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- 2019
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- pattern recognition
- image
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- convolutional neural network(CNN)
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- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Deep Learning Models for PET Scatter Estimations
摘要： Projection data acquired from a positron emission tomography (PET) scanner consist of true, scattered and random events. Scattered events can cause severe artifacts and quantitation errors in reconstructed PET images unless corrected for properly. A scatter correction algorithm is required to predict scattered events from the measurement. Scatter correction requires estimation of both single scatter and multiple scatter profiles. Usually, single scatter profiles are calculated by model-based simulation and multiple scatter profiles are estimated by a kernel-based convolution method. However, design of the convolution kernels for multiple scatter estimation is sophisticated and requires fine parameter tuning. In this work, we adopt deep learning techniques for scatter estimation. We propose two convolutional neural networks. The first network estimates multiple scatter profiles from single scatter profiles, replacing the kernel-based convolution method. The second network is designed to predict the total scatter profiles (including single and multiple scatters) directly from the input of emission and attenuation sinograms. Initial results from both networks show a promise with the potential for more accurate and faster scatter correction for PET.
关键词： Monte Carlo Simulation,Deep Learning,Scatter Estimation,Convolutional Neural Networks,PET
更新于2025-09-09 09:28:46
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Collaborative Classification of Hyperspectral and LIDAR Data Using Unsupervised Image-to-Image CNN
摘要： Currently, how to efficiently exploit useful information from multi-source remote sensing data for better Earth observation becomes an interesting but challenging problem. In this paper, we propose an collaborative classification framework for hyperspectral image (HSI) and Light Detection and Ranging (LIDAR) data via image-to-image convolutional neural network (CNN). There is an image-to-image mapping, learning a representation from input source (i.e., HSI) to output source (i.e., LIDAR). Then, the extracted features are expected to own characteristics of both HSI and LIDAR data, and the collaborative classification is implemented by the deep CNN. Experimental results on two real remote sensing data sets demonstrate the effectiveness of the proposed framework.
关键词： Hyperspectral Image,Convolutional Neural Network,Data Fusion,Deep Learning
更新于2025-09-09 09:28:46
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Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
摘要： The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today’s transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.
关键词： Aerial imagery,wavelet transform,autonomous driving,traffic monitoring,remote sensing,fully convolutional neural networks (FCNNs),lane-marking segmentation,infrastructure monitoring,mapping
更新于2025-09-09 09:28:46
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Light Field Spatial Super-Resolution Using Deep Efficient Spatial-Angular Separable Convolution
摘要： Light ?eld (LF) photography is an emerging paradigm for capturing more immersive representations of the real-world. However, arising from the inherent trade-off between the angular and spatial dimensions, the spatial resolution of LF images captured by commercial micro-lens based LF cameras are signi?cantly constrained. In this paper, we propose effective and ef?cient end-to-end convolutional neural network models for spatially super-resolving LF images. Speci?cally, the proposed models have an hourglass shape, which allows feature extraction to be performed at the low resolution level to save both computational and memory costs. To fully make use of the four-dimensional (4-D) structure information of LF data in both spatial and angular domains, we propose to use 4-D convolution to characterize the relationship among pixels. Moreover, as an approximation of 4-D convolution, we also propose to use spatial-angular separable (SAS) convolutions for more computationally- and memory-ef?cient extraction of spatial-angular joint features. Extensive experimental results on 57 test LF images with various challenging natural scenes show signi?cant advantages from the proposed models over state-of-the-art methods. That is, an average PSNR gain of more than 3.0 dB and better visual quality are achieved, and our methods preserve the LF structure of the super-resolved LF images better, which is highly desirable for subsequent applications. In addition, the SAS convolution-based model can achieve 3× speed up with only negligible reconstruction quality decrease when compared with the 4-D convolution-based one. The source code of our method is online available at https://github.com/spatialsr/DeepLightFieldSSR.
关键词： Light ?eld,super-resolution,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) - Background Subtraction via 3D Convolutional Neural Networks
摘要： Background subtraction can be treated as the binary classification problem of highlighting the foreground region in a video whilst masking the background region, and has been broadly applied in various vision tasks such as video surveillance and traffic monitoring. However, it still remains a challenging task due to complex scenes and for lack of the prior knowledge about the temporal information. In this paper, we propose a novel background subtraction model based on 3D convolutional neural networks (3DCNNs) which combines temporal and spatial information to effectively separate the foreground from all the sequences in an end-to-end manner. Different from conventional models, we view background subtraction as three-class classification problem, i.e., the foreground, the background and the boundary. This design can obtain more reasonable results than existing baseline models. Experiments on the Change Detection 2012 dataset verify the potential of our model in both quantity and quality.
关键词： Background Subtraction,Change Detection,3D Convolutional Neural Networks
更新于2025-09-09 09:28:46
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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 - Dense Fully Convolutional Networks for Crop Recognition from Multitemporal SAR Image Sequences
摘要： This work presents a dense fully convolutional architecture for crop type recognition from multitemporal RS images. Basically, we adapted a dense fully convolutional net to deal with stacks of multitemporal data. The proposed approach was tested upon a public dataset comprising two Sentinel-1A sequences from a tropical region in South America. We took as baseline a dense convolutional network designed for patch classification. Thematic and spatial accuracy, as well as the computational load were evaluated experimentally. The proposed architecture matched the baseline in terms of recognition rates and proved to be very efficient computationally in the inference phase.
关键词： crop type classification,Deep Learning,fully convolutional networks,SAR,multitemporal analysis
更新于2025-09-09 09:28:46
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Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher Discriminative 3D CNN
摘要： Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.
关键词： image-quality assessment,LV coverage,Fisher discriminant criterion,3D convolutional neural network,population image analysis
更新于2025-09-09 09:28:46
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[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) - Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images
摘要： Particulate matter with diameters less than 2.5 micrometers (PM2.5) is one of the most common air pollutants and may cause many severe diseases. An efficient PM2.5 monitoring system is of great benefit for human health and air pollution control. this paper, we estimate PM2.5 concentrations using outdoor images by a proposed ensemble of deep neural networks-based regression, which uses a feed-forward neural network to combine the PM2.5 predictions yielded by three convolutional neural networks, VGG-16, Inception-v3, and ResNet50, and calculate the final PM2.5 prediction of the image. A PM2.5 image dataset with 1460 photos was used for performance evaluation. The experimental results demonstrated that the proposed ensemble can provide more accurate PM2.5 estimation than all three individual deep learning networks used and, therefore, can be used for image-based PM2.5 monitoring.
关键词： convolutional neural network,estimation,deep learning,particulate matter,ensemble
更新于2025-09-09 09:28:46
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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 - Weed Classification in Hyperspectral Remote Sensing Images Via Deep Convolutional Neural Network
摘要： Automatic weed detection and mapping are critical for site-speci?c weed control in order to reduce the cost of farming as well as the impact of herbicides on human health. In this paper, we investigate patch-based weed identi?cation using hyperspectral images. Convolutional Neural Network (CNN) is evaluated and compared with the Histogram of Oriented Gradients (HoG) for this purpose. Suitable patch sizes are investigated. The limitation of RGB imagery is demonstrated. The experimental results indicate that the overall accuracy of the weed classi?cation using CNN increases with the increasing number of bands used. With more bands, CNN extracts more powerful and discriminative features and leads to improved classi?cation as compared to the traditional HoG feature extraction method. The computational load of CNN, however, is slightly increased with the increasing number of bands.
关键词： Histogram of Oriented Gradients (HoG),weed mapping,Hyperspectral images,Convolutional Neural Network (CNN)
更新于2025-09-09 09:28:46
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Combustion Regime Monitoring by Flame Imaging and Machine Learning
摘要： A method for automatic determination of combustion regimes using ?ame images on the basis of a convolutional neural network on labeled data is under consideration. It is shown that the accuracy of regime classi?cation reaches 98% on the ?ame images of a gas burner. The results of the operation of the convolutional neural network and classi?cation using di?erent linear models are compared.
关键词： image classi?cation,convolutional neural network,computer training,monitoring,?ame
更新于2025-09-09 09:28:46