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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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Occluded Pedestrian Detection Through Guided Attention in CNNs 31

Occluded Pedestrian Detection Through Guided Attention in CN...

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ieee/CVF conference on computer vision and pattern recognition

作者： Shanshan Zhang Jian Yang Bernt Schiele Ministry of Education Key Lab of Intelligent Perception and Systems for High-Dimensional Information Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China Saarland Informatics Campus Max Planck Institute for Informatics Germany

ISBN: (纸本)9781538664216;9781538664209

Pedestrian detection has progressed significantly in the last years. However, occluded people are notoriously hard to detect, as their appearance varies substantially depending on a wide range of occlusion patterns. In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection. We start with interpreting CNN channel features of a pedestrian detector, and we find that different channels activate responses for different body parts respectively. These findings motivate us to employ an attention mechanism across channels to represent various occlusion patterns in one single model, as each occlusion pattern can be formulated as some specific combination of body parts. Therefore, an attention network with self or external guidances is proposed as an add-on to the baseline FasterRCNN detector. When evaluating on the heavy occlusion subset, we achieve a significant improvement of 8pp to the baseline FasterRCNN detector on CityPersons and on Caltech we outperform the state-of-the-art method by 4pp.

关键词： Detectors Feature extraction computer architecture Object detection Training Body regions Correlation

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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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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

引用

ieee conference on computer vision and pattern recognition, cvpr 2015

ISBN: (纸本)9781467369640

The proceedings contain 602 papers. The topics discussed include: going deeper with convolutions;propagated image filtering;web scale photo hash clustering on a single machine;supervised discrete hashing;what do 15,000 object categories tell us about classifying and localizing actions?;landmarks-based kernelized subspace alignment for unsupervised domain adaptation;blur kernel estimation using normalized color-line priors;a light transport model for mitigating multipath interference in time-of-flight sensors;traditional saliency reloaded: a good old model in new shape;automatic construction of robust spherical harmonic subspaces;leveraging stereo matching with learning-based confidence measures;saliency detection via cellular automata;and efficient sparse-to-dense optical flow estimation using a learned basis and layers.

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Deep Tattoo recognition 29

Deep Tattoo Recognition

引用

29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Di, Xing Patel, Vishal M. Rutgers State Univ Dept Elect & Comp Engn 508 CoRE94 Brett Rd Piscataway NJ 08854 USA

ISBN: (纸本)9781509014378

Tattoo is a soft biometric that indicates discriminative characteristics of a person such as beliefs and personalities. Automatic detection and recognition of tattoo images is a difficult problem. We present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks. Furthermore, we show that rather than using a simple contrastive loss function, triplet loss function can significantly improve the performance of a tattoo matching system. Extensive experiments on a recently introduced Tatt-C dataset show that our method is able to capture the meaningful structure of tattoos and performs significantly better than many competitive tattoo recognition algorithms.

关键词： Deep neural networks

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Heterogeneous Face recognition using Inter-Session Variability Modelling 29

Heterogeneous Face Recognition using Inter-Session Variabili...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Pereira, Tiago de Freitas Marcel, Sebastien IDIAP Res Inst Martigny Switzerland

ISBN: (纸本)9781509014378

The task of Heterogeneous Face recognition consists inmatch face images that were sensed in different modalities, such as sketches to photographs, thermal images to photographs or near infrared to photographs. In this preliminary work we introduce a novel and generic approach based on Inter-session Variability Modelling to handle this task. The experimental evaluation conducted with two different image modalities showed an average rank-1 identification rates of 96.93% and 72.39% for the CUHK-CUFS (Sketches) and CASIA NIR-VIS 2.0 (Near infra-red) respectively. This work is totally reproducible and all the source code for this approach is made publicly available.

关键词： Face recognition

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A Scalable High-Performance Hardware Architecture for Real-Time Stereo vision by Semi-Global Matching 29

A Scalable High-Performance Hardware Architecture for Real-T...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hofmann, Jaco Korinth, Jens Koch, Andreas Tech Univ Darmstadt Embedded Syst & Applicat Grp ESA Darmstadt Germany

ISBN: (纸本)9781509014378

Perceiving distance from two camera images, a task called stereo vision, is fundamental for many applications in robotics or automation. However, algorithms that compute this information at high accuracy have a high computational complexity. One such algorithm, Semi Global Matching (SGM), performs well in many stereo vision benchmarks, while maintaining a manageable computational complexity. Nevertheless, CPU and GPU implementations of this algorithm often fail to achieve real-time processing of camera images, especially in power-constrained embedded environments. This work presents a novel architecture to calculate disparities through SGM. The proposed architecture is highly scalable and applicable for low-power embedded as well as high-performance multi-camera high-resolution applications.

关键词： Stereo vision

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Embedded Computing Framework for vision-based Real-time Surround Threat Analysis and Driver Assistance 29

Embedded Computing Framework for Vision-based Real-time Surr...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lu, Frankie Lee, Sean Satzoda, Ravi Kumar Trivedi, Mohan Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781509014378

In this paper, we present a distributed embedded vision system that enables surround scene analysis and vehicle threat estimation. The proposed system analyzes the surroundings of the ego-vehicle using four cameras, each connected to a separate embedded processor. Each processor runs a set of optimized vision-based techniques to detect surrounding vehicles, so that the entire system operates at real-time speeds. This setup has been demonstrated on multiple vehicle testbeds with high levels of robustness under real-world driving conditions and is scalable to additional cameras. Finally, we present a detailed evaluation which shows over 95% accuracy and operation at nearly 15 frames per second.

关键词： Vehicles

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Soft-margin learning for multiple feature-kernel combinations with Domain Adaptation, for recognition in surveillance face datasets 29

Soft-margin learning for multiple feature-kernel combination...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Banerjee, Samik Das, Sukhendu IIT Madras Madras 600036 Tamil Nadu India

ISBN: (纸本)9781509014378

Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets.

关键词： Face recognition

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Hidden Hands: Tracking Hands with an Occlusion Aware Tracker 29

Hidden Hands: Tracking Hands with an Occlusion Aware Tracker

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Rangesh, Akshay Ohn-Bar, Eshed Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92093 USA

ISBN: (纸本)9781509014378

This work presents an occlusion aware hand tracker to reliably track both hands of a person using a monocular RGB camera. To demonstrate its robustness, we evaluate the tracker on a challenging, occlusion-ridden naturalistic driving dataset, where hand motions of a driver are to be captured reliably. The proposed framework additionally encodes and learns tracklets corresponding to complex (yet frequently occurring) hand interactions offline, and makes an informed choice during data association. This provides positional information of the left and right hands with no intrusion (through complete or partial occlusions) over long, unconstrained video sequences in an online manner. The tracks thus obtained may find use in domains such as human activity analysis, gesture recognition, and higher-level semantic categorization.

关键词： Palmprint recognition

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