- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery
摘要： Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods.
关键词： Change detection,deep learning,multispectral imagery,generative adversarial networks (GANs)
更新于2025-09-23 15:23:52
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Generative models for direct generation of CNC toolpaths
摘要： Today, numerical controls (CNC) are the standard for the control of machine tools and industrial robots in production and enable highly flexible and efficient production, especially for frequently changing production tasks. A numerical control has discrete inputs and outputs. Within the NC channel, however, it is necessary to analytically describe curves for the calculation of the position setpoints and the jerk limitation. The resulting change between discrete and continuous description forms and the considerable restrictions in the parallelisation of the interpolation of continuous curves within the NC channel lead to a performance overhead that limits the performance of the NC channel with regard to the calculation of new position setpoints. This can lead to a drop in production speed and thus to longer production times. To solve this problem, we propose a new approach in this paper. This is based on the use of deep generative models and allows the direct generation of interpolated toolpaths without calculation of continuous curves and subsequent discretization. The generative models are being trained to create curves of certain types such as linear and parabolic curves or splines directly as discrete point sequences. This approach is very well feasible with regard to its parallelization and reduces the computing effort within the NC channel. First results with straight lines and parabolic curves show the feasibility of this new approach for the generation of CNC toolpaths.
关键词： machine learning,computerized numerical control,interpolation,CNC,generative adversarial networks
更新于2025-09-23 15:22:29
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
摘要： Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays. However, recent approaches mainly focus on one model with two domains, which may face heavy burdens with the large cost of training time and the huge model parameters, under such a requirement that n (n>2) domains are freely transferred to each other in a general setting. To address this problem, we propose a novel and unified framework named Domain-Bank, which consists of a globally shared auto-encoder and n domain-specific encoders/decoders, assuming that there is a universal shared-latent space can be projected. Thus, we not only reduce the parameters of the model but also have a huge reduction of the time budgets. Besides the high efficiency, we show the comparable (or even better) image translation results over state-of-the-arts on various challenging unsupervised image translation tasks, including face image translation and painting style translation. We also apply the proposed framework to the domain adaptation task and achieve state-of-the-art performance on digit benchmark datasets.
关键词： Shared-Latent Space,Unsupervised Image-to-Image Translation,Generative Adversarial Networks,Variational Autoencoders,Domain-Bank,Multi-Domain
更新于2025-09-23 15:22:29
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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 - Data Augmentation Method of SAR Image Dataset
摘要： Large-scale high-quality, standardized, measurable and accurate data is the key to promote the progress of the algorithm in the radar remote sensing. Data scaling is a widespread technology that increases the size of a labeled training set dataset through specific data transformations. Synthetic Aperture Radar (SAR) image simulators based on computer-aided mapping models play an important role in SAR applications such as automatic target recognition and image interpretation, but the accuracy of this simulator is due to geometric errors and simplification of electromagnetic calculations. In order to achieve a SAR image datasets with the known target and azimuth angles, we can generate the desired image directly from a known image database. We can realize the augmentation of SAR image data set through linear synthesis and Generative Adversarial Networks, which can generate SAR images for the specified azimuth.
关键词： generative adversarial networks,SAR image,linear synthesis
更新于2025-09-23 15:21:21
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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) - HESCNET: A Synthetically Pre-Trained Convolutional Neural Network for Human Embryonic Stem Cell Colony Classification
摘要： This paper proposes a method for improving the results of deep convolutional neural network classification using synthetic image samples. Generative adversarial networks are used to generate synthetic images from a dataset of phase-contrast, human embryonic stem cell (hESC) microscopy images. hESCnet, a deep convolutional neural network is trained, and the results are shown on various combinations of synthetic and real images in order to improve the classification results with minimal data.
关键词： Image Processing,Generative Adversarial Networks,Deep Learning,Computer Vision,Video Bioinformatics
更新于2025-09-23 15:21:01
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3D auto-context-based locality adaptive multi-modality GANs for PET synthesis
摘要： Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1×1×1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.
关键词： Image synthesis,Positron emission topography (PET),Locality adaptive fusion,Generative adversarial networks (GANs),Multi-modality
更新于2025-09-23 15:21:01
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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 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - An improvement on the CNN-based OAM Demodulator via Conditional Generative Adversarial Networks
摘要： In the paper, an Orbital Angular Momentum (OAM) demodulation method based on Conditional Generative Adversarial Networks(CGAN) is proposed to improve the accuracy of Convolutional Neural Networks (CNN) based demodulator. We train a CGAN on a limited data set, and the discriminator in CGAN is fine-tuned as a new classifier for OAM demodulation. Our numerical simulations demonstrate that the proposed method can improve the accuracy of OAM demodulator from 93.56% to 98.36% over 400-m free-space link when the turbulence strength equals 4×10-13m-2/3.
关键词： Deep Learning,OAM demodulation,Conditional Generative Adversarial Networks
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE International Conference on Image Processing (ICIP) - Taipei, Taiwan (2019.9.22-2019.9.25)] 2019 IEEE International Conference on Image Processing (ICIP) - Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning
摘要： With the rapid growth of generative adversarial networks (GANs), a photo-realistic image can be easily generated from a low-dimensional random vector nowadays. However, the generated image can be used to synthesize several persons who may have a potential effect on society with radical contents. Considering that many techniques to produce a photo-realistic facial image based on different GANs are already available, collecting training images of all possible generative models is difficult; hence, the learning-based approach would not effectively detect a fake image generated using an excluded generative model. To overcome this shortcoming, we propose a two-step pairwise learning approach to learn common fake features over the training images generated by using different generative models. First, the triplet loss will be used to simulate the relation between fake and real images and utilized to learn the discriminative features to determine whether an image is real or fake. Then, we propose a novel coupled network to accurately capture local and global image features of the fake or real images. The experimental results demonstrate that the proposed method outperforms the baseline supervised learning methods for fake facial image detection.
关键词： generative adversarial networks,coupled network,triplet loss,Forgery detection,deep learning
更新于2025-09-12 10:27:22
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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 - Semi-Supervised Object Detection in Remote Sensing Images Using Generative Adversarial Networks
摘要： Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared w1ith supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.
关键词： generative adversarial networks (GAN),convolutional neural networks (CNN),Semi-supervised learning,object detection
更新于2025-09-10 09:29:36