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6th IEEE International conference on image Processing, Applications and Systems, IPAS 2025

作者： Singh, Sameesha Singh, Maneesha Centre for Health Innovations MRIIRS Faridabad India Railvision Europe Ltd. Castle Donington United Kingdom

ISBN: (纸本)9798331506520

Univariate time series is utilized in numerous scientific applications for a variety of purposes. They serve as the foundation for different statistical measurements and show how data evolves over time when viewed independently as real-time data series. Furthermore, binary data sets are frequently utilized in various fields, including health sciences, where each observation results in either a positive or negative outcome. The primary uses of time series analysis include: (a) Predicting future trends;(b) Determining characteristics like average, range, and standard deviation to comprehend the data's behavior;(c) Examining variations in means and variances among multiple time series using statistical methods like t-tests, F-tests, and Anova;and (d) Contrasting multiple time series to identify point-to-point discrepancies using different measures like root mean square error. This article suggests a straightforward yet successful method for comparing data from various time series that are not synchronized, have gaps in data, and require determining the error of the best match to ensure it falls within an acceptable range. We utilize this method for a challenging railway engineering issue to verify the consistency of laser and inertial sensor measurements within a specified error range. © 2025 IEEE.

关键词： data assimilation

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Neuro-Vis: Guided Complex image reconstruction from Brain Signals Using Multiple Semantic and Perceptual Controls

Neuro-Vis: Guided Complex Image Reconstruction from Brain Si...

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2024 International conference on Computing, Machine Learning and data Science, CMLDS 2024

作者： Balisacan, Gabriela M. Paulo, Anne Therese A. University of San Carlos Philippines

ISBN: (纸本)9798400716393

The externalization of the state of one's mind, which people refer to as "mind reading"in science fiction, is currently being realized through brain decoding research. This field of study aims to deepen our understanding of the human brain, which is among the least understood known biological structures, and to build better foundations for brain-computer interfaces. With the success of state-of-the-art latent diffusion models in image synthesis, a trend in recent studies is to map fMRI recordings to the image embedding space of these generative models. While this method significantly improved image reconstructions in terms of semantics, preserving perceptual features without losing semantic information remains challenging, especially with complex images of natural scenes. This research introduces Neuro-Vis, a novel fMRI-to-image pipeline based on Stable Diffusion that effectively integrates multiple semantic controls through predicted image embeddings and captions and multiple lightweight perceptual controls through predicted blurry initial images, depth maps, and color palettes. Neuro-Vis outperforms the current state-of-the-art methods in terms of consistency in low-level features while also rivaling them in terms of semantics. Furthermore, ablation experiments demonstrate the effectiveness of each component in Neuro-Vis for fMRI-to-image reconstruction. © 2024 ACM.

关键词： Decoding

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Learning Disentangled Representation for Multidimensional MR image reconstruction 46

Learning Disentangled Representation for Multidimensional MR...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhao, Ruiyang Wang, Zepeng Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States University of Illinois Urbana-Champaign Department of Bioengineering UrbanaIL United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL United States

ISBN: (纸本)9798350371499

We proposed a new way to represent and reconstruct multidimensional MR images. Specifically, a representation capable of disentangling different types of features in high-dimensional images was learned via training an autoencoder with separated sets of latent spaces for image style transfer, e.g., contrast or geometry transfer. A latent diffusion model was introduced to capture the distributions of the disentangled latents for constrained reconstruction. A new formulation was developed to integrate the pre-learned representation with other complementary constraints for reconstruction from sparse data. We demonstrated the ability of our model to disentangle contrast and geometry features in multicontrast MR images, and its effectiveness in accelerated T1 and T2 mapping. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Self-Supervised 3D Human Mesh Recovery from a Single image with Uncertainty-Aware Learning 38

Self-Supervised 3D Human Mesh Recovery from a Single Image w...

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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： Yan, Guoli Zhong, Zichun Hua, Jing Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)1577358872

Despite achieving impressive improvement in accuracy, most existing monocular 3D human mesh reconstruction methods require large-scale 2D/3D ground-truths for supervision, which limits their applications on unlabeled in-the-wild data that is ubiquitous. To alleviate the reliance on 2D/3D ground-truths, we present a self-supervised 3D human pose and shape reconstruction framework that relies only on self-consistency between intermediate representations of images and projected 2D predictions. Specifically, we extract 2D joints and depth maps from monocular images as proxy inputs, which provides complementary clues to infer accurate 3D human meshes. Furthermore, to reduce the impacts from noisy and ambiguous inputs while better concentrate on the high-quality information, we design an uncertainty-aware module to automatically learn the reliability of the inputs at body-joint level based on the consistency between 2D joints and depth map. Experiments on benchmark datasets show that our approach outperforms other state-of-the-art methods at similar supervision levels.

关键词： Mesh generation

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Identification of Burnt Rock Based on Hyperspectral Remote Sensing data, Taking Rujigou Coal Mine in Ningxia as an Example

Identification of Burnt Rock Based on Hyperspectral Remote S...

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2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Fan, Yuhai Coal Aerial Survey and Remote Sensing Bureau Shaanxi Xi’an710054 China Yulin University Shaanxi Yulin719000 China Yulin Tongda Mingtai Geological Technology Co. Ltd. Shaanxi 719000 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

The existence of burnt rock seriously affects the safety of coal mine production. In this paper, the airborne hyperspectral remote sensing image (CASI/SASI) was used as a main data resource in Rujigou Coalfield, Ningxia. Preprocesses, such as radiometric correction, radiometric calibration, atmospheric correction(spectral reconstruction) and geometric correction were carried out with ENVI5.1. Meanwhile, ASD FieldSpec® Pro FR portable spectrometer was used to test spectrum of all samples from burnt rocks, for extraction and analysis spectral characteristics. based on ground spectral data processing and comparative analysis, the Mixture Tuned Matched Filtering(MTMF) method was used to extract the boundry of the burnt rocks, and evevtually we got the distribution map of burnt rocks in the study area. After geological comprehensive analysis and field survey, the distribution of burning rocks in the field was basically consistent with the boundry extracted from hyperspectral remote sensing image. The result indicated that the CASI/SASI image had good application results and shew a promising potential in burnt rocks survey, which could provide a basic method for hyperspectral remote sensing prospecting in the same or similar unexplored areas. © 2025 SPIE.

关键词： Coal mines

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Robust structured light field depth estimation via spinning line operator

Robust structured light field depth estimation via spinning ...

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2024 International conference on Optical and Photonic Engineering, icOPEN 2024

作者： Guo, Wenfeng Wu, Qingyang Zou, Yifeng Shenzhen Technology University 3002 Lantian Road Pingshan District Shenzhen Guangdong China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Light fields can simultaneously capture the intensity and direction of each light ray, offering enhanced information for depth estimation. Currently, accurately extracting the epipolar plane lines and handling occlusion are the tough problems of the epipolar plane image depth estimation for light field. Passive depth estimation algorithms only relying on texture information from reconstructed object surfaces struggle to reconstruct texture-free areas, and exhibit poor noise resilience and low accuracy. To address these problems, this paper introduces structured light projection to add accurate and stable texture information, thereby improving the measurement accuracy and robustness, and proposes an epipolar plane line extraction algorithm suitable for structured light field data. This paper designs a spinning line operator based on Structured Light Field characteristics, and uses the variance of the points on the epipolar plane line as the cost of measuring the extraction accuracy. It judges and handles the occlusion problem by gradually removing abnormal points to make the variance converge. This paper measures and reconstructs objects such as standard blocks, standard balls, and portraits, and achieves satisfactory reconstruction results. © 2025 SPIE.

关键词： Structured light reconstruction algorithms Interpolation Cameras image compression Optical spheres Chaos image analysis Shadows Spatial resolution

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Dictionary Learning-Based image reconstruction for Terahertz Computed Tomography

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JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 2021年第8期42卷 829-842页

作者： Zhong, Fasheng Niu, Liting Wu, Weiwen Liu, Fenglin Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testi Minist Educ Chongqing 400044 Peoples R China Guangdong Univ Petrochem Technol Coll Sci Maoming Guangdong Peoples R China

Terahertz computed tomography (THz CT) demonstrates its advantages in aspects of nonmetallic and nonpolar materials penetration, 3D internal structure visualization, etc. To perform satisfied reconstruction results, it is necessary to obtain complete measurements from many different views. However, this process is time-consuming and we usually obtain incomplete projections for THz CT in practice, which generates artifacts in the final reconstructed images. To address this issue, dictionary learning-based THz CT reconstruction (DLTR) model is proposed in this study. Especially, the image patches are extracted from other state-of-the-art reconstructed images to train the initial dictionary by using the K-SVD algorithm. Then, the dictionary can be adaptively updated during THz CT reconstruction. Finally, the updated dictionary is used for further updating reconstructed images. In order to verify the accuracy and quality of DLTR method, the filtered back-projection (FBP), simultaneous algebraic reconstruction technique (SART), and total variation (TV) reconstruction are chosen as comparisons. The experiment results show that the DLTR method has a good capability for noise suppression and structures preservation.

关键词： Terahertz CT image reconstruction Dictionary learning incomplete data

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Panoramic image-Based Aerial Localization using Synthetic data via Photogrammetric reconstruction

Panoramic Image-Based Aerial Localization using Synthetic Da...

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IEEE/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Sufiyan, Danial Pheh, Ying Hong Win, Luke Soe Thura Win, Shane Kyi Hla Tan, U-Xuan Foong, Shaohui

ISBN: (纸本)9781665476331

To successfully adhere to flight plans, aerial vehicles must keep track of their location in 3D space, which is usually reliant on external references such as GNSS which are susceptible to interference. To develop self-reliant onboard positional localization, a workflow using 360-degree panoramic images in an image-based localization system using a Deep Convolutional Neural Network is proposed. 360-degree panoramic images have the advantage that they take into account visual information from all angles. Model performance is also enhanced by generating synthetic data from a 3D model of the region of interest created via photogrammetry techniques. The performances of different training configurations are compared, and the configuration with mixed real and synthetic data exhibits the highest performance, an approximately 10 to 15 percent improvement over using solely real data. Additional image augmentations also further reduce the localization error by 8 to 15 percent.

关键词： Photogrammetry

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Autoencoder-Based Anomaly Detection in ECG image Time Series data: A Comparative Evaluation of Three Different Architectures 14

Autoencoder-Based Anomaly Detection in ECG Image Time Series...

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14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chouhan, Sandeep Rudra, Bhawana National Institute of Technology Karnataka Department of Information Technology India

ISBN: (纸本)9798350335095

Anomaly detection is an essential component of machine learning that renders the outcomes neutral to any category or class. Due to the wide range of anomalies that might exist in time-series data, it plays a crucial role in time-series modelling. This research paper presents an image reconstruction-based approach for anomaly detection in electrocardiogram (ECG) time series data and image data using three different autoencoders, and concludes with a comparison analysis. Anomaly detection is crucial for accurate diagnosis and treatment of heart diseases, and the proposed approach utilizes three types of autoencoders to learn the normal patterns in image data and identify any deviations from these patterns as anomalies. The approach uses a dataset of normal images and is tested on a dataset containing both normal and anomalous images. To reconstruct unseen data like anomalies, models trained only on normal sets will not be able to do so. The results demonstrate that all three types of autoencoders can effectively reconstruct the images, but the Convolutional Autoencoder was able to reconstruct most of the anomaly images correctly, which may not be ideal for anomaly detection. On the other hand, the Deep Autoencoder and Stacked Autoencoder show the reconstruction of the anomalies similar to the normal images, hence, can be very effective for detecting anomalies in ECG image time-series data and similar problems. The proposed approach demonstrates the effectiveness of autoencoder-based anomaly detection for image data and provides a comparative analysis of different types of autoencoders for this task. This can potentially improve the accuracy and efficiency of anomaly detection in various image datasets. © 2023 IEEE.

关键词： Time series

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A COMPARISON THROUGH TREE EXTRACTION IN image-SPACE AND OBJECT-SPACE 6

A COMPARISON THROUGH TREE EXTRACTION IN IMAGE-SPACE AND OBJE...

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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： Amlashi, H. Haddadi Univ Tehran Fac Geospatial Engn Tehran Iran

In various studies trees have been extracted and their conditions have been examined through different detection algorithms from two main data sources including (a) point cloud and (b) raster data. The output of tree extraction is the input of the next processing steps, and the importance of these outputs is proved more than before. Tree Extraction (TE) has many applications in biomass estimation, CHM extraction, etc. All of which require high accuracy and the correct position of the trees. therefore, in this study, a comparison between tree extraction algorithms in two common sources of data has been conducted. As for the raster data, all bands are first co-registered. Afterward, the trees are separated from the background by using image processing techniques such as changing the image color space and weighted averaging on different bands. Finally, TE algorithms such as watershed segmentation, valley following, local maxima, and image binarization were applied. As for the point cloud data, TE can be conducted in the object space to compensate for the methods used in the raster space with object detection algorithms e.g., the coherence between the two trees, etc. which have been discussed in detail in this paper. In the object space, three algorithms, region-based, surface normal, and Euclidean segmentation, were implemented and discussed on the same raster data set in the photogrammetric point cloud. The results show the higher accuracy of the region-based algorithm in object-space by more than 26% in comparison with the valley following algorithm in image space.

关键词： Individual trees Photogrammetric point cloud UAV reconstruction image space Object space

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