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- pattern recognition
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - END-to-END Photopleth YsmographY (PPG) Based Biometric Authentication by Using Convolutional Neural Networks
摘要： Whilst research efforts have traditionally focused on Electrocardiographic (ECG) signals and handcrafted features as potential biometric traits, few works have explored systems based on the raw photoplethysmogram (PPG) signal. This work proposes an end-to-end architecture to offer biometric authentication using PPG biosensors through Convolutional Networks. We provide an evaluation of the performance of our approach in two different databases: Troika and PulseID, the latter a publicly available database specifically collected by the authors for such a purpose. Our verification approach through convolutional network based models and using raw PPG signals appears to be viable in current monitoring procedures within e-health and fitness environments showing a remarkable potential as a biometry. The approach tested on a verification fashion, on trials lasting one second, achieved an AUC of 78.2% and 83.2%, averaged among target subjects, on PulseID and Troika datasets respectively. Our experimental results on previous small datasets support the usefulness of PPG extracted biomarkers as viable traits for multi-biometric or standalone biometrics. Furthermore, the approach results in a low input throughput and complexity that allows for a continuous authentication in real-world scenarios. Nevertheless, the reported experiments also suggest that further research is necessary to account for and understand sources of variability found in some subjects.
关键词： biometric verification,photoplethysmogram signal,convolutional neural networks,biometric authentication,ppg
更新于2025-09-23 15:19:57
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A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training
摘要： Mobile Laser Scanning (MLS) data of outdoor environment are typically characterised by occlusion, noise, clutter, large data size and high quantum of information which makes their classification a challenging problem. This paper presents three deep Convolutional Neural Network (CNN) architectures in three dimension (3D), namely single CNN (SCN), multi-faceted CNN (MFC) and MFC with reproduction (MFCR) for automatic classification of MLS data. The MFC uses multiple facets of an MLS sample as inputs to different SCNs, thus providing additional information during classification. The MFC, once trained, is used to reproduce additional samples with the help of existing samples. The reproduced samples are employed to further refine the MFC training parameters, thus giving a new method called MFCR. The three architectures are evaluated on an ensemble of 3D outdoor MLS data consisting of four classes, i.e. tree, pole, house and ground covered with low vegetation along with car samples from KITTI dataset. The total accuracy and kappa values of classifications reached up to (i) 86.0% and 81.3% for the SCN (ii) 94.3% and 92.4% for the MFC and (iii) 96.0% and 94.6% for the MFCR, respectively. The paper has demonstrated the use of multiple facets to significantly improve classification accuracy over the SCN. Finally, a unique approach has been developed for reproduction of samples which has shown potential to improve the accuracy of classification. Unlike previous works on the use of CNN for structured point cloud of indoor objects, this work shows the utility of different proposed CNN architectures for classification of varieties of outdoor objects, viz., tree, pole, house and ground which are captured as unstructured point cloud by MLS.
关键词： Sample reproduction,Mobile Laser Scanning (MLS),Automatic classification,Convolutional Neural Network (CNN)
更新于2025-09-19 17:15:36
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Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks
摘要： Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, Precision, Recall, Overall Error, F-score, and Accuracy.
关键词： landslide inventory mapping (LIM),multivariate morphological reconstruction (MMR),Change detection,deep convolutional network
更新于2025-09-19 17:15:36
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Unit panel node detection by CNN on FAST reflector
摘要： The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has an active reflector. During observations, the reflector will be deformed into a paraboloid 300 meters in diameter. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. The way to detect the nodes in the photos is the key problem in this application of photogrammetry. This paper applies a convolutional neural network (CNN) with candidate regions to detect the nodes in the photos. Experimental results show a high recognition rate of 91.5%, which is much higher than the recognition rate for traditional edge detection.
关键词： photogrammetry,FAST,telescopes,nodes detect,convolutional neural network
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) - Shenzhen, China (2018.10.25-2018.10.27)] 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) - RDS-Denoiser: a Detail-preserving Convolutional Neural Network for Image Denoising
摘要： Image noise is usually modeled as additive independent Gaussian random variables with fixed standard deviation, and most existing methods developed under this assumption have difficulties handling spatially varying noise. In this work, we aim to solve the problem of image denoising when the noise level is unknown. We propose a simple yet effective Stacked Denoising Networks. It decomposes the denoising process into two stages. The Stage-I Denoising is to predict the noise map of noisy image. The Stage-II Denoising to further improve the visual quality and alleviate overfitting to Gaussian noise. Experiments show that RDS-Denoiser achieves competitive performance comparing to state-of-the-art denoising methods. In addition, we propose RDS-GAN, a conditional generative adversarial network, to further improve the visual quality and alleviate overfitting to Gaussian noise.
关键词： Image Denoising,Convolutional Neural Networks,Conditional Generative Adversarial Networks
更新于2025-09-19 17:15:36
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image
摘要： As a kind of behavioral biometric feature, gait has been widely applied for human verification and identification. Approaches to gait recognition can be classified into two categories: model-free approaches and model-based approaches. Model-free approaches are sensitive to appearance changes. For model-based approaches, it is difficult to extract the reliable body models from gait sequences. In this paper, based on the robust skeleton points produced from a two-branch multi-stage CNN network, a novel model-based feature, Skeleton Gait Energy Image (SGEI), has been proposed. Relevant experimental performances indicate that SGEI is more robust to the cloth changes. Another contribution is that two different CNN-based architectures have been separately proposed for gait verification and gait identification. Both these two architectures have been evaluated on the datasets. They have presented satisfying performances and increased the robustness for gait recognition in the unconstrained environments with view variances and cloth variances.
关键词： Gait Identification,Gait Verification,Deep Convolutional Neural Networks,Skeleton Gait Energy Image
更新于2025-09-19 17:15:36
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Adversarial Context Aggregation Network for Low-Light Image Enhancement
摘要： Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
关键词： context aggregation,Low-light image enhancement,Convolutional neural network,generative adversarial network
更新于2025-09-19 17:15:36
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[IEEE 2018 7th European Workshop on Visual Information Processing (EUVIP) - Tampere, Finland (2018.11.26-2018.11.28)] 2018 7th European Workshop on Visual Information Processing (EUVIP) - A Hybrid Approach to Hand Detection and Type Classification in Upper-Body Videos
摘要： Detection of hands in videos and their classification into left and right types are crucial in various human-computer interaction and data mining systems. A variety of effective deep learning methods have been proposed for this task, such as region-based convolutional neural networks (R-CNNs), however the large number of their proposal windows per frame deem them computationally intensive. For this purpose we propose a hybrid approach that is based on substituting the 'selective search' R-CNN module by an image processing pipeline assuming visibility of the facial region, as for example in signing and cued speech videos. Our system comprises two main phases: preprocessing and classification. In the preprocessing stage we incorporate facial information, obtained by an AdaBoost face detector, into a skin-tone based segmentation scheme that drives Kalman filtering based hand tracking, generating very few candidate windows. During classification, the extracted proposal regions are fed to a CNN for hand detection and type classification. Evaluation of the proposed hybrid approach on four well-known datasets of gestures and signing demonstrates its superior accuracy and computational efficiency over the R-CNN and its variants.
关键词： region-based convolutional neural network (R-CNN),hand type classification,Hand detection,AdaBoost face detection,Kalman filtering
更新于2025-09-19 17:15:36
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Curvature Augmented Deep Learning for 3D Object Recognition
摘要： This paper presents a new method to incorporate shape information into convolutional neural network (CNN)s for 3D object recognition. Voxel CNNs have been very successful with the task of 3D object recognition. However, continuous shape information that is useful for recognition is often lost in their conversion to a voxel representation. We propose a single dimensional feature that can be applied to voxel CNNs. This paper presents a novel rotation-invariant feature based on mean curvature that improves shape recognition for voxel CNNs. We augment the recent voxel CNN Octnet architecture with our feature and demonstrate a 1% overall accuracy increase on the ModelNet10 dataset.
关键词： 3D Object Recognition,Convolutional Neural Networks,Computational Geometry,Deep Learning
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Information and Communication Technology Convergence (ICTC) - Jeju (2018.10.17-2018.10.19)] 2018 International Conference on Information and Communication Technology Convergence (ICTC) - Deep Inverse Tone Mapping Optimized for High Dynamic Range Display
摘要： The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.
关键词： inverse tone mapping,high dynamic range imaging,convolutional neural networks
更新于2025-09-19 17:15:36