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arXiv 2020年

作者： Raisi, Zobeir Naiel, Mohamed A. Fieguth, Paul Wardell, Steven Zelek, John Vision and Image Processing Lab and Department of Systems Design Engineering University of Waterloo ONN2L 3G1 Canada ATS Automation Tooling Systems Inc. CambridgeONN3H 4R7 Canada

Detection and recognition of text in natural im-ages are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assessespre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Food for thought: Ethical considerations of user trust in computer vision

arXiv

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arXiv 2019年

作者： Pfisterer, Kaylen J. Boger, Jennifer Wong, Alexander Vision and Image Processing Research Group University of Waterloo WaterlooON Canada Intelligent Technologies for Wellness Independent Living Lab University of Waterloo WaterlooON Canada Schlegel-UW Research Institute for Aging WaterlooON Canada

In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be considered and supported. Here, we describe an example of the incorporation of such considerations within the long-term care sector for tracking resident food and fluid intake. We highlight our recent user study conducted to develop a Goldilocks’ quality horizontal prototype designed to support trust cues in which perceived trust in our horizontal prototype was higher than the existing system in place. We discuss the importance and need for user engagement as part of ongoing computer vision-driven technology development and describe several important factors related to trust that are relevant to developing decision-making tools. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making

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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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Improving maximal safe brain tumor resection with photoacoustic remote sensing microscopy

arXiv

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arXiv 2020年

作者： Ecclestone, Benjamin R. Bell, Kevan Abbasi, Saad Dinakaran, Deepak van Landeghem, Frank K.H. Mackey, John R. Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Department of System Design Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada IllumiSonics Inc Department of System Design Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Vision and Image Processing Lab Department of System Design Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Department of Laboratory Medicine and Pathology University of Alberta 116 St & 85 Ave EdmontonABT6G 2B7 Canada Cross Cancer Institute Department of Oncology University of Alberta 116 St & 85 Ave EdmontonABT6G 2V1 Canada

Malignant brain tumors are among the deadliest neoplasms with the lowest survival rates of any cancer type. In considering surgical tumor resection, suboptimal extent of resection is linked to poor clinical outcomes and lower overall survival rates. Currently available tools for intraoperative histopathological assessment require an average of 20 minutes processing and are of limited diagnostic quality for guiding surgeries. Consequently, there is an unaddressed need for a rapid imaging technique to guide maximal resection of brain tumors. Working towards this goal, presented here is an all optical non-contact label-free reflection mode photoacoustic remote sensing (PARS) microscope. By using a tunable excitation laser, PARS takes advantage of the endogenous optical absorption peaks of DNA and cytoplasm to achieve virtual contrast analogous to standard hematoxylin and eosin (H&E) staining. In conjunction, a fast 266 nm excitation is used to generate large grossing scans and rapidly assess small fields in real-time with hematoxylin-like contrast. images obtained using this technique show comparable quality and contrast to the current standard for histopathological assessment of brain tissues. Using the proposed method, rapid, high-throughput, histological-like imaging was achieved in unstained brain tissues, indicating PARS’ utility for intraoperative guidance to improve extent of surgical resection. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Deep ROC analysis and AUC as balanced average accuracy to improve model selection, understanding and interpretation

arXiv

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arXiv 2021年

作者： Carrington, André M. Manuel, Douglas G. Fieguth, Paul W. Ramsay, Tim Osmani, Venet Wernly, Bernhard Bennett, Carol Hawken, Steven McInnes, Matthew Magwood, Olivia Sheikh, Yusuf Holzinger, Andreas Ottawa Hospital Research Institute Institute of Clinical Evaluative Sciences Ottawa Canada Ottawa Hospital Research Institute Department of Family Medicine School of Epidemiology and Public Health University of Ottawa Institute of Clinical Evaluative Sciences Bruyère Research Institute Canada Department of Systems Design Engineering Vision and Image Processing Lab Faculty of Engineering University of Waterloo Canada e-health group Fondazione Bruno Kessler Research Institute department of Psychology and Cognitive Science University of Trento Italy Department of Cardiology Paracelsus Medical University of Salzburg Salzburg Austria Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute Institute of Clinical Evaluative Sciences Ottawa Canada Bruyere Research Institute University of Ottawa Canada Ottawa Hospital Research Institute Department of Biology University of Ottawa Canada Alberta Machine Intelligence Institute University of Alberta Canada Human-Centered AI Lab Medical University Graz Austria

Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted risk, rather than a range of individuals or risk. We propose a method in between, deep ROC analysis, that examines groups of probabilities or predicted risks for more insightful analysis. We translate esoteric measures into familiar terms: AUC and the normalized concordant partial AUC are balanced average accuracy (a new finding);the normalized partial AUC is average sensitivity;and the normalized horizontal partial AUC is average specificity. Along with post-test measures, we provide a method that can improve model selection in some cases and provide interpretation and assurance for patients in each risk group. We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python. © 2021, CC BY-NC-SA.

关键词： Decision making

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SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae

arXiv

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arXiv 2018年

作者： Deglint, Jason L. Tang, Lyndon Wang, Yitian Jin, Chao Wong, Alexander Vision and Image Processing Lab Systems Design Engineering University of Waterloo Ontario Canada School of Environmental Science and Engineering Yat-sen University Guangzhou510275 China

This paper presents SAMSON, a Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae within a water sample. Designed to be portable and low-cost for on-site use, the optical sub-system of SAMSON consists of a mixture of low-cost optics and electronics, designed specifically to capture both fluorescent and absorption responses from a water sample. The graphical user interface (GUI) sub-system of SAMSON was designed to enable flexible visualisation of algae in the water sample in real-time, with the ability to perform fine-grained exposure control and illumination wavelength selection. We demonstrate SAMSON’s capabilities by equipping the system with two fluorescent illumination sources and seven absorption illumination sources to enable the capture of multispectral data from six different algae species (three from the Cyanophyta phylum (blue-green algae) and three from the Chlorophyta phylum (green algae)). The key benefit of SAMSON is the ability to perform rapid acquisition of fluorescence and absorption data at different wavelengths and magnification levels, thus opening the door for machine learning methods to automatically identify and enumerate different algae in water samples using this rich wealth of data. Copyright © 2018, The Authors. All rights reserved.

关键词： Algae

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An inverse problem approach to computational active depth from defocus

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Journal of Physics: Conference Series 2018年第1期1047卷

作者： A Ma A Wong Vision and Image Processing Lab Systems Design Engineering University of Waterloo 200 University Ave W Waterloo ON N2L 3G1

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Generic Hypothesis Generation for Small and Distant Objects

Generic Hypothesis Generation for Small and Distant Objects

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IEEE International Conference on Intelligent Transportation Systems

作者： Ann-Katrin Batzer Christian Scharfenberger Michelle Karg Stefan Lueke Jurgen Adamy Control Methods and Robotics Technische Universitat Darmstadt Vision and Image Processing Lab University of Waterloo Continental AG

ISBN: (纸本)9781509018901

Existing approaches to object detection address the generation of object hypotheses by extracting several cues in natural and automotive images, relying on objects with sufficiently high resolution. Very little to almost no approaches, however, address the generation of hypothesis of very small or distant objects in images such as on motorways. Here, we propose a simple yet effective approach to generating hypotheses of small and distant objects in images. Our key contribution is a novel voting scheme that makes efficient use of the different appearance of small candidate objects to their environment. We model the environment as being composed of very few regions with homogeneous appearance, extracted by evaluating the inner statistics of an image in an unsupervised fashion. Small regions that can not be assigned to the environment form potential candidate locations. Experimental results on motorway scenes with cars, traffic signs, and other automotive objects based on a variety of performance evaluation metrics show that our approach provides promising results, and outperforms one of the currently leading approaches in generating hypotheses for small and/or distant objects in images.

关键词： Vehicles Robustness image resolution Cameras Feature extraction Computational modeling Roads

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HIGH DYNAMIC RANGE MAP ESTIMATION VIA FULLY CONNECTED RANDOM FIELDS WITH STOCHASTIC CLIQUES

HIGH DYNAMIC RANGE MAP ESTIMATION VIA FULLY CONNECTED RANDOM...

引用

IEEE International Conference on image processing

作者： F. Y. Li M. J. Shafiee A. G. Chung B. Chwyl F. Kazemzadeh A. Wong J. Zelek Vision & Image Processing Lab Systems Design Engineering Dept. University of Waterloo

ISBN: (纸本)9781479983407

The reconstruction of high dynamic range (HDR) images via conventional camera systems and low dynamic range (LDR) images is a growing field of research in image acquisition. The radiance map associated with the HDR image of a scene is typically computed using multiple images of the same scene captured at different exposures (i.e., bracketed LDR imzages). This approach, though inexpensive, is sensitive to noise under high camera ISO. Each bracketed image is associated with a different level of noise due to the change in exposure time, and the noise is further amplified when tone-mapping the HDR image for display. A new framework is proposed to address the associated noise in the context of random fields. The estimation of the HDR image from a set of LDR images is formulated as a stochastically fully connected conditional random field where the spatial information is incorporated to compute the HDR value in combination with the LDR image values. Experimental results show that the proposed framework compensated the non-stationary ISO noise while preserving the boundaries in the estimated HDR images.

关键词： High Dynamic Range Imaging Conditional Random Fields image Denoising HDR Reconstruction SFCRF image denoising hot dry rocks GATA3 gene LAND DISPOSAL RESTRICTIONS Noise images HIGH DYNAMIC RANGE Markov random fields field surveys Noise levels Brackets Cameras

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EXTERNAL FORCES FOR ACTIVE CONTOURS USING THE UNDECIMATED WAVELET TRANSFORM

EXTERNAL FORCES FOR ACTIVE CONTOURS USING THE UNDECIMATED WA...

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IEEE International Conference on image processing

作者： Ahmed Gawish Paul Fieguth Vision and Image Processing (VIP) Lab Department of Systems Design Engineering University of Waterloo

ISBN: (纸本)9781479983407

A limitation of active contours models (both parametric and geometric) is their sensitivity to noise. Many solutions to noise sensitivity have been proposed in the literature, with the current state-of-the-art based on image blurring and multiresolution processing. However a significant drawback of both approaches is the side effect of edge delocalization. In this paper, gradient information extracted from all resolutions of the undecimated wavelet transform is used to build the external force map for the active contour. The new map accurately drives the active contour and improves edge localization. The proposed method builds on both Gradient Vector Flow and Vector Field Convolution active contours. Comparisons to classical and state-of-the-art methods show a dramatic improvement in active contour convergence for all levels of noise.

关键词： Active contours Undecimated wavelet transform Active contours contour image blur Noise sensitivity Side Effect wavelet transforms Gradient vector Noise Parametric

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