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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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Self-Supervised Learning Using Nonlinear Dependence

arXiv

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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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Mobile's millimeter-wave makeover: Wireless engineers long considered high frequencies worthless for cellular systems. They couldn't be more wrong

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IEEE Spectrum 2014年第9期51卷 34-58页

作者： Rappaport, Theodore Roh, Wonil Cheun, Kyungwhoon Vision and Image Processing Research Group Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

People in real estate joke that there are only three things that matter in the business of buying and selling property: location, location, location. The same could be said for radio spectrum. The frequencies used for cellular communications have acquired the status of waterfront lots-highly coveted and woefully scarce. And like beach-home buyers in a bidding war, mobile operators must constantly jockey for these prime parcels, sometimes shelling out as much as tens of billions of dollars for just a small sliver of the electromagnetic pie. © 1964-2012 IEEE.

关键词： Location

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

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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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Autocalibration: Finding infinity in a projective reconstruction

Autocalibration: Finding infinity in a projective reconstruc...

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Canadian Conference on Computer and Robot vision

作者： Cavan, Neil Fieguth, Paul Clausi, David A. Vision and Image Processing Lab. Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (纸本)9780769543628

In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associated with auto calibration is the nonlinear optimization of a cost function based on extracting camera intrinsics from a potential upgrading transform, and evaluating fitness with respect to prior knowledge of physical cameras. The nonlinearity of the problem leads, in general, to poor convergence and a failure of the calibration process. This paper presents a novel auto calibration pipeline that seeks to develop a more robust approach to the nonlinear optimization. After testing a variety of methods, none of which yielded satisfactory solutions, we have developed a strategy combining the best aspects of two methods representing the current state of the art. The former method preconditions the projective space by ensuring it is quasi-affine with respect to camera centers, allowing a naive initialization in the new space, and uses a fitness measure resistant to focal length collapse. The latter method initializes using the best results of an exhaustive search over reasonable values of focal length. Our novel approach, presented here, uses the exhaustive search initialization of the latter combined with the improved fitness measure of the former, producing results that outperform both of its predecessors. © 2011 IEEE.

关键词： Cameras

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Self-adjusting beat detection and prediction in music

Self-adjusting beat detection and prediction in music

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Proceedings - IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Harper, Robert Jernigan, M.E. Vision and Image Processing Lab. Systems Design Engineering University of Waterloo Waterloo Ont. Canada

This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for selection as the best beat hypothesis. Beat prediction nodes perform period self-adjustment to better represent the detected music beat period. The system is tested using a variety of music from different genres and shows promise, in many cases with high correct beat detection percentages.

关键词： Computer music

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Efficient Bayesian inference using fully connected conditional random fields with stochastic cliques

Efficient Bayesian inference using fully connected condition...

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作者： Shafiee, M.J. Wong, A. Siva, P. Fieguth, P. Vision and Image Processing Lab System Design Engineering Dept. University of Waterloo Canada

ISBN: (纸本)9781479957514

Conditional random fields (CRFs) are one of the most powerful frameworks in image modeling. However practical CRFs typically have edges only between nearby nodes;using more interactions and expressive relations among nodes make these methods impractical for large-scale applications, due to the high computational complexity. Recent work has shown that fully connected CRFs can be tractable by defining specific potential functions. In this paper, we present a novel framework to tackle the computational complexity of a fully connected graph without requiring specific potential functions. Instead, inspired by random graph theory and sampling methods, we propose a new clique structure called stochastic cliques. The stochastically fully connected CRF (SFCRF) is a marriage between random graphs and random fields, benefiting from the advantages of fully connected graphs while maintaining computational tractability. The effectiveness of SFCRF was examined by binary image labeling of highly noisy images. The results show that the proposed framework outperforms an adjacency CRF and a CRF with a large neighborhood size. © 2014 IEEE.

关键词： Computational complexity

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Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements

Multi-scale saliency-guided compressive sensing approach to ...

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2012 9th Conference on Computer and Robot vision, CRV 2012

作者： Schwartz, Shimon Wong, Alexander Clausi, David A. Department of Systems Design Engineering University of Waterloo Vision and Image Processing Lab. Research Group Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769546834

Improving laser range data acquisition speed is important for many robotic applications such as mapping and localization. One approach to reducing acquisition time is to acquire laser range data through a dynamically small subset of measurement locations. The reconstruction can then be performed based on the concept of compressed sensing (CS), where a sparse signal representation allows for signal reconstruction at sub-Nyquist measurements. Motivated by this, a novel multi-scale saliency-guided CS-based algorithm is proposed for an efficient robotic laser range data acquisition for robotic vision. The proposed system samples the objects of interest through an optimized probability density function derived based on multi-scale saliency rather than the uniform random distribution used in traditional CS systems. Experimental results with laser range data from indoor and outdoor environments show that the proposed approach requires less than half the samples needed by existing CS-based approaches while maintaining the same reconstruction performance. In addition, the proposed method offers significant improvement in reconstruction SNR compared to current CS-based approaches. © 2012 IEEE.

关键词： Compressed sensing

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An inverse problem approach to computational active depth from defocus 9

An inverse problem approach to computational active depth fr...

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9th International Conference on Inverse Problems in engineering, ICIPE 2017

作者： Ma, A. Wong, A. Vision and Image Processing Lab Systems Design Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

A novel methodology for inferring depth measurements using active quasi-random colored point projection patterns is presented by recourse to computational inverse modelling within the context of depth from defocus. The forward problem is considered that when capturing objects using a camera, light reflected off of objects at different depths will result in blurry observations in the acquired image, whereas the inverse problem seeks to estimate the depth from the knowledge of the blurry observations. In the proposed approach, a colored quasi-random point projection pattern is projected onto the scene of interest, and each projection point in the image captured by a cellphone camera is analyzed using an inverse model to estimate the depth at that point. The proposed method has a relatively simple setup, consisting of a camera and a projector, and enables depth inference from a single capture. We conclude with qualitative and quantitative results that highlight the strong potential for enabling active depth sensing in a simple, efficient manner. © Published under licence by IOP Publishing Ltd.

关键词： Inverse problems

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Human action recognition using salient opponent-based motion features

Human action recognition using salient opponent-based motion...

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7th Canadian Conference on Computer and Robot vision, CRV 2010

作者： Shabani, Amir-Hossein Zelek, John S. Clausi, David A. Vision and Image Processing Group Department of Systems Design Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769540405

Human action recognition can be performed using multi-scale salient features which encode the local events in the video. Existing feature extraction methods use non-causal spatio-temporal filtering, and hence, they are not biologically plausible. To address this inconsistency, new features extracted from a biologically plausible perception model are introduced. In this model, the opponent-based motion energy is computed using oriented motion filters constructed from a bio-inspired time-causal filtering. The salient features are then extracted from the regions of interest in the motion energy map. The extracted opponent-based motion features are then utilized for action classification with a bag-of-words approach. Experiments using a publicly available (Weizmann) data set shows 93:5% classification accuracy which is an improvement over comparable methods. © 2010 IEEE.

关键词： Classification (of information)

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