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QuadricSLAM: Dual Quadrics as SLAM Landmarks 31

QuadricSLAM: Dual Quadrics as SLAM Landmarks

引用

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

作者： Nicholson, Lachlan Milford, Michael Sunderhauf, Niko Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld Australia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D bounding boxes (such as those typically obtained from visual object detection systems) can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for deep-learned object detectors that addresses the challenge of partial object detections often encountered in robotics applications, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.

关键词： Simultaneous localization and mapping Cameras Object detection Trajectory Robot vision systems Three-dimensional displays

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Comparing the Effects of Annotation Type on Machine Learning Detection Performance

Comparing the Effects of Annotation Type on Machine Learning...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： James F. Mullen Franklin R. Tanner Phil A. Sallee Raytheon Corporation

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The most prominent machine learning (ML) methods in use today are supervised, meaning they require ground-truth labeling of the data on which they are trained. Annotating data is arduous and expensive. Additionally, data sets for image object detection may be annotated by drawing polygons, drawing bounding boxes, or providing single points on targets. Selection of annotation technique is a tradeoff between time to annotate and accuracy of the annotation. When annotating a dataset for machine object recognition algorithms, researchers may not know the most advantageous method of annotation for their experiments. This paper evaluates the performance tradeoffs of three alternative methods of annotating imagery for use in ML. A neural network was trained using the different types of annotations and compares the detection accuracy of and differences between the resultant models. In addition to the accuracy, cost is analyzed for each of the models and respective datasets.

关键词： ANNOTATIONS Dataset tradeoffs bounding boxes Artificial Intelligence Machine Learning alternative methods computer Programming Languages drawing Drawing

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Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits 31

Incorporating Touch Biometrics to Mobile One-Time Passwords:...

引用

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

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab E-28049 Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work evaluates the advantages and potential of incorporating touch biometrics to mobile one-time passwords (OTP). The new e-BioDigit database, which comprises on-line handwritten numerical digits from 0 to 9, has been acquired using the finger touch as input to a mobile device. This database is used in the experiments reported in this work and it is publicly available to the research community. An analysis of the OTP scenario using handwritten digits is carried out regarding which are the most discriminative handwritten digits and how robust the system is when increasing the number of them in the user password. Additionally, the best features for each handwritten numerical digit are studied in order to enhance our proposed biometric system. Our proposed approach achieves remarkable results with EERs ca. 5.0% when using skilled forgeries, outperforming other traditional biometric verification traits such as the handwritten signature or graphical passwords on similar mobile scenarios.

关键词： Biometrics (access control) Databases Password Mobile handsets Feature extraction Protocols

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Cosmetic Features Extraction by a Single Image Makeup Decomposition 31

Cosmetic Features Extraction by a Single Image Makeup Decomp...

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

作者： Yamagishi, Kanami Yamamoto, Shintaro Kato, Takuya Morishima, Shigeo Waseda Univ Tokyo Japan Waseda Res Inst Sci & Engn Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, a large number of makeup images have been shared on social media. Most of these images lack information about the cosmetics used, such as color, glitter or etc., while they are difficult to infer due to the diversity of skin color or lighting conditions. In this paper, our goal is to estimate cosmetic features only from a single makeup image. Previous work has measured the material parameters of cosmetic products from pairs of images showing the face with and without makeup, but such comparison images are not always available. Furthermore, this method cannot represent local effects such as pearl or glitter since they adapted physically-based reflectance models. We propose a novel image-based method to extract cosmetic features considering both color and local effects by decomposing the target image into makeup and skin color using Difference of Gaussian (DoG). Our method can be applied for single, standalone makeup images, and considers both local effects and color. In addition, our method is robust to the skin color difference due to the decomposition separating makeup from skin. The experimental results demonstrate that our method is more robust to skin color difference and captures characteristics of each cosmetic product.

关键词： Image color analysis Feature extraction Dogs Skin Lighting computer vision pattern recognition

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DIY Human Action dataset Generation 31

DIY Human Action dataset Generation

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

作者： Khodabandeh, Mehran Joze, Hamid Reza Vaezi Zharkov, Ilya Pradeep, Vivek Simon Fraser Univ Burnaby BC Canada Microsoft Redmond WA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing dataset, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition datasets, that this new data generation technique can improve the performance of current action recognition neural nets.

关键词： Skeleton Gallium nitride Task analysis Trajectory Generators computer vision Training

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Surrogate Contrastive Network for Supervised Band Selection in Multispectral computer vision Tasks

Surrogate Contrastive Network for Supervised Band Selection ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Edgar A. Bernal University of Rochester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

computer vision techniques that operate on hyper- and multispectral imagery benefit from the additional amount of spectral information relative to those that exploit traditional RGB or monochromatic visual data. However, the increased volume of data to be processed brings about additional memory, storage and computational requirements. In order to address such limitations, a wide range of techniques for dimensionality reduction have been introduced by previous work. In this paper, we propose a framework for spectral band selection that is highly data- and computationally efficient. The method leverages a convolutional siamese network learned by optimizing a contrastive loss, and performs band selection based on the low-dimensional data embeddings produced by the network. We empirically demonstrate the efficacy of the method on an object detection task from aerial multispectral imagery. The results show that, in spite of the method's frugality, it produces very competitive band selection results against the evaluated competing techniques.

关键词： Task analysis Feature extraction Dimensionality reduction Training Object detection Network architecture computer architecture Object detection dimensionality reduction band selection task analysis NETWORK STRUCTURE computer Architecture Feature extraction Training Functional training images computer vision memory storage multispectral imagery

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Live Demonstration: Real-time VI-SLAM with High-Resolution Event Camera

Live Demonstration: Real-time VI-SLAM with High-Resolution E...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Gongyu Yang Qilin Ye Wanjun He Lifeng Zhou Xinyu Chen Lei Yu Wen Yang Shoushun Chen Wei Li Whuhan University CelePixel Technology Co. Ltd Shanghai Baolong Automotive Corporation

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Event camera is bio-inspired vision sensors that output pixel-level brightness changes asynchronously. Compared to the conventional frame-based camera, it is with high dynamic range, low latency and high sensitivity, and thus can be exploited in SLAM to tackle the problem of occasions with high-speed camera moving and low-light scenes. In this demo, we implement the visual-inerial SLAM in real time with the recently released event camera, namely, CeleX-V. With the feature of high spatial resolution (1280×800) and low latency (< 0.5μs), the proposed method can provide frames with abundant textures and high time response, which leads to more stable tracking ability and better performance in SLAM system.

关键词： supersonic low altitude missile Cameras vision sensors Cameras-High speed Asynchronous Time response Gatsos FRAMES Latency

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Robot Workspace Monitoring using a Blockchain-based 3D vision Approach

Robot Workspace Monitoring using a Blockchain-based 3D Visio...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Vasco Lopes Nuno Pereira Luis A. Alexandre Universidade da Beira Interior Instituto de Telecomunicacoes

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Blockchain has been used extensively for financial purposes, but this technology can also be beneficial in other contexts where multi-party cooperation, security and decentralization of the data is essential. Properties such as immutability, accessibility and non-repudiation and the existence of smart-contracts make blockchain technology very interesting in robotic contexts that require event registration or integration with Artificial Intelligence. In this paper, we propose a system that leverages blockchain as a ledger to register events and information to be processed by Oracles and uses smart-contracts to control robots by adjusting their velocity, or stopping them, if a person enters the robot working space without permission. We show how blockchain can be used in computer vision problems by interacting with multiple external parties, Oracles, that perform image analysis and how it is possible to use multiple smart-contracts for different tasks. The method proposed is shown in a scenario representing a factory environment, but since it is modular, it can be easily adapted and extended for other contexts, allowing for simple integration and maintenance.

关键词： Robot Workplace non-repudiation Leverage permission decentralization Artificial Intelligence images computer vision Modularity DNA Integration integration Persons image analysis stopping

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Feature Pyramid Network for Multi-Class Land Segmentation 31

Feature Pyramid Network for Multi-Class Land Segmentation

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

作者： Seferbekov, Selim Iglovikov, Vladimir Buslaev, Alexander Shvets, Alexey Veeva Syst D-60314 Frankfurt Germany Lyft Inc Engn Ctr Level5 Palo Alto CA 94304 USA Mapbox R&D Ctr Minsk 220030 BELARUS MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leader-board score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.

关键词： Image segmentation Satellites Training Feature extraction Convolution Semantics computer vision

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Priming Neural Networks 31

Priming Neural Networks

引用

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

作者： Rosenfeld, Amir Biparva, Mandi Tsotsos, John K. York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise, small size or atypical appearance. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.

关键词： Visualization Task analysis Object detection Neural networks Semantics computer vision Image segmentation

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