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Automated Segmentation of the Vocal Folds in Laryngeal Endoscopy Videos Using Deep Convolutional Regression Networks

Automated Segmentation of the Vocal Folds in Laryngeal Endos...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ali Hamad Megan Haney Teresa E. Lever Filiz Bunyak Department of Electrical Engineering and Computer Science School of Medicine University of Missouri-Columbia Columbia MO USA Department of Veterinary Pathobiology School of Medicine University of Missouri-Columbia Columbia MO USA Department of Otolaryngology-Head and Neck Surgery School of Medicine University of Missouri-Columbia Columbia MO USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Swallowing and breathing are vital, life-sustaining upper airway functions that require precise, reciprocal coordination of the vocal folds (VFs). During swallowing, the VFs must fully close to prevent aspiration of food/liquid into the lungs, whereas during breathing, the VFs must remain open to prevent obstruction of airflow into and out of the lungs. This coordination may become impaired by a variety of neurological conditions and diseases. Clinical evaluation relies on transnasal endoscopy to visualize the VFs within the larynx, and subjective interpretation of VF function by clinicians. However, objective, quantitative, and high-throughput analysis of VF function is important for early diagnosis, monitoring disease progression, treatment monitoring, and treatment discovery. In this paper we propose a fully automated, deep learning based VF segmentation system for the analysis of VF motion behavior captured using flexible endoscopes with low-speed capability. Experimental results on human laryngeal videos showed promising results that were robust to many challenges caused by imaging, anatomical, and behavioral variations. The proposed segmentation and tracking system will be used to compute quantitative outcome measures describing VF motion behavior in order to help clinical practice and scientific discovery.

关键词： Videos Endoscopes Image segmentation Training Motion segmentation Convolution Machine learning

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M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

M2U-Net: Effective and Efficient Retinal Vessel Segmentation...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tim Laibacher Tillman Weyde Sepehr Jalali City University of London University College London

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.

关键词： Image segmentation Image resolution Retinal vessels computer architecture Training Neural networks

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UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tobias Ringwald Lars Sommer Arne Schumann Jürgen Beyerer Rainer Stiefelhagen CV:HCI Karlsruhe Institute of Technology Karlsruhe Germany Vision and Fusion Lab Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Vehicle detection in aerial imagery is a challenging task due to small object sizes, high object density and partial occlusions. While past research mostly focused on improving detection accuracy, inference speed is another important factor when using CNN object detectors in a real life scenario - especially when targeting mobile platforms like unmanned aerial vehicles (UAVs). In this work, we compare several established detection frameworks in terms of their accuracy-speed trade-off and show that the Single Shot MultiBox Detector (SSD) offers the best compromise. We subsequently undertake a thorough evaluation of several design choices to further increase detection speed while sacrificing little to no accuracy. This includes the choice of base network architecture, improved prediction layers and an automatic model pruning approach. Given our evaluation results, we finally construct UAV-Net - a novel aerial vehicle detector that has a model size of less than 0.4 MiB and is more than 16 times faster than current top performing approaches. UAV-Net is well suited for on-board processing and operates in real time on a Jetson TX2 platform. Nevertheless, its accuracy is on par with state-of-the-art approaches on the DLR 3K, VEDAI and UAVDT datasets. Code and models are available on the project website.

关键词： Convolution Detectors Feature extraction Vehicle detection Task analysis Machine learning Kernel

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Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection

Fooling Automated Surveillance Cameras: Adversarial Patches ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Simen Thys Wiebe Van Ranst Toon Goedemé KU Leuven EAVISE Technology Campus De Nayer Belgium KU Leuven Leuven Belgium

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Adversarial attacks on machine learning models have seen increasing interest in the past years. By making only subtle changes to the input of a convolutional neural network, the output of the network can be swayed to output a completely different result. The first attacks did this by changing pixel values of an input image slightly to fool a classifier to output the wrong class. Other approaches have tried to learn "patches" that can be applied to an object to fool detectors and classifiers. Some of these approaches have also shown that these attacks are feasible in the real-world, i.e. by modifying an object and filming it with a video camera. However, all of these approaches target classes that contain almost no intra-class variety (e.g. stop signs). The known structure of the object is then used to generate an adversarial patch on top of it. In this paper, we present an approach to generate adversarial patches to targets with lots of intra-class variety, namely persons. The goal is to generate a patch that is able successfully hide a person from a person detector. An attack that could for instance be used maliciously to circumvent surveillance systems, intruders can sneak around undetected by holding a small cardboard plate in front of their body aimed towards the surveilance camera. From our results we can see that our system is able significantly lower the accuracy of a person detector. Our approach also functions well in real-life scenarios where the patch is filmed by a camera. To the best of our knowledge we are the first to attempt this kind of attack on targets with a high level of intra-class variety like persons.

关键词： Detectors Image color analysis Cameras Robustness Optimization computer vision Surveillance

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Dynamic Representations Toward Efficient Inference on Deep Neural Networks by Decision Gates

Dynamic Representations Toward Efficient Inference on Deep N...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohammad Saeed Shafiee Mohammad Javad Shafiee Alexander Wong Faculty of Electrical Engineering Shahid Rajee University Tehran Iran Vision & Image Processing Research Group Waterloo AI Institute University of Waterloo DarwinAI Corp. Waterloo Canada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

While deep neural networks extract rich features from the input data, the current trade-off between depth and computational cost makes it difficult to adopt deep neural networks for many industrial applications, especially when computing power is limited. Here, we are inspired by the idea that, while deeper embeddings are needed to discriminate difficult samples (i.e., fine-grained discrimination), a large number of samples can be well discriminated via much shallower embeddings (i.e., coarse-grained discrimination). In this study, we introduce the simple yet effective concept of decision gates (d-gate), modules trained to decide whether a sample needs to be projected into a deeper embedding or if an early prediction can be made at the d-gate, thus enabling the computation of dynamic representations at different depths. The proposed d-gate modules can be integrated with any deep neural network and reduces the average computational cost of the deep neural networks while maintaining modeling accuracy. The proposed d-gate framework is examined via different network architectures and datasets, with experimental results showing that leveraging the proposed d-gate modules led to a ~43% speed-up and 44% FLOPs reduction on ResNet-101 and 55% speed-up and 39% FLOPs reduction on DenseNet-201 trained on the CIFAR10 dataset with only ~2% drop in accuracy. Furthermore, experiments where d-gate modules are integrated into ResNet-101 trained on the ImageNet dataset demonstrate that it is possible to reduce the computational cost of the network by 1.5 GFLOPs without any drop in the modeling accuracy.

关键词： Neural networks computer architecture Computational efficiency Feature extraction Computational modeling Visualization Logic gates

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AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design

AttoNets: Compact and Efficient Deep Neural Networks for the...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexander Wong Zhong Qiu Lin Brendan Chwyl Vision & Image Processing Research Group Waterloo AI Institute University of Waterloo DarwinAI Corp. Waterloo Canada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-device edge scenarios such as on mobile devices, consumer devices, drones, and vehicles. There has been significant recent effort in designing small, low-footprint deep neural networks catered for low-power edge devices, with much of the focus on two extremes: hand-crafting via design principles or fully automated network architecture search. In this study, we take a deeper exploration into a human-machine collaborative design approach for creating highly efficient deep neural networks through a synergy between principled network design prototyping and machine-driven design exploration. The efficacy of human-machine collaborative design is demonstrated through the creation of AttoNets, a family of highly efficient deep neural networks for on-device edge deep learning. Each AttoNet possesses a human-specified network-level macro-architecture comprising of custom modules with unique machine-designed module-level macro-architecture and micro-architecture designs, all driven by human-specified design requirements. Experimental results for the task of object recognition showed that the AttoNets created via human-machine collaborative design has significantly fewer parameters and computational costs than state-of-the-art networks designed for efficiency while achieving noticeably higher accuracy (with the smallest AttoNet achieving ~1.8% higher accuracy while requiring ~10x fewer multiply-add operations and parameters than MobileNet-V1). Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.

关键词： Neural networks Convolutional codes Man-machine systems Prototypes Collaboration Task analysis Network architecture

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Joint denoising and decompression using CNN regularization 31

Joint denoising and decompression using CNN regularization

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31st Meeting of the ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2018

作者： González, Mario Preciozzi, Javier Musé, Pablo Almansa, Andrés IIE-FING Universidad de la República Uruguay CNRS and MAP5 Université Paris Descartes France

Wavelet compression schemes such as JPEG2000 may lead to very specific visual artifacts due to quantization of noisy wavelet coefficients. These artifacts have highly spatially-correlated structure, making it difficult to be removed with standard denoising algorithms. In this work, we propose a joint denoising and decompression method that combines a data-fitting term, which takes into account the quantization process, and an implicit prior learnt using a state-of-the-art denoising CNN. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： pattern recognition

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3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results 31

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and V...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kalinin, Alexandr A. Allyn-Feuer, Ari Ade, Alex Fon, Gordon-Victor Meixner, Walter Dilworth, David de Wet, Jeffrey R. Higgins, Gerald A. Zheng, Gen Creekmore, Amy Wiley, John W. Verdone, James E. Veltri, Robert W. Pienta, Kenneth J. Coffey, Donald S. Athey, Brian D. Dinov, Ivo D. Univ Michigan Sch Med Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Univ Michigan Sch Nursing SOCR Hlth Behav & Biol Sci Ann Arbor MI 48109 USA Univ Michigan Sch Med Dept Internal Med Div Gastroenterol Ann Arbor MI 48109 USA Johns Hopkins Univ Dept Urol James Buchanan Brady Urol Inst Baltimore MD 21218 USA Univ Michigan Michigan Inst Data Sci MIDAS Ann Arbor MI 48109 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AU-ROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project webpage: http://***/projects/3d-cell-morphometry/***.

关键词： Three-dimensional displays computer architecture Image segmentation Microprocessors Microscopy Two dimensional displays Morphology

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NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results 31

NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Timofte, Radu Gu, Shuhang Wu, Jiqing Van Gool, Luc Zhang, Lei Yang, Ming-Hsuan Haris, Muhammad Shakhnarovich, Greg Ukita, Norimichi Hu, Shijia Bei, Yijie Hui, Zheng Jiang, Xiao Gu, Yanan Liu, Jie Wang, Yifan Perazzi, Federico McWilliams, Brian Sorkin-Hornung, Alexander Sorkine-Hornung, Olga Schroers, Christopher Yu, Jiahui Fan, Yuchen Yang, Jianchao Xu, Ning Wang, Zhaowen Wang, Xinchao Huang, Thomas S. Wang, Xintao Yu, Ke Hui, Tak-Wai Dong, Chao Lin, Liang Loy, Chen Change Park, Dongwon Kim, Kwanyoung Chun, Se Young Zhang, Kai Liu, Pengjv Zuo, Wangmeng Guo, Shi Liu, Jiye Xu, Jinchang Liu, Yijiao Xiong, Fengye Dong, Yuan Bai, Hongliang Damian, Alexandru Ravi, Nikhil Menon, Sachit Rudin, Cynthia Seo, Junghoon Jeon, Taegyun Koo, Jamyoung Jeon, Seunghyun Kim, Soo Ye Choi, Jae-Seok Ki, Sehwan Seo, Soomin Sim, Hyeonjun Kim, Saehun Kim, Munchurl Chen, Rong Zeng, Kun Guo, Jinkang Qu, Yanyun Li, Cuihua Ahn, Namhyuk Kang, Byungkon Sohn, Kyung-Ah Yuan, Yuan Zhang, Jiawei Pang, Jiahao Xu, Xiangyu Zhao, Yan Deng, Wei Ul Hussain, Sibt Aadil, Muneeb Rahim, Rafia Cai, Xiaowang Huang, Fang Xu, Yueshu Michelini, Pablo Navarrete Zhu, Dan Liu, Hanwen Kim, Jun-Hyuk Lee, Jong-Seok Huang, Yiwen Qiu, Ming Jing, Liting Zeng, Jiehang Wang, Ying Sharma, Manoj Mukhopadhyay, Rudrabha Upadhyay, Avinash Koundinya, Sriharsha Shukla, Ankit Chaudhury, Santanu Zhang, Zhe Hu, Yu Hen Fu, Lingzhi Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Merantix Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium Hong Kong Polytech Univ Hong Kong Peoples R China Univ Calif Merced Merced CA USA Toyota Technol Inst Nagoya Aichi Japan TTI Chicago Chicago IL USA Duke Univ Durham NC USA Xidian Univ Sch Elect Engn Xian Shaanxi Peoples R China Disney Res Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Facebook Oculus Zurich Switzerland Univ Illinois Urbana IL USA Snap Inc Los Angeles CA USA Adobe Res San Jose CA USA Stevens Inst Technol Hoboken NJ 07030 USA Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan South Korea Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Faceall Technol Co Ltd Beijing Peoples R China Satrec Initiat R&D Ctr Daejeon South Korea Korea Adv Inst Sci & Technol Daejeon South Korea Xiamen Univ Xiamen Peoples R China Ajou Univ Suwon South Korea Univ Alberta Elect & Comp Engn Edmonton AB Canada Fuzhou Univ Fuzhou Fujian Peoples R China Natl Univ Comp & Emerging Sci Islamabad Pakistan BOE Technol Grp Co Ltd Beijing Peoples R China Yonsei Univ Seoul South Korea Wenhua Coll Wuhan Hubei Peoples R China Xiamen Univ Software Sch Xiamen Peoples R China CSIR CEERI Pilani Rajasthan India Univ Wisconsin Dept Elect & Comp Engn 1415 Johnson Dr Madison WI 53706 USA Xi An Jiao Tong Univ Sch Elect & Informat Engn Inst Integrated Automat MOE KLINNS Lab Xian Shaanxi Peoples R China

ISBN: (纸本)9781538661000

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

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Designing a Moral Compass for the Future of computer vision using Speculative Analysis 30

Designing a Moral Compass for the Future of Computer Vision ...

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30th ieee/cvf conference on computer vision and pattern recognition workshops (cvprw)

作者： Skirpan, Michael Yeh, Tom Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9781538607336

In this paper we discuss and analyze possible futures for technologies in the field of computer vision (CV). Using a method we have coined speculative analysis we take a broad look at research trends in the field to categorize risks, analyze which ones are most threatening and likely, and ultimately summarize conclusions for how the field may attempt to stem future harms caused by CV technologies. We develop narrative case studies to provoke dialogue and deeply explore possible risk scenarios we found to be most probable and severe. We arrive at the position that there are serious potentials for CV to cause discriminatory harm and exacerbate cybersecurity issues.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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