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

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Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting 38

Semi-supervised TEE Segmentation via Interacting with SAM Eq...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Deng, Sen Feng, Yidan Lin, Haoneng Fan, Yiting Lee, Alex Pui-Wai Hu, Xiaowei Qin, Jing Hong Kong Polytech Ctr Smart Hlth Sch Nursing Hong Kong Peoples R China Shanghai Jiao Tong Univ Shanghai Chest Hosp Dept Cardiol Sch Med Shanghai 200030 Peoples R China Chinese Univ Hong Kong Div Cardiol Dept Med & Therapeut Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)1577358872

Semi-supervised learning (SSL) is a powerful tool to address the challenge of insufficient annotated data in medical segmentation problems. However, existing semi-supervised methods mainly rely on internal knowledge for pseudo labeling, which is biased due to the distribution mismatch between the highly imbalanced labeled and unlabeled data. Segmenting left atrial appendage (LAA) from transesophageal echocardiogram (TEE) images is a typical medical image segmentation task featured by scarcity of professional annotations and diverse data distributions, for which existing SSL models cannot achieve satisfactory performance. In this paper, we propose a novel strategy to mitigate the inherent challenge of distribution mismatch in SSL by, for the first time, incorporating a large foundation model (i. e. SAM in our implementation) into an SSL model to improve the quality of pseudo labels. We further propose a new self-reconstruction mechanism to generate both noise-resilient prompts to demonically improve SAM's generalization capability over TEE images and self-perturbations to stabilize the training process and reduce the impact of noisy labels. We conduct extensive experiments on an in-house TEE dataset;experimental results demonstrate that our method achieves better performance than state-of-the-art SSL models.

关键词： image enhancement

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Robot vision and Deep Learning for Automated Planogram Compliance in Retail 1

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4th International conference on Robotics, Computer vision and Intelligent Systems (ROBOvIS)

作者： Merabet, Adel Latha, Abhishek v. Kuzhippallil, Francis A. Rahimipour, Mohammad Rhinelander, Jason venkat, Ramesh St Marys Univ Div Engn Halifax NS B3N 3C3 Canada St Marys Univ Comp & Data Analyt Halifax NS B3N 3C3 Canada St Marys Univ Sobey Sch Business Halifax NS B3N 3C3 Canada

ISBN: (数字)9783031590573

ISBN: (纸本)9783031590566;9783031590573

In this paper, automated planogram compliance technique is proposed for retail applications. A mobile robot with camera vision capabilities provides the images of the products on shelves, which are processed to reconstruct an overall image of the shelves to be compared to the planogram. The image reconstruction includes image frames extraction from live video stream, images stitching and concatenation. Object detection, for the products, is achieved using a deep learning tool based on YOLOv5 model. dataset, for algorithm training and testing, is built to identify the products based on their image identification, number, and location on the shelf. A small scale of shelves with products is built and different cases of products on shelves are tested in a laboratory environment. It was found that YOLOv5 algorithm detects various products on shelves with a precision of 0.98, recall of 0.99, F-measure of 0.98, and clarification loss of 0.006.

关键词： Robot image Deep learning Object detection Planogram Retail

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Codebook vQ-vAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI

Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using...

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IEEE/CvF conference on Computer vision and Pattern Recognition (CvPR)

作者： Redekop, Ekaterina Pleasure, Mara Wang, Zichen Sarma, Karthik, v Kinnaird, Adam Speier, William Arnold, Corey W. Univ Calif Los Angeles Los Angeles CA 90095 USA Univ Alberta Edmonton AB Canada

ISBN: (纸本)9798350365474

Multiparametric magnetic resonance imaging (mpMRI) plays an essential role in prostate cancer diagnosis as it can noninvasively localize and grade lesions based on their suspicion of representing clinically significant prostate cancer (csPCa). With the development of deep learning, automatic solutions for csPCa detection based on mpMRI have been developed;however, mpMRI data introduces several difficulties, including data scarcity, heterogeneity in image quality across institutions, and missing modalities. This work addresses these difficulties by building a radiology-based foundational model for prostate cancer mpMRI. Foundation models are deep learning models pre-trained on a large-scale dataset and they have recently gained significant interest in computer vision and natural language applications. After pretraining, these models are often adapted for a variety of downstream tasks using smaller datasets from within the same domain. In this work, a large prostate multiparametric MRI (mpMRI) dataset was collected by combining data from our institution with two publicly available datasets. Joint modeling of all mpMRI modalities is essential for accurate prostate cancer diagnosis;however, some of these modalities may be missing. Using unsupervised learning, we pretrained modalityspecific vector quantized variational autoencoders (vQ-vAE) to form a radiology foundational model. The learned codebook from vQ-vAE was then used to train a multi-modal transformer to perform the diagnosis of clinically significant prostate cancer (csPCa). The proposed multimodal transformer models long-range dependencies between latent representations of input modalities and is augmented with modality-level dropout to increase the model robustness to incomplete modalities. Our framework outperforms previously published work and achieves an average AUC/sensitivity/specificity of 0.764/0.690/0.781. Our results show that pretraining on a larger dataset in combination with the power of transform

关键词： foundational model multimodal transformer prostate cancer radiology vQ-vAE

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Time Dependent image Generation of Plants from incomplete Sequences with CNN-Transformer 44th

Time Dependent Image Generation of Plants from Incomplete Se...

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44th DAGM German conference on Pattern Recognition (DAGM GCPR)

作者： Drees, Lukas Weber, Immanuel Russwurm, Marc Roscher, Ribana Tech Univ Munich Data Sci Earth Observat Munich Germany Univ Bonn Remote Sensing IGG Bonn Germany AB EX PLEdoc Essen Germany Ecole Polytech Fed Lausanne EPFL ECEO Lab Lausanne Switzerland

ISBN: (纸本)9783031167881;9783031167874

data imputation of incomplete image sequences is an essential prerequisite for analyzing and monitoring all development stages of plants in precision agriculture. For this purpose, we propose a conditional Wasserstein generative adversarial network TransGrow that combines convolutions for spatial modeling and a transformer for temporal modeling, enabling time-dependent image generation of above-ground plant phenotypes. Thereby, we achieve the following advantages over comparable data imputation approaches: (1) The model is conditioned by an incomplete image sequence of arbitrary length, the input time points, and the requested output time point, allowing multiple growth stages to be generated in a targeted manner;(2) By considering a stochastic component and generating a distribution for each point in time, the uncertainty in plant growth is considered and can be visualized;(3) Besides interpolation, also test-extrapolation can be performed to generate future plant growth stages. Experiments based on two datasets of different complexity levels are presented: Laboratory single plant sequences with Arabidopsis thaliana and agricultural drone image sequences showing crop mixtures. When comparing TransGrow to interpolation in image space, variational, and adversarial autoencoder, it demonstrates significant improvements in image quality, measured by multi-scale structural similarity, peak signal-to-noise ratio, and Frechet inception distance. To our knowledge, TransGrow is the first approach for time- and image-dependent, high-quality generation of plant images based on incomplete sequences.

关键词： data imputation Transformer Positional encoding image time series Conditional GAN image generation Plant growth modeling

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Multi-Source Domain Adaptation with incomplete Source Label Spaces 27

Multi-Source Domain Adaptation with Incomplete Source Label ...

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27th International conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Li, Keqiuyin Lu, Jie Zuo, Hua Zhang, Guangquan Australia Artificial Intelligence Institute University of Technology Sydney 81 Broadway Ultimo2007 Australia

Domain adaptation is a practicable tool in real world application where there exists data scarcity. Multi-source domain adaptation attracts increasing attention due to its ability to enrich transfer knowledge by combining information from multiple domains. However, knowledge transfer can trigger privacy concerns by accessing source data. In addition, multiple domains can have label heterogeneity problem. In this paper, to solve the mentioned problems, we conduct an incomplete multi-source domain adaptation (IMSDA) method which can address transfer learning with and without the access to source data. As far as we are aware, this is the first work handling source-free incomplete domain adaptation. To take the benefits of multiple sources, multi-task learning is adopted to learn a general source model which can perform on multiple domains. A data matching strategy with and without source data forcing target sample to source latent feature space is developed to combine with self-supervision to adapt source model to the target domain. Experiments on real-world datasets indicate the superiority of the proposed method. © 2023 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： domain adaptation image classification machine learning Transfer learning

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RAFaRe: Learning Robust and Accurate Non-parametric 3D Face reconstruction from Pseudo 2D&3D Pairs 37

RAFaRe: Learning Robust and Accurate Non-parametric 3D Face ...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Guo, Longwei Zhu, Hao Lu, Yuanxun Wu, Menghua Cao, Xun Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9781577358800

We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SvFR). While tremendous efforts have been devoted to parametric SvFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database;2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SvFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at https://***/zhuhao-nju/rafare.

关键词： image reconstruction

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Lens Calibration in Systems for Increasing Resolution by Subpixel Shifts in Optical Microscopy 3

Lens Calibration in Systems for Increasing Resolution by Sub...

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3rd IEEE International conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Andryushchenko, Ekaterina v. Guzhov, vladimir I. Khaidukov, Dmitry S. Novosibirsk State Technical University Department of Data Acquisition and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

Optical microscope image quality is directly dependent on the objective lenses selected. In this paper we investigate a method of improving image quality by applying subpixel shifts, i.e. displacements smaller than the resolving power of the objective lens. The increase in spatial resolution is achieved by a mathematical calculation based on the expression of the spectrum of discrete signals, which uses the apparatus of generalized functions. To produce high-resolution elements, it is required to divide the Fourier spectrum generated from a few subpixel shifted images by a coefficient termed the aperture function. The aperture function depends on the kind of lens and can be its characteristic stated in the microscope passport. This paper presents a technique for calibrating a low-resolution lens based on the analysis of images taken with higher quality lenses. After determining the aperture function, you can apply a low-resolution lens to obtain higher quality images similar to the original. Structurally, as the resolution of a micro lens increases, its field of view decreases. At the same time, lenses with lower magnification provide a wider field of view. from a manufacturing point of view, lenses with lower resolution are easier to build, allowing them to exhibit higher metrological performance. The paper also discusses an analytical expression for the image spectrum that can be used for image reconstruction in the field of optical microscopy. Application of the inverse Fourier transform to this expression allows to reconstruct the original image with improved resolution. © 2024 IEEE.

关键词： Inverse problems

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Edge Structure Learning via Low Rank Residuals for Robust image Classification 37

Edge Structure Learning via Low Rank Residuals for Robust Im...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Shen, Xiang-Jun Abhadiomhen, Stanley Ebhohimhen Yang, Yang Liu, Zhifeng Tian, Sirui JiangSu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Univ Nigeria Dept Comp Sci Nsukka Nigeria Nanjing Univ Sci & Technol Sch Elect & Opt Engn Dept Elect Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9781577358800

Traditional low-rank methods overlook residuals as corruptions, but we discovered that low-rank residuals actually keep image edges together with corrupt components. Therefore, filtering out such structural information could hamper the discriminative details in images, especially in heavy corruptions. In order to address this limitation, this paper proposes a novel method named ESL-LRR, which preserves image edges by finding image projections from low-rank residuals. Specifically, our approach is built in a manifold learning framework where residuals are regarded as another view of image data. Edge preserved image projections are then pursued using a dynamic affinity graph regularization to capture the more accurate similarity between residuals while suppressing the influence of corrupt ones. With this adaptive approach, the proposed method can also find image intrinsic low-rank representation, and much discriminative edge preserved projections. As a result, a new classification strategy is introduced, aligning both modalities to enhance accuracy. Experiments are conducted on several benchmark image datasets, including MNIST, LFW, and COIL100. The results show that the proposed method has clear advantages over compared state-of-the-art (SOTA) methods, such as Low-Rank Embedding (LRE), Low-Rank Preserving Projection via Graph Regularized reconstruction (LRPP GRR), and Feature Selective Projection (FSP) with more than 2% improvement, particularly in corrupted cases.

关键词： image classification

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Mining Landmark images for Scene reconstruction from Weakly Annotated video Collections 30th

Mining Landmark Images for Scene Reconstruction from Weakl...

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30th International conference on MultiMedia Modeling, MMM 2024

作者： Neuschmied, Helmut Bailer, Werner JOANNEUM RESEARCH – DIGITAL Graz Austria

ISBN: (纸本)9783031533013

Many XR productions require reconstructions of landmarks such as buildings or public spaces. Shooting content on demand is often not feasible, thus tapping into audiovisual archives for images and videos as input for reconstruction is a promising way. However, if annotated at all, videos in (broadcast) archives are annotated on item level, so that it is not known which frames contain the landmark of interest. We propose an approach to mine frames containing relevant content in order to train a fine-grained classifier that can then be applied to unlabeled data. To ensure the reproducibility of our results, we construct a weakly labelled video landmark dataset (WAvL) based on Google Landmarks v2. We show that our approach outperforms a state-of-the-art landmark recognition method in this weakly labeled input data setting on two large datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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A Low Illumination image Enhancement Algorithm Based on Adaptive Color Correction and Guided Filtering 5

A Low Illumination Image Enhancement Algorithm Based on Adap...

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2023 5th International conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Liu, Xinshu Wang, Shi Liu, Xiangju Yu, Jiawen Li, Jiao Yang, Jing School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350323313

A low illumination image enhancement algorithm based on adaptive color correction and guided filtering is proposed to address the issues of dim brightness and severe detail loss in low illumination images. Firstly, an adaptive color restoration algorithm with detail enhancement is used to process the color pixel values of the original image to obtain a color balanced image;Convert the color equalization image components from RGB to HSv, and enhance the H, S, and v components separately;Using the histogram equalization CLAHE algorithm to enhance the contrast of the H component;By reconstructing the improved fractional order differential operator of the first n coefficients, the S-component is convolved, and then the processed S-component is subjected to saturation adaptive nonlinear correction;The improved Retinex algorithm based on the joint weighting of bilateral filtering and guided filtering as a new center surround function is used to enhance the v component;Finally, the enhanced hue, saturation, and brightness are fused and converted into RGB to output the final result image. The experimental results show that the algorithm proposed in this paper has good advantages in various data indicators. Compared to other algorithms, this algorithm enhances the overall details of the image while ensuring color balance, thereby enhancing the overall visual effect. Both subjective and objective data analysis of the human eye show that the enhanced image has better visual effects than existing image enhancement algorithms. © 2023 IEEE.

关键词： image enhancement

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