This paper describes a novel approach to optimal kernel placement in kernel-based tracking. If kernels are placed at arbitrary places, kernel-based methods are likely to be trapped in ill-conditioned locations, which ...
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Visual tracking involves generating an inference about the motion of an object from measured image locations in a video sequence. In this paper we present a unified framework that incorporates shape and illumination i...
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This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within cvpr2020. The RePSS challenge was focused on measuring the average ...
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ISBN:
(纸本)9781728193601
This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within cvpr2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computervision. Our deep learning-based approach includes 3D spatio-temporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse and redundant representations over a tr...
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Tracking can be considered a two-class classification problem between the foreground object and its surrounding background. Feature selection to better discriminate object from background is thus a critical step to en...
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Simultaneous localisation and mapping using a single camera becomes difficult when erratic motions violate predictive motion models. This problem needs to be addressed when visual SLAM algorithms are transferred from ...
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We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. Ii furth...
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ISBN:
(纸本)0818684976
We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. Ii further uses sequences of images to obtain more reliable clustering. Finally, since our method relies on a non-Euclidean similarity measure we introduce algebraic techniques for estimating local properties of these surfaces without first embedding the images in a Euclidean space. We demonstrate our method by applying it to a large database of images.
Model order selection and cue combination are both difficult open problems in the area of clustering. In this work we build upon stability-based approaches to develop a new method for automatic model order selection a...
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Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by explori...
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ISBN:
(纸本)9781479943098
Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by exploring cognitive context, demonstrating how rich dynamic context provided by computational cognitive models can improve object recognition. We demonstrate the use cognitive context to improve recognition using a small database of objects.
In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images ...
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