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检索条件"任意字段=2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014"

共 4555 条记录，以下是61-70 订阅

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

引用

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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A High-Performance Hardware Architecture for a Frameless Stereo vision Algorithm Implemented on a FPGA Platform

A High-Performance Hardware Architecture for a Frameless Ste...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Eibensteiner, Florian Kogler, Juergen Scharinger, Josef Upper Austria Univ Appl Sci Softwarepk 11 A-4232 Hagenberg Austria Austrian Inst Technol GmbH AIT A-1220 Vienna Austria Johannes Kepler Univ Linz A-4040 Linz Austria

ISBN: (纸本)9781479943098

As a novelty, in this paper we present an event-based stereo vision matching approach based on time-correlation using segmentation to restrict the matching process to active image areas, exploiting the event-driven behavior of a silicon retina sensor. Stereo matching is used in depth generating camera systems for solving the correspondence problem and reconstructing 3D data. Using conventionally frame-based cameras, this correspondence problem is a time consuming and computationally expensive task. To overcome this issue, embedded systems can be used to speed up the calculation of stereo matching results. The silicon retina delivers asynchronous events if the illumination changes instead of synchronous intensity or color images. It provides sparse input data and therefore the output of the stereo vision algorithm (depth map) is also sparse. The high temporal resolution of such event-driven sensors leads to high data rates. To handle these and the correspondence problem in real time, we implemented our stereo matching algorithm for a field programmable gate array (FPGA). The results show that our matching criterion, based on the time of occurrence of an event, leads to a small average distance error and the parallel hardware architecture and efficient memory utilization results in a frame rate of up to 1140fps.

关键词： Stereo image processing

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A Mobile vision System for Fast and Accurate Ellipse Detection

A Mobile Vision System for Fast and Accurate Ellipse Detecti...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Fornaciari, Michele Cucchiara, Rita Prati, Andrea Univ Modena & Reggio Emilia Via Vignolese 905 Modena Italy Univ IUAV Venice I-1957 Venice Italy

ISBN: (纸本)9780769549903

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. This demo is based on a novel algorithm for fast and accurate ellipse detection. The proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The demo will show it working on a commercial smart-phone.

关键词： Hough transforms computer vision smart phones

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Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras 32

Asynchronous Convolutional Networks for Object Detection in ...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cannici, Marco Ciccone, Marco Romanoni, Andrea Matteucci, Matteo Politecn Milan Milan Italy

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption. Becoming available only very recently, a limited amount of work addresses object detection on these devices. In this paper we propose two neural networks architectures for object detection: YOLE, which integrates the events into surfaces and uses a frame-based model to process them, and fcYOLE, an asynchronous event-based fully convolutional network which uses a novel and generalformalization of the convolutional and max pooling layers to exploit the sparsity of camera events. We evaluate the algorithm with different extensions of publicly available datasets, and on a novel synthetic dataset.

关键词： Object detection Cameras Neuromorphics Surface treatment Neural networks computer architecture Feature extraction

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vision-based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization

Vision-based Lane Analysis: Exploration of Issues and Approa...

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Satzoda, R. K. Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab San Diego CA 92103 USA

ISBN: (纸本)9780769549903

Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems' constraints. The proposed lane feature extraction process is evaluated in detail using real world lane data, to explore its effectiveness for embedded realization and adaptability to varying contextual information like lane types and environmental conditions.

关键词： computer vision embedded systems feature extraction traffic engineering computing

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

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

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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Rotated Rectangles for Symbolized Building Footprint Extraction 31

Rotated Rectangles for Symbolized Building Footprint Extract...

引用

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

作者： Dickenson, Matt Gueguen, Lionel UBER Technol Inc San Francisco CA 94103 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Building footprints (BFP) provide useful visual context for users of digital maps when navigating in space. This paper proposes a method for extracting and symbolizing building footprints from satellite imagery using a convolutional neural network (CNN). The CNN architecture outputs rotated rectangles, providing a symbolized approximation that works well for small buildings. Experiments are conducted on the four cities in the DeepGlobe Challenge dataset (Las Vegas, Paris, Shanghai, Khartoum). Our method performs best on suburbs consisting of individual houses. These experiments show that either large buildings or buildings without clear delineation produce weaker results in terms of precision and recall.

关键词： Buildings computer architecture Microprocessors Architecture Urban areas computer vision Satellites

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

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

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Deep Fusion of Appearance and Frame Differencing for Motion ...

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

作者： Ellenfeld, Marc Moosbauer, Sebastian Cardenes, Ruben Klauck, Ulrich Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Hensoldt Analyt GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany Univ Western Cape Cape Town South Africa

ISBN: (纸本)9781665448994

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene's motion independent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.

关键词： computer vision Image segmentation Three-dimensional displays Fuses Motion segmentation Cameras pattern recognition

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

引用

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

作者： Yang, Ren Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665448994

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.

关键词： computer vision Databases conferences Benchmark testing Solids pattern recognition

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