechanism for visual Object Tracking 8

Dynamic Template Update Mechanism for Visual Object Tracking

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

作者： Hu, YongQi Cai, ZhongWang Nie, ZiJun Yu, ZiZheng Cao, Ying Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9798350302455

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time. © 2023 IEEE.

关键词： Computer vision

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Proceedings - 2023 IEEE International conference on Multimedia and Expo, ICME 2023

Proceedings - 2023 IEEE International Conference on Multimed...

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2023 IEEE International conference on Multimedia and Expo, ICME 2023

ISBN: (纸本)9781665468916

The proceedings contain 475 papers. The topics discussed include: weight-based regularization for improving robustness in image classification;weakly supervised few-shot and zero-shot semantic segmentation with mean instance aware prompt learning;a retriever-reader framework with visual entity linking for knowledge-based visual question answering;2S-DFN: dual-semantic decoding fusion networks for fine-grained image recognition;Action-GPT: leveraging large-scale language models for improved and generalized action generation;protecting intellectual property of EEG-based model with watermarking;making adversarial attack imperceptible in frequency domain: a watermark-based framework;content-adaptive adversarial embedding for image steganography using deep reinforcement learning;a robust generative image steganography method based on guidance features in image synthesis;adversarial audio watermarking: embedding watermark into deep feature;deniable diffusion generative steganography;and sea surface object detection based on background dynamic perception and cross-layer semantic interaction.

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A Fine-Grained image Description Generation Method Based on Joint Objectives 18th

A Fine-Grained Image Description Generation Method Based on...

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18th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Zhang, Yifan Lin, Chunzhen Cao, Donglin Lin, Dazhen Artificial Intelligence Department Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China

ISBN: (纸本)9789819996360

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model’s performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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