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FRESCO: Fast Radiometric Egocentric Screen Compensation

FRESCO: Fast Radiometric Egocentric Screen Compensation

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Matthew Post Paul Fieguth Mohamed A. Naiel Zohreh Azimifar Mark Lamm Vision and Image Processing Lab University of Waterloo Shiraz University Christie Digital Systems Canada Inc

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Existing radiometric compensation methods for projector-camera systems have been shown to produce compensated colours which are inconsistent to a human viewer. In this paper, a novel radiometric compensation method for projector-camera systems and textured surfaces is introduced based on the human visual system (HVS) colour response. The proposed method can extend established compensation methods to produce colours which are human-perceived to be correct (egocentric modelling). As a result, this method performs radiometric compensation which is not only consistent and precise, but also produces images which are visually accurate to an external colour reference. This method is shown to produce generally the lowest average radiometric compensation error when compared to compensation performed using only the response of a camera, demonstrated through quantitative analysis of compensated colours, and supported by qualitative results.

关键词： compensation method Radiation compensated Color Error compensation Human Cameras QUANTITATIVE ANALYSIS Inconsistent

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DIY Human Action dataset Generation 31

DIY Human Action dataset Generation

引用

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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Feature Pyramid Network for Multi-Class Land Segmentation 31

Feature Pyramid Network for Multi-Class Land Segmentation

引用

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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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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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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Text Image Super-resolution by Image Matting and Text Label Supervision

Text Image Super-resolution by Image Matting and Text Label ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Kai Lin Yubao Liu Thomas H. Li Shan Liu Ge Li School of Electronic and Computer Engineering Peking University Tencent America

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

These days, many methods have been proposed to deal with nature image super-resolution (SR) and get impressive performance. However, these methods don't do well in text images SR due to their ignorance of the difference between nature images and text images. In this paper, we propose a matting-based dual generative adversarial network (mdGAN) for text image SR. Firstly, the input image is decomposed into text, foreground and background layers using deep image matting. Then two parallel branches are constructed to recover text boundary information and color information respectively. Furthermore, in order to improve the restoration accuracy of characters in output image, we use the input image's corresponding ground truth text label as extra supervise information to refine the two-branch networks during training. Experiments on real text images demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.

关键词： texts images Image Imagery (Psychotherapy) chromatic information super-resolution

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Online Reconstruction of Indoor Scenes with Local Manhattan Frame Growing

Online Reconstruction of Indoor Scenes with Local Manhattan ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Mahdi Yazdanpour Guoliang Fan Weihua Sheng School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

We propose an efficient approach for robust reconstruction of indoor scenes by taking advantage of the geometric relation between consecutive Manhattan keyframes and local pose refinement to improve the accuracy and fidelity of the reconstructed models. At the core of our framework, we have a Local Manhattan Frame Growing system, which finds the principal directions of the scene and aligns point clouds with the dominant plane, and a Local Pose Optimization, which refines the pose estimation for a specific range of frames. During the reconstruction process, we use Manhattan keyframes for a planar pre-alignment to provide a robust initialization for the final surface registration. All Manhattan keyframes are integrated using a frame-to-model scheme to create local models based on the refined camera poses. The final dense model is reconstructed by adopting a geometric registration between local segments and integrating them into a global frame. The experimental results show the effectiveness of our approach to reduce the cumulative registration error and overall geometric drift.

关键词： Manhattan Frame FRAMES measuring datum error Reconstructive Surgical Procedures Fidelity Clouds

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Image Caption Generation with Hierarchical Contextual Visual Spatial Attention 31

Image Caption Generation with Hierarchical Contextual Visual...

引用

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

作者： Khademi, Mahmoud Schulte, Oliver Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on two-dimensional context, by selecting or ignoring its input. The Grid LSTM has not been applied to image caption generation task before. Another novel aspect is that we leverage a set of local region-grounded texts obtained by transfer learning. The region-grounded texts often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word, while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on MS-COCO dataset show that our architecture outperforms the state-of-the-art.

关键词： Feature extraction Visualization Logic gates computer architecture Task analysis Context modeling Computational modeling

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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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Robust Aleatoric Modeling for Future Vehicle Localization

Robust Aleatoric Modeling for Future Vehicle Localization

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Max Hudnell True Price Jan-Michael Frahm University of North Carolina at Chapel Hill

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The task of 2D object localization prediction, or the estimation of an object's future location and scale in an image, is a developing area of computer vision research. An accurate prediction of an object's future localization has the potential for drastically improving critical decision making systems. In particular, an autonomous driving system's collision prevention system could make better-informed decisions in the presence of accurate localization predictions for nearby objects (i.e. cars, pedestrians, and hazardous obstacles). Improving the accuracy of such localization systems is crucial to passenger/pedestrian safety. This paper presents a novel technique for determining future bounding boxes, representing the size and location of objects - and the predictive uncertainty of both aspects - in a transit setting. We present a simple feed-forward network for robust prediction as a solution of this task, which is able to generate object locality proposals by making use of an object's previous locality information. We evaluate our method against a number of related approaches and demonstrate its benefits for vehicle localization, and different from previous works, we propose to use distribution-based metrics to truly measure the predictive efficiency of the network-regressed uncertainty models.

关键词： images computer vision vehicle

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