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- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
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[Advances in Intelligent Systems and Computing] Emerging Trends in Expert Applications and Security Volume 841 (Proceedings of ICETEAS 2018) || Exploring Open Source for Machine Learning Problem on Diabetic Retinopathy
摘要： Open-source operating system, as well as its packages, is more powerful and secure than the proprietary sources. In the proprietary source, software source code is not easily available because it is secret; by contrast in the open-source operating system source code is easily available, so any programmer can change the code and implement their ideas and modify it because of its openness. Also, one major advantage is that we do not need to spend a huge amount of money on the software. So, in this paper, we used open-source software for coding purposes and looked at the data available on the UCI machine learning repository on the diabetic retinopathy.
关键词： Neural network,Open-source,Boosted decision tree,Diabetic retinopathy
更新于2025-09-10 09:29:36
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[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 - A Classification Method for Polsar Images using SLIC Superpixel Segmentation and Deep Convolution Neural Network
摘要： Deep convolution neural networks (DCNN) have been successfully introduced in the field of Polarimetric SAR image classification. However, the commonly used DCNN will classify each pixel in the image and neglect the fact that neighboring pixels may have similar intensity. Besides, the fixed size input in DCNN cannot be well adopted in remote sensing image which includes a great deal of different-scale information. Thus, superpixel segmentation (SS) and the input pyramid are introduced in this paper to improve the performance of DCNN. The former will guide the DCNN to classify superpixel instead of single pixel and the latter will include different-scale information around the pixel. Experiments carried out on two scenes of ALOS-2 PALSAR-2 POLSAR images demonstrate that the introduced technic can help DCNN achieve good accuracy and smooth boundary adherence with highly efficiency.
关键词： superpixel segmentation,convolution neural network,Polarimetric synthetic aperture radar
更新于2025-09-10 09:29:36
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Deep learning optical-sectioning method
摘要： Current optical-sectioning methods require complex optical system or considerable computation time to improve imaging quality. Here we propose a deep learning-based method for optical sectioning of wide-field images. This method only needs one pair of contrast images for training to facilitate reconstruction of an optically sectioned image. The removal effect of background information and resolution that is achievable with our technique is similar to traditional optical-sectioning methods, but offers lower noise levels and a higher imaging depth. Moreover, reconstruction speed can be optimized to 14 Hz. This cost-effective and convenient method enables high-throughput optical sectioning techniques to be developed.
关键词： wide-field imaging,optical sectioning,deep learning,convolutional neural network,background suppression
更新于2025-09-10 09:29:36
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Machine learning analysis of extreme events in optical fibre modulation instability
摘要： A central research area in nonlinear science is the study of instabilities that drive extreme events. Unfortunately, techniques for measuring such phenomena often provide only partial characterisation. For example, real-time studies of instabilities in nonlinear optics frequently use only spectral data, limiting knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, a supervised neural network is trained to correlate the spectral and temporal properties of modulation instability using simulations, and then applied to analyse high dynamic range experimental spectra to yield the probability distribution for the highest temporal peaks in the instability field. We also use unsupervised learning to classify noisy modulation instability spectra into subsets associated with distinct temporal dynamic structures. These results open novel perspectives in all systems exhibiting instability where direct time-domain observations are difficult.
关键词： machine learning,extreme events,optical fibre modulation instability,unsupervised learning,supervised neural network
更新于2025-09-10 09:29:36
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[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) - Temporal Attention Network for Action Proposal
摘要： Temporal action proposal, which extracts segments of interests from untrimmed video, is an important step for video analysis. For state-of-the-art temporal action proposal methods, average pooling is often used to aggregate features in deep neural networks, which inevitably ignores the significances of different video clips. Therefore, we propose a Temporal Attention Network (TAN) model to address this issue. Temporal attention with fully connected layers is introduced to adaptively combine clip-level features and form a compact and discriminative video representation. In addition, we show that the learned attention weights could also be used as an effective temporal feature to further improve the performance. Extensive experiments on THUMOS-14 demonstrate that our algorithm performs favorably against the state-of-the-art methods.
关键词： untrimmed video analysis,Temporal action proposal,neural network,temporal attention
更新于2025-09-10 09:29:36
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[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 - Deep Tensor Factorization for Hyperspectral Image Classification
摘要： High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral datasets show the superiority of the proposed method.
关键词： Hyperspectral image (HSI) classification,feature extraction,convolutional neural network (CNN),tensor decomposition
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Enhanced Interactive Remote Sensing Image Retrieval with Scene Classification Convolutional Neural Networks Model
摘要： In this paper, we address the semantic gap problem in high-spatial resolution remote sensing images retrieval. We propose a useful semantic image representation that improves the understanding of the machine with respect to the human perception. We use a remote sensing scene classification Convolutional Neural Network (CNN) model to detect the semantic concepts. The similarity distance is calculated to retrieve the most similar images to the given query image. Then, to improve the performance of the retrieval results, a relevance feedback phase has been proposed, which ensures that the final result corresponds to the user need. Our proposal shows promising results and improves the retrieved quality with respect to state-of–the-art approaches.
关键词： scene classification,semantic image retrieved,relevance feedback,Remote sensing,convolutional neural network
更新于2025-09-10 09:29:36
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[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 - Shadow Tracking of Moving Target Based on CNN for Video SAR System
摘要： Fast Moving targets always are shifted or smeared outside the scene in different images sequence to make video by Circle Synthetic Aperture Radar (SAR).In this paper, a novel moving target tracking approach with the shadow detection and tracking (SDT) is presented based on Convolution Neural Network. Based on the shadow characteristic of moving target in SAR imagery, CNN tracking classification is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets. By the simulation experiments and performance analysis, the validity of the proposed algorithm can be demonstrated. Real data set processing results are provided to demonstrate the effectiveness of the proposed approach.
关键词： Video SAR,Convolution Neural Network,Shadow detection,Moving target tracking
更新于2025-09-10 09:29:36
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Convolutional Neural Network Trained by Joint Loss for Hyperspectral Image Classification
摘要： In this letter, is proposed the hyperspectral image classification method based on the convolutional neural network, which is trained jointly by the reconstruction and discriminative loss functions. In the network, small convolutional kernels are cascaded with the pooling operator to perform feature abstraction, and a decoding channel composed of the deconvolutional and unpooling operators is established. The unsupervised reconstruction, performed by the decoding channel, not only introduces priors to the network training but also is made use to enhance the discriminability of the abstracted features by the control gate. By the experiments, it is shown that the proposed method performs better than the state-of-the-art neural network-based classification methods.
关键词： Control gate,unsupervised reconstruction,convolutional neural network (CNN),joint loss (JL),hyperspectral image (HSI) classification
更新于2025-09-10 09:29:36
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[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) - Learning Based Segmentation of Skin Lesion from Dermoscopic Images
摘要： Segmentation is the pre-requisite process in most of the computer aided diagnosis systems for medical imaging. Presence of different artifacts makes segmentation of skin lesion very difficult. Abnormal growth of artifacts can appear as false positives and can degrade the performance of the diagnosis systems. It can be avoided only when false structures are removed while extracting the lesion. To address this issue, this paper proposes deep leaning for skin lesion segmentation. Within this framework, automated skin lesion segmentation is proposed which achieves high accuracy segmentation of skin lesion. Our proposed architecture is 31 layers deep with same filter size. The validity of the proposed techniques is tested on two publically available databases of PH2 and ISIC 2017. Experimental results show the efficiency of the proposed approaches. The proposed method gives Dice Coefficient of 92.3% for PH2 Dataset while Dice Coefficient of 85.5% for ISIC 2017 Dataset.
关键词： Dice Coefficient,Deep Learning,Melanoma,Dermoscopy,Automatic segmentation,Convolution Neural Network
更新于2025-09-10 09:29:36