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- 2019
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- pattern recognition
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[IEEE 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) - Enschede (2018.8.26-2018.8.29)] 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) - Application of Convolutional Neural Networks to Femur Tracking in a Sequence of X-Ray Images
摘要： A path along which the human knee joint moves can be estimated from real-time moving images or a sequence of static images. In case of many algorithms solving this problem, it is essential to locate the characteristic points (i.e., key-points) on each image and find the correspondence between them in the image sequence. In this paper we present an algorithm, which detects such key-points facilitating effective femur tracking in a sequence of X-ray images. We use a set of X-ray images manually labeled with the key-point positions, to train a Convolutional Neural Network (CNN) for the purposes of solving a regression task corresponding to finding key-point positions in previously unknown images. CNN hyper-parameters such as number of convolutions and layers, learning rate, regularization parameters, and activation functions were optimized using a tree of Parzen estimators guiding the process of training multiple models. Results for models with the best mean-square estimation error computed for a validation set and lowest structural complexity are presented. Key-point positions predicted by the CNN are on par with human predictions, even though the actual key-point position is ambiguous in some cases. The feasibility of detected key-points for femur tracking has been verified by several case studies.
关键词： key-point detection,femur tracking,medical image analysis,X-ray images,Convolutional Neural Networks
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Skip-Connected Deep Convolutional Autoencoder for Restoration of Document Images
摘要： The denoising and deblurring of images are the two essential restoration tasks in the document image processing task. As the preprocessing stages of the processing pipeline, the quality of denoising and deblurring heavily influences the result of subsequent tasks, such as character detection and recognition. In this paper, we propose a novel neural method for restoring document images. We named our network Skip-Connected Deep Convolutional Autoencoder (SCDCA), which is composed of multiple layers of convolution followed by a batch normalization layer and the leaky rectified linear unit (LeakyReLU) activation function. Inspired by the idea of residual learning, we use two types of skip connections in the network. One is identity mapping between convolution layers and the other is used to connect the input and output. Through these connections, the network learns the residual between the noisy and clean images instead of learning an ordinary transformation function. We empirically evaluate our algorithm on an open and challenging document images dataset. We also assess our restoring results using the optical character recognition (OCR) test. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.
关键词： residual learning,convolutional autoencoder,denoising,deblurring,skip connections,deep learning,document image restoration
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments
摘要： Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that later can be used by the navigation system. In this paper, we first present an efficient multi-sensor fusion approach based on the probabilistic data association method in order to achieve accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from four main sensors: radar, LiDAR, RGB camera and infrared camera. It generates object region proposals based on the fused detection result. Then, a Convolutional Neural Network (CNN) approach is used to identify the object categories within these regions. The CNN is trained on a real dataset from different ferry driving scenarios. The experimental results of tracking and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments.
关键词： maritime environment,object detection,convolutional neural networks,region proposals,autonomous vessel,multi-sensor fusion
更新于2025-09-09 09:28:46
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Measuring Oxygen Saturation with Smartphone Cameras using Convolutional Neural Networks
摘要： Arterial oxygen saturation (SaO2) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO2 is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO2. In this paper, we propose methods for the measurement of SaO2 with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) from a medical pulse oximeter.
关键词： Mobile Sensing,Convolutional Neural Networks,Oxygen Saturation
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition
摘要： Reservoir computing has attracted much attention for its easy training process as well as its ability to deal with temporal data. A reservoir computing system consists of a reservoir part represented as a sparsely connected recurrent neural network and a readout part represented as a simple regression model. In machine learning tasks, the reservoir part is fixed and only the readout part is trained. Although reservoir computing has been mainly applied to time series prediction and recognition, it can be applied to image recognition as well by considering an image data as a sequence of pixel values. However, to achieve a high performance in image recognition with raw image data, a large-scale reservoir including a large number of neurons is required. This is a bottleneck in terms of computer memory and computational cost. To overcome this bottleneck, we propose a new method which combines reservoir computing with untrained convolutional neural networks. We use an untrained convolutional neural network to transform raw image data into a set of smaller feature maps in a preprocessing step of the reservoir computing. We demonstrate that our method achieves a high classification accuracy in an image recognition task with a much smaller number of trainable parameters compared with a previous study.
关键词： Reservoir computing,Image recognition,Untrained networks,Convolutional neural networks
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - A Light CNN based Method for Hand Detection and Orientation Estimation
摘要： Hand detection is an essential step to support many tasks including HCI applications. However, detecting various hands robustly under conditions of cluttered backgrounds, motion blur or changing light is still a challenging problem. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection yet at a high computational expense. In this paper, we propose a light CNN network, which uses a modified MobileNet as the feature extractor in company with the SSD framework to achieve a robust and fast detection of hand location and orientation. The network generates a set of feature maps of various resolutions to detect hands of different sizes. In order to improve the robustness, we also employ a top-down feature fusion architecture that integrates context information across levels of features. For an accurate estimation of hand orientation by CNN, we manage to estimate two orthogonal vectors’ projections along the horizontal and vertical axes then recover the size and orientation of a bounding box exactly enclosing the hand. Evaluated on the challenging Oxford hand dataset, our method reaches 83.2% average precision (AP) at 139 FPS on a Nvidia Titan X, outperforming the previous methods both in accuracy and efficiency.
关键词： convolutional neural network,hand detection,orientation estimation
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Single Shot Feature Aggregation Network for Underwater Object Detection
摘要： The rapidly developing ocean exploration and observation make the demand for underwater object detection become increasingly urgent. Recently, deep convolutional neural networks (CNN) have shown strong ability in feature representation and CNN-based detectors also achieve remarkable performance, but still facing the big challenge when detecting multi-scale objects in a complex underwater environment. To address this challenge, we propose a novel underwater object detector, introducing multi-scale features and complementary context information for better classification and location ability. In the auto-grabbing contest of 2017 Underwater Robot Picking Contest sponsored by National Natural Science Foundation of China (NSFC), we won the 1-st place by using proposed method for real coastal underwater object detection.
关键词： context information,multi-scale features,underwater object detection,deep convolutional neural networks
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - FV-Net: learning a finger-vein feature representation based on a CNN
摘要： Finger vein pattern has been proven to be an effective biometric for personal identification in recent years. Nevertheless, there remain challenges that need to be solved, such as finger-vein features that lack robustness and expressiveness. In this paper, we propose a deep convolutional neural network (CNN) model, named the Finger-vein Network (FV-Net), to learn the features representative of a finger vein that is more discriminative and robust than handcrafted features. Next, to address the issue of translation and rotation in vein imaging, we propose a template-like matching strategy while designing the top architecture of the FV-net to extract features with spatial information. Finally, the extensive experimental results show that our proposed method can achieve excellent performance on several public datasets.
关键词： feature representation,deep learning,convolutional neural network,finger vein verification
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
摘要： We propose a deep bilinear model for blind image quality assessment (BIQA) that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNN), specializing in the two distortion scenarios separately. For synthetic distortions, we first pre-train a CNN to classify the distortion type and level of an input image, whose ground truth label is readily available at a large scale. For authentic distortions, we make use of a pre-train CNN (VGG-16) for the image classification task. The two feature sets are bilinearly pooled into one representation for a final quality prediction. We fine-tune the whole network on target databases using a variant of stochastic gradient descent. Extensive experimental results show that the proposed model achieves state-of-the-art performance on both synthetic and authentic IQA databases. Furthermore, we verify the generalizability of our method on the large-scale Waterloo Exploration Database, and demonstrate its competitiveness using the group maximum differentiation competition methodology.
关键词： Blind image quality assessment,convolutional neural networks,bilinear pooling,perceptual image processing,gMAD competition
更新于2025-09-09 09:28:46
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CoinNet: Copy Initialization Network for Multispectral Imagery Semantic Segmentation
摘要： Remote sensing imagery semantic segmentation refers to assigning a label to every pixel. Recently, deep convolutional neural networks (CNNs)-based methods have presented an impressive performance in this task. Due to the lack of sufficient labeled remote sensing images, researchers usually utilized transfer learning (TL) strategies to fine tune networks which were pretrained in huge RGB-scene data sets. Unfortunately, this manner may not work if the target images are multispectral/hyperspectral. The basic assumption of TL is that the low-level features extracted by the former layers are similar in most data sets, hence users only require to train the parameters in the last layers that are specific to different tasks. However, if one should use a pretrained deep model imagery in RGB data for multispectral /hyperspectral semantic segmentation, the structure of the input layer has to be adjusted. In this case, the first convolutional layer has to be trained using the multispectral /hyperspectral data sets which are much smaller. Apparently, the feature representation ability of the first convolutional layer will decrease and it may further harm the following layers. In this letter, we propose a new deep learning model, COpy INitialization Network (CoinNet), for multispectral imagery semantic segmentation. The major advantage of CoinNet is that it can make full use of the initial parameters in the pretrained network’s first convolutional layer. Comparison experiments on a challenging multispectral data set have demonstrated the effectiveness of the proposed improvement. The demo and a trained network will be published in our homepage.
关键词： deep convolutional network,CoinNet,transfer learning (TL),semantic segmentation
更新于2025-09-09 09:28:46