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

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incomplete Multimodal Learning for Visual Acuity Prediction After Cataract Surgery Using Masked Self-Attention 26th

Incomplete Multimodal Learning for Visual Acuity Prediction ...

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26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhou, Qian Zou, Hua Jiang, Haifeng Wang, Yong Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Aier Eye Hosp Wuhan Peoples R China

ISBN: (纸本)9783031439896;9783031439902

As the primary treatment option for cataracts, it is estimated that millions of cataract surgeries are performed each year globally. Predicting the Best Corrected Visual Acuity (BCVA) in cataract patients is crucial before surgeries to avoid medical disputes. However, accurate prediction remains a challenge in clinical practice. Traditional methods based on patient characteristics and surgical parameters have limited accuracy and often underestimate postoperative visual acuity. In this paper, we propose a novel framework for predicting visual acuity after cataract surgery using masked self-attention. Especially different from existing methods, which are based on monomodal data, our proposed method takes preoperative images and patient demographic data as input to leverage multimodal information. Furthermore, we expand our method to a more complex and challenging clinical scenario, i.e., the incomplete multimodal data. Firstly, we apply efficient Transformers to extract modality-specific features. Then, an attentional fusion network is utilized to fuse the multimodal information. To address the modality-missing problem, an attention mask mechanism is proposed to improve the robustness. We evaluate our method on a collected dataset of 1960 patients who underwent cataract surgery and compare its performance with other state-of-the-art approaches. The results show that our proposed method outperforms other methods and achieves a mean absolute error of 0.122 logMAR. The percentages of the prediction errors within +/- 0.10 logMAR are 94.3%. Besides, extensive experiments are conducted to investigate the effectiveness of each component in predicting visual acuity. Codes will be available at https://***/liyiersan/MSA.

关键词： incomplete Multimodal Learning Visual Acuity Prediction Self-Attention

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Research on 3D point cloud registration algorithm based on FPFH and ColorICP

Research on 3D point cloud registration algorithm based on F...

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2024 International conference on Optical and Photonic Engineering, icOPEN 2024

作者： Pan, Junjie Zhang, Xu School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

With the rapid development of 3D reconstruction, point cloud registration technology, a key step in 3D data processing, has garnered significant attention. Existing 3D point cloud registration technologies face issues such as low matching rates in coarse registration, misidentification of feature points, lengthy registration times, and low registration accuracy. Consequently, an improved registration algorithm that combines Fast Point Feature Histogram (FPFH) for coarse registration and Colored Iterative Closest Point (ColorICP) for fine registration has been proposed. The process begins with pre-processing the point cloud through downsampling filtration;next, point cloud features are extracted using FPFH to achieve feature matching and obtain the initial transformation matrix;finally, ColorICP is used for fine point cloud registration. Experiments on the Stanford bunny dataset from the standard point cloud library and real-world point cloud data demonstrate that the proposed registration algorithm effectively utilizes both color and geometric features of the point cloud, achieving significant improvements in registration accuracy and duration compared to traditional registration algorithms. © 2025 SPIE.

关键词： Point clouds image registration Histograms Detection and tracking algorithms reconstruction algorithms Nondestructive evaluation Picosecond phenomena Cameras RGB color model Algorithm development

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Using Neural Networks to Classify Hyperspectral Signatures of Unresolved Resident Space Objects 30

Using Neural Networks to Classify Hyperspectral Signatures o...

引用

conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Cedillo, Luis R. Acosta, Kevin Velez-Reyes, Miguel DeBlasio, Dan Univ Texas El Paso Comp Sci Dept El Paso TX USA Univ Texas El Paso Elect & Comp Engn Dept El Paso TX USA Carnegie Mellon Univ Ray & Stephanie Lane Computat Biol Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781510673816;9781510673809

This work advances Space Situational Awareness (SSA) by analyzing ground-based hyper/multispectral images of Unresolved Resident Space Objects (URSO). Machine-learning models are constructed for satellite classification using unresolved spectral imagery. The study uses simulated data of observations of nine distinct satellites retrieved from the U.S. Space Force Unified data Library (UDL). The dataset consists of unresolved hyperspectral imagery of satellites in different poses collected with a slitless spectrograph imager. The slitless spectrograph allows the collection of the spectral signature of the URSO with a single focal plane array in the 630 nm to 980 nm spectral range. In practice, the expectation is to have more unlabeled than labeled samples. Thus, the proposed classifier leverages the concept of Semi-Supervised machine learning. The network architecture leverages image reconstruction via a Convolutional AutoEncoder (CAE) and Multi-layer Perceptron (MLP) for classification. The architecture consists of three submodels: an Encoder, a Decoder (the two traditional components of a CAE), and a separate MLP. The Decoder and MLP use the Encoder's low-dimensional representation of the samples as their input. The Encoder performs the task of creating the shared latent space via dimensionality reduction of the spectral data. The CAE (Encoder/Decoder) can be trained on labeled and unlabeled data, while the MLP requires labeled data for training. The newly developed classification models achieve a mean validation accuracy of 84% and a mean testing accuracy of 82%, utilizing 10-fold cross-validation. Additionally, the decoder achieves low image reconstruction error with a mean test error of 0.008, measured by Mean Squared Error, over the cross-validation folds. This network architecture demonstrates the ability to map slitless spectral data for accurate identification of RSO despite the challenges of using a limited-size and unbalanced dataset.

关键词： Space Situational Awareness Autoencoders Dimensionality Reduction image Classification Neural Networks Machine Learning Remote Sensing Unresolved Resident Space Objects

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ISAR-Diff: A High-Resolution ISAR Imaging Method Based on Conditional Diffusion Models

ISAR-Diff: A High-Resolution ISAR Imaging Method Based on Co...

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Signal, Information and data Processing (ICSIDP), IEEE International conference on

作者： Yue Wang Xueru Bai National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Due to low signal-to-noise ratio (SNR) and incomplete echoes, the performance of inverse synthetic aperture radar (ISAR) imaging tends to degrade rapidly. To address this issue, this paper proposes a high-resolution ISAR imaging method based on a conditional diffusion model, dubbed as ISAR-Diff. Specifically, an ISAR imaging diffusion model is trained. Then, the complex-valued ISAR image is then inferred using the diffusion model with a guidance function, conditional on complex-valued echoes. In particular, an ISAR imaging guidance function is designed to make ISAR-Diff applicable to different scenarios and to improve the reconstruction accuracy. Experimental results based on Monte Carlo simulation and measured data demonstrate the superiority of ISAR-Diff in low SNR and incomplete echo scenarios.

关键词： Monte Carlo methods Imaging Radar imaging Diffusion models Particle measurements data processing data models Inverse synthetic aperture radar image reconstruction Signal to noise ratio

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Unified data-Free Compression: Pruning and Quantization without Fine-Tuning

Unified Data-Free Compression: Pruning and Quantization with...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Bai, Shipeng Chen, Jun Shen, Xintian Qian, Yixuan Liu, Yong Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China

ISBN: (纸本)9798350307184

Structured pruning and quantization are promising approaches for reducing the inference time and memory footprint of neural networks. However, most existing methods require the original training dataset to fine-tune the model. This not only brings heavy resource consumption but also is not possible for applications with sensitive or proprietary data due to privacy and security concerns. Therefore, a few data-free methods are proposed to address this problem, but they perform data-free pruning and quantization separately, which does not explore the complementarity of pruning and quantization. In this paper, we propose a novel framework named Unified data-Free Compression(UDFC), which performs pruning and quantization simultaneously without any data and fine-tuning process. Specifically, UDFC starts with the assumption that the partial information of a damaged(e.g., pruned or quantized) channel can be preserved by a linear combination of other channels, and then derives the reconstruction form from the assumption to restore the information loss due to compression. Finally, we formulate the reconstruction error between the original network and its compressed network, and theoretically deduce the closed-form solution. We evaluate the UDFC on the large-scale image classification task and obtain significant improvements over various network architectures and compression methods. For example, we achieve a 20.54% accuracy improvement on imageNet dataset compared to SOTA method with 30% pruning ratio and 6- bit quantization on ResNet-34. Code will be available at here.

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Kernelless Blind Inverse Imaging for Flat Meta-Optics Camera 31

Kernelless Blind Inverse Imaging for Flat Meta-Optics Camera

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31st European Signal Processing conference, EUSIPCO 2023

作者： Pinilla, Samuel Xing, Wenzhu Rostami, Seyyed Reza Miri Katkovnik, Vladimir Shevkunov, Igor Fröch, Johannes E. Majumdar, Arka Egiazarian, Karen Science and Technology Facilities Council Harwell United Kingdom Tampere University Tampere Finland University of Washington SeattleWA United States

ISBN: (纸本)9789464593600

The race for color micro-cameras employing flat meta-optics instead of conventional refractive lenses has rapidly developed various end-to-end design frameworks. The meta-optics produce a specially designed spatial modulation of light wavefronts resulting in heavily blurred registered images. The optimal modulation is engineered to achieve advanced sharp imaging after computational data processing. The wavefront modulation and the image reconstruction are the fundamental micro-camera design problems with meta-optics. The popular convolution-based blurred image modeling (kernel-based) does not fit well with cameras with meta-optics. As a valuable alternative, we develop for image reconstruction the kernelless blind inverse imaging. This technique is based on a convolutional neural network. Its efficiency is demonstrated in the frame of the hardware-in-the-loop (HIL) joint optimization of meta-optics and image reconstruction software. The developed HIL setup allows us to overcome fundamental limitations of mismatch between theory-based and resultant experimental image formation problems of meta-optics. The resulting camera achieves high-quality full-color imaging for a 5 mm aperture optics with a focal length of 5 mm. We have observed a superior quality of the images captured by the developed hybrid meta-optical camera compared to the compound multi-lens optics of a commercial camera. © 2023 European Signal Processing conference, EUSIPCO. All rights reserved.

关键词： image reconstruction

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MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH imageS USING CONVOLUTIONAL NEURAL NETWORKS 6

MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONV...

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ISPRS GeoSpatial conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing conference (SMPR) / 4th Geospatial Information Research (GIResearch) conference

作者： Najaf, M. Arefi, H. Amirkolaee, H. Amini Farajelahi, B. Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran

Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth from a single image, which act as encoders and decoders. In this article, we use one of these networks, which has performed well for depth estimation, in order to extract height from aerial and satellite images. Our main goal is to investigate the performance of Google Earth satellite data in order to produce a digital elevation model. At first, we extracted the digital model of the target area using ISPRS benchmark data, then we did the same thing using Google Earth satellite images. The paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB Google Earth image. The results show the proper performance of Google Earth satellite images for height extraction. We achieved values of 2.07 m and 0.36 m for the RMS and REL metrics, respectively, which are very comparable and acceptable to the values of 2.04 m and 0.39 m obtained from the ISPRS benchmark images.

关键词： Depth Estimation Scene Recognition Convolutional Neural Network Height image Digital Elevation Model Google Earth image

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Deep Super Resolution Techniques for Remote Sensing Big data: A Comparative Study

Deep Super Resolution Techniques for Remote Sensing Big Data...

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2024 IEEE International conference on Big data, Bigdata 2024

作者： Jain, Rishabh Vatsavai, Ranga Raju North Carolina State University Dept. Computer Science RaleighNC United States

ISBN: (纸本)9798350362480

Recent advancements in remote sensing technology have led to vast collections of big image data, ranging from high-resolution (sub-meter) to medium-resolution (10 to 30 meters). While medium-resolution images are freely available, high-resolution imagery is costly. Several applications, such as object recognition and cloud imputation, necessitate super-resolving medium-resolution images to align with high-resolution data. Deep learning has significantly improved single-image super-resolution (SISR) accuracy. However, super-resolving at scales exceeding 5x (10 meters to 2 or 1 meter) remains challenging. Unlike natural photographs, remote sensing images require recovering intricate details for 25 pixels at a super-resolution of 2 meters from an aggregated single pixel at a medium resolution of 10 meters. This demands the ability to discern subtle geographic features, such as textures, variations in vegetation and land use changes, and small man-made structures. Existing SR techniques, primarily designed and tested on natural images, focus on establishing hidden pixel relationships by learning contextual constraints from high-resolution data and employing innovative loss functions during high-resolution image reconstruction through residual or similarity transformations. However, their effectiveness with geospatial data requires careful evaluation. To evaluate the effectiveness of various super-resolution (SR) techniques, this paper conducts a comparative study using a diverse dataset of over 16,530 high-resolution satellite images from Planet Explorer's remote sensing database, sourced from Sentinel-2 (10-meter) and RapidEye (3-meter) satellites. The performance of each method is assessed and compared using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. We hope the insights from this comparative study will help guide the selection of suitable SR methods by highlighting their advantages and disadvantages. © 2024 IEEE.

关键词： Pixels

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image-free Classifier Injection for Zero-Shot Classification

Image-free Classifier Injection for Zero-Shot Classification

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Christensen, Anders Mancini, Massimiliano Koepke, A. Sophia Winther, Ole Akata, Zeynep Univ Tubingen Tubingen Germany Tech Univ Denmark Lyngby Denmark Univ Trento Trento Italy Univ Copenhagen Copenhagen Denmark Copenhagen Univ Hosp Copenhagen Denmark FindZebra Copenhagen Denmark MPI Intelligent Syst Stuttgart Germany

ISBN: (纸本)9798350307184

Zero-shot learning models achieve remarkable results on image classification for samples from classes that were not seen during training. However, such models must be trained from scratch with specialised methods: therefore, access to a training dataset is required when the need for zero-shot classification arises. In this paper, we aim to equip pre-trained models with zero-shot classification capabilities without the use of image data. We achieve this with our proposed image-free Classifier Injection with Semantics (ICIS) that injects classifiers for new, unseen classes into pre-trained classification models in a post-hoc fashion without relying on image data. Instead, the existing classifier weights and simple class-wise descriptors, such as class names or attributes, are used. ICIS has two encoder-decoder networks that learn to reconstruct classifier weights from descriptors (and vice versa), exploiting (cross-)reconstruction and cosine losses to regularise the decoding process. Notably, ICIS can be cheaply trained and applied directly on top of pre-trained classification models. Experiments on benchmark ZSL datasets show that ICIS produces unseen classifier weights that achieve strong (generalised) zero-shot classification performance. Code is available at https://***/ExplainableML/imageFreeZSL.

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A Surface Defect Detection Method Based on image Guided Conditional Diffusion Model 43

A Surface Defect Detection Method Based on Image Guided Cond...

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43rd Chinese Control conference, CCC 2024

作者： Wang, Jianqi Zhu, Zhixiang Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an710121 China Xi'an University of Posts and Telecommunications School of Modem Post Xi'an710121 China

ISBN: (纸本)9789887581581

Surface defect detection aims to identify local image regions that significantly deviate from the normal appearance. In recent years, surface defect detection using generative models such as GANs and VAEs has shown promising results. However, due to the training instability of GANs and the suboptimal generation performance of VAEs, especially in scenarios with complex backgrounds and structures, they often exhibit coarse reconstruction of normal areas and introduce many false positives. Therefore, this paper proposes a defect detection model based on a classifier-guided conditional diffusion model. The approach for defect detection in this paper involves constructing an image generation network using a conditional diffusion model. The model takes defect images with class labels as conditional inputs to repair the defective regions in the input samples. The input samples and the repaired samples are simultaneously fed into the Improved Local Binary Pattern (ICLBP) network for local image contrast to detect defects and accurately identify the defective regions. During the model training phase, positive samples are inputted, and through a data augmentation module, images are classified and labeled. The defect generation module is then utilized to randomly generate defect images, making the training samples more representative of real-world scenarios. Experimental results on the DAGM 2007 dataset demonstrate that the proposed surface defect detection method achieves superior image reconstruction quality and higher detection accuracy when combined with Improved Local Binary Pattern (ICLBP). © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Surface defects

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