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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

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SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation

SNIDA: Unlocking Few-Shot Object Detection with Non-linear S...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yanjie Zou, Xu Yan, Luxin Zhong, Sheng Zhou, Jiahuan Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Natl Key Lab Multi Spectral Informat Intelligent Wuhan Peoples R China Peking Univ Wangxuan Inst Comp Technol Beijing 100871 Peoples R China

ISBN: (纸本)9798350353006

Once only a few-shot annotated samples are available, the performance of learning-based object detection would be heavily dropped. Many few-shot object detection ( FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless, those handcrafted enhancements often suffer from limited diversity and lack of semantic awareness, resulting in unsatisfactory performance. To this end, we propose a Semantic-guided Non-linear Instance-level data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely, masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods, we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and non-linear training data at the instance level through a semantic-guided masked autoencoder. In this way, the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms baselines by a large margin and achieves new state-of-the-art results under different shot settings.

关键词： data augmentation few-shot learning object detection

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How much does input data type impact final face model accuracy?

How much does input data type impact final face model accura...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Luo, Jiahao Khan, Fahim Hasan Mori, Issei de Silva, Akila Ruezga, Eric Sandoval Liu, Minghao Pang, Alex Davis, James Univ Calif Santa Cruz Santa Cruz CA 95064 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are underconstrained. As a result, reconstruction methods include embedded priors encoding the valid domain of faces. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? Does the input data need to be 3D, or will 2D data suffice? This paper takes a step toward answering these questions using synthetic data. A ground truth dataset is used to analyze accuracy obtainable from 2D landmarks, 3D landmarks, low quality 3D, high quality 3D, texture color, normals, dense 2D image data, and when regions of the face are missing. Since the data is synthetic it can be analyzed both with and without measurement error. This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs. The experimental results suggest that accuracy is severely limited when only 2D raw input data exists.

关键词： Solid modeling Measurement errors Three-dimensional displays image coding image analysis image color analysis Face recognition

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Automated Generation of Urban Medium-voltage Grids using OpenStreetMap data

Automated Generation of Urban Medium-voltage Grids using Ope...

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2024 IEEE PES Innovative Smart Grid Technologies Europe conference, ISGT EUROPE 2024

作者： Gebhard, Tobias Tundis, Andrea Steinke, Florian Darmstadt Germany Technische Universität Darmstadt Energy Information Networks & Systems Darmstadt Germany

ISBN: (纸本)9789531842976

Realistic geo-referenced electrical distribution grid (DG) models are of great importance for power system analysis and resilience studies. However, DG data are usually not publicly available. In this study, we develop a new process for the automated generation of medium-voltage (MV) grid topologies, specifically for urban areas, based on openly available data and open-source software. OpenStreetMap (OSM) data on power infrastructure, street layouts, and land use, are used as the only input source. In contrast to previous works on DG reconstruction, we use available OSM data on substation locations. Different existing methods are combined in a new, hybrid approach by considering the incompleteness of OSM data, taking the street network into account, and applying the Capacitated vehicle routing problem (CVRP) to find cost-optimal routes for power lines. Our method is tested with a German city as a case study. Furthermore, we verify the result using land use data and evaluate the quality of power-related OSM data. The results demonstrate that our approach can yield realistic geo-referenced MV grid topologies, even with incomplete OSM power data. © 2024 IEEE.

关键词： Power distribution lines

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SSTHyper: Sparse Spectral Transformer for Hyperspectral image reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image reconstruction

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GTA-HDR: A Large-Scale Synthetic dataset for HDR image reconstruction

GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Recon...

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2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Barua, Hrishav Bakul Stefanov, Kalin Wong, KokSheik Dhall, Abhinav Krishnasamy, Ganesh Monash University Australia Tcs Research India

ISBN: (纸本)9798331510831

High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community. A major challenge is the lack of datasets, which capture diverse scene conditions (e.g., lighting, weather, locations) and various image features (e.g., color, contrast, saturation). To address this gap, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic HDR images sampled from the GTA-V video game. We perform thorough evaluation of the proposed dataset, which enables significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. Furthermore, we demonstrate the effectiveness of the proposed dataset and its impact on additional computer vision tasks including 3D human pose estimation, human body part segmentation, and holistic scene segmentation. The dataset, data collection pipeline, and evaluation code are available at: https://***/HrishavBakulBarua/GTA-HDR. © 2025 IEEE.

关键词： Deep learning

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Enhancing Archeological Building reconstruction Through image-Based 3D Modeling and LoFTR 4

Enhancing Archeological Building Reconstruction Through Imag...

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4th IEEE International conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Joshi, Hemant Shakya, Subarna Institute of Engineering Tribhuvan University Pulchowk Campus Department of Electronics and Computer Engineering Kathmandu Nepal

ISBN: (纸本)9798350306118

Archaeological research often relies on the meticulous reconstruction of historical structures and artifacts. However, this process is frequently hindered by the absence of detailed 3D data and the limited availability of well-preserved sites. In response to these challenges, this thesis tries to contribute in rendering the 3D structure of archaeological buildings from 2D unstructured image. In case of damage to the original buildings, its 3D model can be helpful for replication. The images from the internet are taken to train the model using Structure from Motion (SfM) based algorithms. A model named Detector-Free Local Feature Matching with Transformers (LoFTR) is utilized for local image feature matching. Ultimately, the feature points are adjusted to generate a 3D representation of the building. The key stages of structure from motion encompass feature detection and matching, outlier removal, camera pose estimation, point cloud creation, and 3D mesh generation. This reformulation method can reduce the expense and intricacy associated with traditional approaches. © 2023 IEEE.

关键词： 3D modeling

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Neural Style Transfer of Text Supervision 2

Neural Style Transfer of Text Supervision

引用

2nd International conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Xie, Bin Shao, Xiang College of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9798350371451

Style transfer technology has been widely applied in the field of image processing. Most of the current style transfer methods obtain style information from a single mode. This will cause the model to lose or incomplete information during style transfer, which will affect the visual effect of the resulting image. Multimodal technology is a method that combines the information of different modes together to realize a more comprehensive understanding and processing of data. It has the advantages of rich information expression, cross-modal interconnection, complementing and complementing each other, which provides a new idea for style transfer method. Based on CNN, this paper proposes a style transfer method using text information as constraint. On the one hand, the style information provided by the text makes up for the deficiency of the visual modal information, which helps the model to learn more style information. On the other hand, the semantic structure and spatial relationship provided by the text playa constraining role in maintaining the semantic structure of the content image. The experiment shows that compared with the traditional style transfer method, the style transfer method with text constraint can solve the problem of information loss and incomplete by using a single mode style transfer, and get better experimental results. © 2023 IEEE.

关键词： image processing

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GPR image Retrieval and Localization Based on Spatial Prior Constraints 20

GPR Image Retrieval and Localization Based on Spatial Prior ...

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20th International conference on Ground Penetrating Radar, GPR, 2024

作者： Bi, Beizhen Chen, Yuwei Shen, Liang Zhang, Pengyu Huang, Xiaotao Jin, Tian College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410073 China Test Center National University of Defense Technology Xi'an710106 China

Localizing Ground Penetrating Radar (LGPR) offers the distinct advantage of being unaffected by weather and light conditions changes. As a novel auxiliary driving localizing system, LGPR enhances the robustness of the auto-drive system. Traditional ground penetrating radar localizing technologies rely heavily on high-quality map data, exhibiting optimal performance in environments with complete, high-precision maps. However, deviations from the mapped trajectory can compromise localization accuracy. We introduce a novel approach: a ground penetrating radar image retrieval and localization method based on spatial prior constraints of array antennas to address this challenge. This method aims to mitigate mismatches in ground penetrating radar navigation and localization encountered under incomplete map coverage. We performed a comparative analysis with the LGPR baseline and then validated them experimentally on our dataset. The results show that the proposed method achieved significant enhancement in accuracy. In particular, the superiority of the proposed method is demonstrated in low overlap GPR image matching and localisation. © Published under licence by IOP Publishing Ltd.

关键词： image enhancement

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Generative data Augmentation of Human Biomechanics 22nd

Generative Data Augmentation of Human Biomechanics

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22nd International conference on image Analysis and Processing (ICIAP)

作者： Karason, Halldor Ritrovato, Pierluigi Maffulli, Nicola Tortorella, Francesco Univ Salerno Dept Informat & Elect Engn & Appl Math Salerno Italy Univ Salerno Dept Med Surg & Dent Salerno Italy

ISBN: (纸本)9783031510229;9783031510236

Wearable sensors are miniature and affordable devices used for monitoring human motion in daily life. data-driven models applied to wearable sensor data can enhance the accuracy of movement analysis outside of controlled settings. However, obtaining a large and representative database for training these models is challenging due to the specialised motion laboratories and expensive equipment required. To address this limitation, this study proposes a data augmentation approach using generative deep learning to enhance biomechanical datasets. A novel conditional generative adversarial network (GAN) was developed to synthesise biomechanical data during gait. The GAN takes into account the subject's anthropometric measures to generate data that represents specific body types as well as information about the gait cycle for reconstruction back into the time domain. The proposed model was evaluated for generating biomechanical data of unseen subjects and fine-tuning the model with small percentages (1%, 2% and 5%) of the test dataset. Researchers and practitioners can overcome the limitations of obtaining large training datasets from human participants by synthesising realistic and diverse synthetic data. This paper outlines the methodology and experimental setup for developing and evaluating the GAN and discusses its potential impact on the field of biomechanics and human motion analysis.

关键词： biomechanics deep learning wearable sensors generative adversarial networks

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Automatic detection of exam contents from a 3D image series

Automatic detection of exam contents from a 3D image series

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conference on Medical Imaging - image Processing

作者： Dutta, Sandeep Li, Yunfeng Bonnard, Maud Hsieh, Jiang GE HealthCare 3000 Grandview Blvd Waukesha WI 53188 USA

ISBN: (纸本)9781510671577;9781510671560

Radiology workflow automation requires knowledge of exam contents in an image series such as anatomy region, injected contrast phase, presence of metals, so that appropriate post-processing steps and analysis can be invoked automatically. This paper investigates the applicability of DL to the task of classifying an entire image series into one of fourteen common exam types. A total of 2300 independent computed tomography (CT) image series, each manually labeled for its exam category by clinical experts, was used to train DL models. An additional 593 series were labeled and used as an independent test set. Each CT image series containing a 3D volume acquisition is converted to a special 2D multiplanar-reconstruction (MPR) image. DL based classifier was trained to classify the image series based on this 2D representation, which could be an AP view, a Lateral view or both. Different convolutional neural network architectures with varying block depths were compared. Global average pooling (GAP) layer was used in the final classification block and the impact of input view was studied. The impact of depth of feature extraction layer, input image type, data augmentation techniques and learning rates were studied. The best single class prediction accuracy achieved was 97%. The top-two classes classification accuracy reached > 99%. This method avoids the cost of inferencing each image in a 3D series but still provides very high classification accuracy.

关键词： Medical Imaging Computed Tomography Deep Learning (DL) Convolutional Neural Network Classification Global Average Pooling

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