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).
This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use...
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ISBN:
(纸本)0818672587
This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilinear. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computervision in road traffic scenes. The limitations of the algorithms are also addressed in the paper.
Pain is a critical sign in many medical situations and its automatic detection and recognition using computervision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the pro...
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ISBN:
(纸本)9781467367592
Pain is a critical sign in many medical situations and its automatic detection and recognition using computervision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the proposed system in this paper employs steerable and separable filters to measures energies released by the facial muscles during the pain process. The proposed system not only detects the pain but recognizes its level. Experimental results on the publicly available pain database of UNBC show promising outcome for automatic pain detection and recognition.
In this paper, we present a generative model based approach to solve the multi-view stereo problem. The input images are considered to be generated by either one of two processes: (i) an inlier process, which generate...
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We present a novel discriminative-generative hybrid approach in this paper, with emphasis on application in multi-view object detection. Our method includes a novel generative model called Random Attributed Relational...
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This paper describes a general methodology for automated recognition of complex human activities. The methodology uses a context-free grammar (CFG) based representation scheme to represent composite actions and intera...
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In this paper, a novel cultural event classification algorithm based on convolutional neural networks is proposed. The proposed method firstly extracts regions that contain meaningful information.. Then, convolutional...
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ISBN:
(纸本)9781467367592
In this paper, a novel cultural event classification algorithm based on convolutional neural networks is proposed. The proposed method firstly extracts regions that contain meaningful information.. Then, convolutional neural networks are trained to classify the extracted regions. The final classification of a scene is performed by combining the classification results of each extracted region of the scene probabilistically. Compared to the state-of-the-art methods for classifying Chalearn Looking at People cultural event recognition database, the proposed methods shows competitive results.
We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two po...
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In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the stares of the HMM. Development of the parametric hidden Markov m...
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ISBN:
(纸本)0818684976
In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the stares of the HMM. Development of the parametric hidden Markov model (PHMM) was motivated by the task of simultaneously recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. The original PHMM approach assumes a linear dependence of output density means on the global parameter In this paper we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. We show a generalized expectation-maximization (GEM) algorithm for training the PHMM and a GEM algorithm hm to simultaneously recognize the gesture and estimate the value of the parameter We present results on a pointing gesture, where the nonlinear approach permits the natural! azimuth/elevation parameterization of pointing direction.
We demonstrate that is it possible to automatically find representative example images of a specified object category These canonical examples are perhaps the kind of images that one would show a child to teach them w...
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ISBN:
(纸本)9781424439942
We demonstrate that is it possible to automatically find representative example images of a specified object category These canonical examples are perhaps the kind of images that one would show a child to teach them what, for example a horse is - images with a large object clearly separated from the background. Given a large collection of images returned by a web search for an object category, our approach proceeds without an), user supplied training data for the category. First images are ranked according to a category independent composition model that predicts whether the), contain a large clearly depicted object, and outputs an estimated location of that object. Then local features calculated on the proposed object regions are used to eliminate images not distinctive to the category, and to cluster images by similarity of object appearance. We present results and a user evaluation on a variety of object categories, demonstrating the effectiveness of the approach.
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