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Autocalibration for Creating 2D/3D Displays on Complex Geometric Surfaces

Information Display 2022年第6期38卷 31-37页

作者： Arnold, Katherine Fieguth, Paul Dennison, Andrew Vision and Image Processing Lab University of Waterloo Canada Systems Design Engineering Department University of Waterloo Canada Software Solutions Development Department Christie Digital Systems

Geometric calibration methods tend to rely on precalibrated cameras or real calibration targets. Here we explore the chal-lenge of achieving a sufficiently accurate projector system calibration for complex projection mapping tasks that can rely on neither precalibrated cameras nor calibration targets. Virtual scene models can take the place of a real calibration target for the purpose of providing scene information that is used to refine geometric estimates. Refinement of the display parameters aims to reduce the reprojection error, or the error between the theoretical coordinate (generated from the projection of the scene estimate coordinate by the estimated parameters) and the actual coordinate (obtained from pixel cor-respondences). For Christie installations, the final reprojection error of each pixel should be less than 1 pixel. Mystique was not always so capable. Christie’s research department, Christie labs, has a history of creating solutions for new projection concepts. Part of that strategy has been a long partnership with the University of Waterloo, particularly the vision and image processing (VIP) lab in systems design engineering. Graduate students study computer vision and machine learning—topics related to problems often at the heart of techniques—which can be used to bring new ideas, meth-ods, and solutions to Mystique and, by extension, to Christie’s customers. Years of investment in research and development, including partnership with the VIP lab and others, have enabled Mystique to handle challenges beyond any other projec-tor-alignment solution. ID. © 2022, John Wiley and Sons Inc. All rights reserved.

关键词： Pixels

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Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging

arXiv

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arXiv 2025年

作者： Dewbury, Jarett Tai, Chi-En Amy Wong, Alexander Vision and Image Processing Group Systems Design Engineering University of Waterloo Canada

Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. In this paper, we explore the potential of enhancing PCa lesion segmentation using a novel MRI modality called synthetic correlated diffusion imaging (CDIs). We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDIs patient cohort. We find that SegResNet achieved superior segmentation performance with a Dice-Sørensen coefficient (DSC) of 76.68 ± 0.8. Notably, the Attention U-Net, while slightly less accurate (DSC 74.82 ± 2.0), offered a favorable balance between accuracy and computational efficiency. Our findings demonstrate the potential of deep learning models in improving PCa lesion segmentation using CDIs to enhance PCa management and clinical support. © 2025, CC BY.

关键词： Urology

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Particle-Filtering-based Latent Diffusion for Inverse Problems

arXiv

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arXiv 2024年

作者： Nazemi, Amir Sepanj, Mohammad Hadi Pellegrino, Nicholas Czarnecki, Chris Fieguth, Paul Vision & Image Processing Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling. However, almost all approaches make no explicit attempt to explore the solution space, instead drawing only a single sample from a Gaussian distribution from which to generate their solution. In this paper, we introduce a particle-filtering-based framework for a nonlinear exploration of the solution space in the initial stages of reverse SDE methods. Our proposed particle-filtering-based latent diffusion (PFLD) method and proposed problem formulation and framework can be applied to any diffusion-based solution for linear or nonlinear inverse problems. Our experimental results show that PFLD outperforms the SoTA solver PSLD on the FFHQ-1K and imageNet-1K datasets on inverse problem tasks of super resolution, Gaussian debluring and inpainting. © 2024, CC BY-NC-ND.

关键词： Wiener filtering

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A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation

arXiv

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arXiv 2024年

作者： Huang, Yuxiang Zelek, John Vision and Image Processing Lab System Design Engineering University of Waterloo Canada

Motion segmentation is a fundamental problem in computer vision and is crucial in various appications such as robotics, autonomous driving and action recognition. Recently, spectral clustering based methods have shown impressive results on motion segmentation in dynamic environments. These methods perform spectral clustering on motion affinity matrices to cluster objects or point trajectories in the scene into different motion groups. However, existing methods often need the number of motions present in the scene to be known, which significantly reduces their practicality. In this paper, we propose a unified model selection technique to automatically infer the number of motion groups for spectral clustering based motion segmentation methods by combining different existing model selection techniques together. We evaluate our method on the KT3DMoSeg dataset and achieve com-petitve results comparing to the baseline where the number of clusters is given as ground truth information. y © 2024, CC BY.

关键词： Clustering algorithms

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Memory-Efficient Continual Learning Object Segmentation for Long Videos

arXiv

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arXiv 2023年

作者： Nazemi, Amir Shafiee, Mohammad Javad Gharaee, Zahra Fieguth, Paul Vision & Image Processing Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from preceding frames is used in segmenting the current frame. In particular, such memory-based approaches can help a model to more effectively handle appearance changes (representation drift) or occlusions. Ideally, for maximum performance, Online VOS methods would need all or most of the preceding frames (or their extracted information) to be stored in memory and be used for online learning in later frames. Such a solution is not feasible for long videos, as the required memory size grows without bound, and such methods can fail when memory is limited and a target object experiences repeated representation drifts throughout a video. We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos. Motivated by the success of continual learning techniques in preserving previously-learned knowledge, here we propose Gated-Regularizer Continual Learning (GRCL), which improves the performance of any Online VOS subject to limited memory, and a Reconstruction-based Memory Selection Continual Learning (RMSCL), which empowers Online VOS methods to efficiently benefit from stored information in memory. We also analyze the performance of a hybrid combination of the two proposed methods. Experimental results show that the proposed methods are able to improve the performance of Online VOS models by more than 8%, with improved robustness on long-video datasets while maintaining comparable performance on short-video datasets such as DAVIS16, DAVIS17, and YouTube-VOS18. © 2023, CC BY-NC-SA.

关键词： image segmentation

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Dataset for Real-World Human Action Detection Using FMCW mmWave Radar

arXiv

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arXiv 2024年

作者： Jayabahu, Dylan Siva, Parthipan Chirp Inc Canada Vision and Image Processing Group Systems Design Engineering University of Waterloo Canada

Human action detection using privacy-preserving mmWave radar sensors is studied for its applications in healthcare and home automation. Unlike existing research, limited to simulations in controlled environments, we p... 详细信息

关键词： Frequency modulation

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Self-Supervised Learning Using Nonlinear Dependence

arXiv

引用

arXiv 2025年

作者： Sepanj, M. Hadi Ghojogh, Benyamin Fieguth, Paul Vision and Image Processing Group Systems Design Engineering University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo ON Canada

Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies inherent in complex data. In this paper, we introduce Correlation-Dependence Self-Supervised Learning (CDSSL), a novel framework that unifies and extends existing SSL paradigms by integrating both linear correlations and nonlinear dependencies, encapsulating sample-wise and feature-wise interactions. Our approach incorporates the Hilbert-Schmidt Independence Criterion (HSIC) to robustly capture nonlinear dependencies within a Reproducing Kernel Hilbert Space, enriching representation learning. Experimental evaluations on diverse benchmarks demonstrate the efficacy of CDSSL in improving representation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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The Influence of Input Variable Selection on Deep Learning-Based Sea Ice Parameter Inversion from Multi-Sensor Satellite Data

The Influence of Input Variable Selection on Deep Learning-B...

引用

2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Chen, Xinwei Patel, Muhammed Cantu, Fernando J. Pena Park, Jinman Turnes, Javier Noa Jiang, Mingzhe Xu, Linlin Scott, K. Andrea Clausi, David A. Huang, Weimin University of Waterloo Vision and Image Processing Lab Department of System Design Engineering Canada Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798218142186

The automated mapping of Arctic sea ice using satellite data from various sources plays a critical role in maritime shipping and climate science research. Deep learning models, particularly convolutional neural networks (CNNs), have shown significant potential in generating more accurate and reliable automated sea ice maps from synthetic aperture radar (SAR) images. This study focuses on investigating the impact of integrating multi-source data on enhancing the performance of CNN-based sea ice mapping models. By employing a multitask U-Net architecture, the paper aims to estimate three essential sea ice parameters, namely sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE), through different combinations of input data. The AI4Arctic Sea Ice Challenge dataset with multi-sensor satellite data is utilized for model training and evaluation. The experimental findings confirm the effectiveness of incorporating passive microwave data and spatial-temporal information in conjunction with SAR imagery, as well as feature redundancy removal, resulting in improved accuracy for sea ice parameter estimation. © 2023 The Marine Technology Society (MTS).

关键词： Sea ice

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REGION-LEVEL labELS IN ICE CHARTS CAN PRODUCE PIXEL-LEVEL SEGMENTATION FOR SEA ICE TYPES

arXiv

引用

arXiv 2024年

作者： Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K. Andrea Clausi, David A. Vision and Image Processing Lab Systems Design Engineering University of Waterloo Canada School of Marine Science and Engineering South China University of Technology China Department of Mechanical and Mechatronics Engineering University of Waterloo Canada

Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data. In response, our weakly supervised learning method provides a compelling alternative by utilizing lower-resolution regional labels from expert-annotated ice charts. This approach achieves exceptional pixel-level classification performance by introducing regional loss representations during training to measure the disparity between predicted and ice chart-derived sea ice type distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method outperforms the fully supervised U-Net benchmark, the top solution of the AutoIce challenge, in both mapping resolution and class-wise accuracy, marking a significant advancement in automated operational sea ice mapping. © 2024, CC BY.

关键词： Sea ice

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MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting

arXiv

引用

arXiv 2024年

作者： Hu, Yan Song Abboud, Nicolas Ali, Muhammad Qasim Yang, Adam Srebrnjak Elhajj, Imad Asmar, Daniel Chen, Yuhao Zelek, John S. Vision and Image Processing Lab The Faculty of System Design Engineering The University of Waterloo 200 University Avenue West WaterlooON Canada Vision and Robotics Lab Maroun Semaan Faculty of Engineering and Architecture American University of Beirut Riad El Solh Beirut1107 2020 Lebanon

Real-time SLAM with dense 3D mapping is computationally challenging, especially on resource-limited devices. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems struggle to balance hardware simplicity, speed, and map quality. Most systems excel in one or two of the aforementioned aspects but rarely achieve all. A key issue is the difficulty of initializing 3D Gaussians while concurrently conducting SLAM. To address these challenges, we present Monocular GSO (MGSO), a novel real-time SLAM system that integrates photometric SLAM with 3DGS. Photometric SLAM provides dense structured point clouds for 3DGS initialization, accelerating optimization and producing more efficient maps with fewer Gaussians. As a result, experiments show that our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art. Furthermore, our system achieves all results using RGB inputs. We evaluate the Replica, TUM-RGBD, and EuRoC datasets against current live dense reconstruction systems. Not only do we surpass contemporary systems, but experiments also show that we maintain our performance on laptop hardware, making it a practical solution for robotics, A/R, and other real-time applications. © 2024, CC BY-NC-SA.

关键词： SLAM robotics

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