- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2019
- 2018
- pattern recognition
- image
- partial discharge
- convolutional neural network(CNN)
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- Optoelectronic Information Science and Engineering
- Shanghai Jiao Tong University
- Ocean University of China
- University of Oulu
- Wuhan University
- Central South University
- Hubei University
-
Convolutional Neural Network Based Feature Extraction for IRIS Recognition
摘要： Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a high degree of assurance. Extracting good features is the most significant step in the iris recognition system. In the past, different features have been used to implement iris recognition system. Most of them are depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed system is investigated when extracting features from the segmented iris image and from the normalized iris image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the very high accuracy rate.
关键词： Iris,Recognition,Feature extraction (FE),Convolutional Neural Network (CNN),Deep learning,Biometrics
更新于2025-09-10 09:29:36
-
[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities
摘要： Extraction of retinal layers from optical coherence tomography (OCT) scans is critical for analyzing retinal anomalies and manual segmentation of these retinal layers is a very cumbersome task. Recently, deep learning has gained much popularity in medical image analysis due to its underlying precision and robustness. Many researchers have utilized deep learning for extracting retinal layers from OCT images. However, to the best of our knowledge, there is no literature available that presents a robust segmentation framework that is able to extract retinal layers from OCT scans having different retinal pathological syndromes. Therefore, this paper presents a deep convolutional neural network and structure tensor-based segmentation framework (CNN-STSF) for the fully automated segmentation of up to eight retinal layers from normal as well as diseased OCT scans. First of all, the proposed framework computes coherent tensor from the candidate scan through which retinal layers are extracted. Afterwards, the pixels representing the layers are further classified using cloud based deep convolutional neural network (CNN) model trained on 1,200 retinal layers patches. CNN model in the proposed framework computes the probability of each layer pixels and assign it to be part of that layer for which it has the highest probability. The proposed framework was tested and validated on more than 39,000 retinal OCT scans from different publicly available datasets and from local Armed Forces Institute of Ophthalmology (AFIO) dataset where it outperformed all the existing solutions by achieving the overall layer segmentation accuracy of 0.9375.
关键词： Transfer learning,Convolutional neural network (CNN),Deep learning,AlexNet
更新于2025-09-10 09:29:36
-
[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Low Resolution Cell Image Edge Segmentation Based on Convolutional Neural Network
摘要： An low-resolution cell images captured by a lens-free imaging system is presented in this paper. The resolution of this cell images is impacted by the low and experimental cell segmentation methods to solve the original cell images is not robust and sensitive to noise. So based on the convolutional neural network, an optimized CSnet method is proposed in this paper for automatically segmenting cell. In the proposed method, the produced data set will be sent into the convolutional neural network firstly for training to obtain an optimized convolution neural network segmentation model. And then, the pre-divided images acquired by the lens-free imaging system are loaded into the segmentation model to get the segmentation images. Finally, our proposed method in this paper is tested in a neural network framework built in keras. The experimental results show that the accuracy of our proposed method can reach about 96%. At the same time, it also can implement batch segmentation automatically and make the problem of heavy task for segmentation better.
关键词： convolutional neural network,cell segmentation,Lensfree imaging,microfluidic chip
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deconv R-CNN for Small Object Detection on Remote Sensing Images
摘要： Small object detection has drawn increasing interest in computer vision and remote sensing image processing. The Region Proposal Network (RPN) methods (e.g., Faster R-CNN) have obtained promising detection accuracy with several hundred proposals. However, due to the pooling layers in the network structure of the deep model, precise localization of small-size object is still a hard problem. In this paper, we design a network with a deconvolution layer after the last convolution layer of base network for small target detection. We call our model DeconvR-CNN. In the experiment on a remote sensing image dataset, DeconvR-CNN reaches a much higher mean average precision (mAP) than Faster R-CNN.
关键词： Object detection,Small object,Convolutional neural network,R-CNN,Deconvolution
更新于2025-09-09 09:28:46
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - CNN-Based Target Detection in Hyperspectral Imagery
摘要： This paper proposes a hyperspectral target detection framework with convolutional neural network (CNN). The number of training samples is first sufficiently enlarged by subtraction method to maximize the advantages of the multilayer CNN. Next, the CNN is given a target detection function by labelling the new pixels subtracted between target and background classes as 1, and the pixels subtracted between pixels within both the same and different background classes as 0. Finally, for each testing pixel, the difference between the central pixel and its adjacent pixels is input into the framework. If the testing pixel belongs to the target, the output score is close to the target label. Aircrafts and vehicles are selected as targets of interest in the experiment conducted to validate the proposed method. The experiment results show that the proposed method has an advantage over classic hyperspectral target detection algorithms in terms of precision and robustness.
关键词： Deep Learning,Convolutional Neural Network,Target Detection,Remote Sensing,Hyperspectral
更新于2025-09-09 09:28:46
-
[IEEE 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC) - Xi'an (2018.5.25-2018.5.27)] 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) - Hyperspectral Target Detection with CNN Using Subtraction Model
摘要： Recently, the convolutional neural network (CNN) has been widely used in the fields of hyperspectral image (HSI) processing. In this paper, a CNN-based hyperspectral target detection framework is presented. And subtraction model is used to sufficiently enlarge the number of training samples. The subtraction model is built from twenty-eight manually selected objects in several AVIRIS date following three aspects: 1) The new pixel made by subtraction of any two pixels between 27 different classes is labelled as 0; 2) the new pixel made by subtraction of any two pixels within per class is labelled as 0; 3) the new pixel made by subtraction of any two pixels, in which one pixel is from the target class and the other is from background classes, is labelled as 1. Theoretically, if the pixel under test belongs to the target class, the output label of the CNN will be the same as the label of the target class. The experiment results on three images all indicate that the proposed CNN-based detector outperforms the classical hyperspectral target detection algorithms.
关键词： target detection,convolutional neural network,deep learning,hyperspectral imagery
更新于2025-09-09 09:28:46
-
[IEEE 2018 International Conference on 3D Vision (3DV) - Verona (2018.9.5-2018.9.8)] 2018 International Conference on 3D Vision (3DV) - MVDepthNet: Real-Time Multiview Depth Estimation Neural Network
摘要： Although deep neural networks have been widely applied to computer vision problems, extending them into multiview depth estimation is non-trivial. In this paper, we present MVDepthNet, a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints. Multiview observations are encoded in a cost volume and then combined with the reference image to estimate the depth map using an encoder-decoder network. By encoding the information from multiview observations into the cost volume, our method achieves real-time performance and the flexibility of traditional methods that can be applied regardless of the camera intrinsic parameters and the number of images. Geometric data augmentation is used to train MVDepthNet. We further apply MVDepthNet in a monocular dense mapping system that continuously estimates depth maps using a single localized moving camera. Experiments show that our method can generate depth maps efficiently and precisely.
关键词： convolutional neural network,cost volume,monocular dense mapping,multiview depth estimation,geometric data augmentation
更新于2025-09-09 09:28:46
-
[IEEE 2018 Condition Monitoring and Diagnosis (CMD) - Perth, WA (2018.9.23-2018.9.26)] 2018 Condition Monitoring and Diagnosis (CMD) - Classification of Partial Discharge Images within DC XLPE Cables Based on Convolutional Deep Belief Network
摘要： The classification of partial discharge is of significance to diagnose the defects in high voltage cable insulation defect classification systems. To accuracy at DC cross linked polyethylene(XLPE) cables, a new method used images classification based on convolutional deep belief network (CDBN) is proposed in this paper. Firstly, four kinds of defects in XLPE cables are designed and tested under DC voltage. The q-Δt-n image is constructed based on PD signal collected by HFCT. Then the diagnostic CDBN model is constructed to extract the high-level detailed feature of q-Δt-n images with Gaussian visible units. Finally, classification experiments with CDBN, deep belief network(DBN), support vector machine(SVM) and back- propagation neural network(BPNN) is conducted. The experiment results show that the proposed method has higher classification accuracy of insulation defect diagnosis.
关键词： PD image,insulation defect,feature extraction,convolutional deep belief network(CDBN),DC cable
更新于2025-09-09 09:28:46
-
[IEEE 2018 Condition Monitoring and Diagnosis (CMD) - Perth, WA (2018.9.23-2018.9.26)] 2018 Condition Monitoring and Diagnosis (CMD) - Pattern Recognition of Partial Discharge Image Based on One-dimensional Convolutional Neural Network
摘要： Big data platforms and centers are ubiquitous today where a large amount of unstructured data on site such as is accumulated. For structured data, partial discharge pattern recognition method has been extensively studied, whereas traditional methods can not be directly applied to unstructured data. To this end, a time-domain waveform pattern recognition method based on one- dimensional convolutional neural network (CNN) is proposed. Image processing techniques are applied to obtain one- dimensional characteristics of the waveform. Based on deep learning, the network is constructed for pattern recognition straight forwardly. Through on site detection and simulation experiments, image data sets of five partial discharge defects are established and comparative experiments are conducted. Experimental results show that the proposed method can successfully perform pattern recognition with applications in work of data mining and data utilization. Under the same complexity, it is also with higher accuracy comparing to two- dimensional CNN. Furthermore, the method autonomously extrapolates features without manual extraction, which achieves low experimental complexity and robustness simultaneously.
关键词： pattern recognition,image,partial discharge,convolutional neural network(CNN)
更新于2025-09-09 09:28:46
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Only-Reference Video Quality Assessment for Video Coding Using Convolutional Neural Network
摘要： Conventional video quality assessment methods are either full-, reduced-, or no-reference methods that need to access decoded videos. Hence, to calculate quality of decoded video in video coding regarding an image/video quality metric, complete encoding and decoding have to executed, which is computationally expensive. To address this problem, we propose to estimate quality of decoded videos from the original video only (i.e., only-reference) using convolutional neural network, as if the original video is encoded using a range of quantization parameter. The proposed network is shallow and can be trained to estimate various video quality metrics. Furthermore, among potential rate control applications using the proposed network, we demonstrate achieving a targeted decoded-video quality by selecting a proper quantization parameter before actually encoding.
关键词： only-reference,Video quality assessment,convolutional neural network,video coding
更新于2025-09-09 09:28:46