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SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence 37

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SolderNet: Towards Trustworthy Visual Inspection of Solder J...

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Gunraj, Hayden Guerrier, Paul Fernandez, Sheldon Wong, Alexander Vision and Image Processing Research Group University of Waterloo Waterloo Canada DarwinAI Corp. Waterloo Canada Moog Inc. New York United States

ISBN: (纸本)9781577358800

In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet which we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Printed circuit boards

Particle-Filtering-based Latent Diffusion for Inverse Problems

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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

Multi Player Tracking in Ice Hockey with Homographic Projections

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arXiv 2024年

作者： Prakash, Harish Cheng Shang, Jia Nsiempba, Ken M. Chen, Yuhao Clausi, David A. Zelek, John S. Vision and Image Processing Group University of Waterloo ON Canada

Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities. Tracking players from monocular broadcast feeds is an important computer vision problem offering various downstream analytics and enhanced viewership experience. However, existing trackers encounter significant difficulties in dealing with occlusions, blurs, and agile player movements prevalent in telecast feeds. In this work, we propose a novel tracking approach by formulating MOT as a bipartite graph matching problem infused with homography. We disentangle the positional representations of occluded and overlapping players in broadcast view, by mapping their foot keypoints to an overhead rink template, and encode these projected positions into the graph network. This ensures reliable spatial context for consistent player tracking and unfragmented tracklet prediction. Our results show considerable improvements in both the IDsw and IDF1 metrics on the two available broadcast ice hockey datasets. © 2024, CC BY.

关键词： Sports

How Much You Ate? Food Portion Estimation on Spoons

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How Much You Ate? Food Portion Estimation on Spoons

IEEE Computer Society Conference on Computer vision and Pattern Recognition Workshops (CVPRW)

作者： Aaryam Sharma Chris Czarnecki Yuhao Chen Pengcheng Xi Linlin Xu Alexander Wong Vision and Image Processing Lab University of Waterloo National Research Council Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： Computer vision image segmentation Accuracy Pipelines Estimation image capture Cameras

A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation

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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

A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data

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arXiv 2023年

作者： Tai, Chi-En Amy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada

Recently, a new form of magnetic resonance imaging (MRI) called synthetic correlated diffusion (CDIs) imaging was introduced and showed considerable promise for clinical decision support for cancers such as prostate cancer when compared to current gold-standard MRI techniques. However, the efficacy for CDIs for other forms of cancers such as breast cancer has not been as well-explored nor have CDIs data been previously made publicly available. Motivated to advance efforts in the development of computer-aided clinical decision support for breast cancer using CDIs, we introduce Cancer-Net BCa, a multi-institutional open-source benchmark dataset of volumetric CDIs imaging data of breast cancer patients. Cancer-Net BCa contains CDIs volumetric images from a pre-treatment cohort of 253 patients across ten institutions, along with detailed annotation metadata (the lesion type, genetic subtype, longest diameter on the MRI (MRLD), the Scarff-Bloom-Richardson (SBR) grade, and the post-treatment breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy). We further examine the demographic and tumour diversity of the Cancer-Net BCa dataset to gain deeper insights into potential biases. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging

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arXiv 2023年

作者： Tai, Chi-En Amy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada

The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. However, there are different levels of severity of breast cancer requiring different treatment strategies, and hence, grading breast cancer has become a vital component of breast cancer diagnosis and treatment planning. Specifically, the gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. Unfortunately, the current method to determine the SBR grade requires removal of some cancer cells from the patient which can lead to stress and discomfort along with costly expenses. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDIs) imaging, a new magnetic resonance imaging (MRI) modality and found that it achieves better performance on SBR grade prediction compared to those learnt using gold-standard imaging modalities. Hence, we introduce Cancer-Net BCa-S, a volumetric deep radiomics approach for predicting SBR grade based on volumetric CDIs data. Given the promising results, this proposed method to identify the severity of the cancer would allow for better treatment decisions without the need for a biopsy. Cancer-Net BCa-S has been made publicly available as part of a global open-source initiative for advancing machine learning for cancer care. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemotherapy

How Much You Ate? Food Portion Estimation on Spoons

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arXiv 2024年

作者： Sharma, Aaryam Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

关键词： Cameras

In The Wild Ellipse Parameter Estimation for Circular Dining Plates and Bowls

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arXiv 2024年

作者： Pathiranage, Akil Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view angles and food portion sizes. Automatically detecting the elliptical rim of plates and bowls and estimating their ellipse parameters for data in-the-wild is challenging: diverse camera angles and plate shapes could have been used for capture, noisy background, multiple non-uniform plates and bowls in the image could be present. Recent advancements in foundational models offer promising capabilities for zero-shot semantic understanding and object segmentation. However, the output mask boundaries for plates and bowls generated by these models often lack consistency and precision compared to traditional ellipse fitting methods. In this paper, we combine ellipse fitting with semantic information extracted by zero-shot foundational models and propose WildEllipseFit, a method to detect and estimate the elliptical rim for plate and bowl. Evaluation on the proposed Yummly-ellipse dataset demonstrates its efficacy and zero-shot capability in real-world scenarios. © 2024, CC BY.

关键词： Parameter estimation