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- 2019
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- 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
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- Central South University
- Hubei University
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A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
摘要： Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At ?rst, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the ?nal improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast signi?cantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.
关键词： convolutional neural network,Retinex model,image enhancement,color model,batch normalization,feature learning
更新于2025-09-23 15:21:01
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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) - Visual Tree Convolutional Neural Network in Image Classification
摘要： In image classification, Convolutional Neural Network (CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others. Improving the classification accuracy on these confused categories is benefit to the overall performance. In this paper, we build a Confusion Visual Tree (CVT) based on the confused semantic level information to identify the confused categories. With the information provided by the CVT, we can lead the CNN training procedure to pay more attention on these confused categories. Therefore, we propose Visual Tree Convolutional Neural Networks (VT-CNN) based on the original deep CNN embedded with our CVT. We evaluate our VT-CNN model on the benchmark datasets CIFAR-10 and CIFAR-100. In our experiments, we build up 3 different VT-CNN models and they obtain improvement over their based CNN models by 1.36%, 0.89% and 0.64%, respectively.
关键词： Image Classification,Convolutional Neural Network,Confusion Visual Tree,Deep Learning
更新于2025-09-23 15:21:01
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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) - Semi-supervised convolutional neural networks with label propagation for image classification
摘要： Over the past several years, deep learning has achieved promising performance in many visual tasks, e.g., face verification and object classification. However, a limited number of labeled training samples existing in practical applications is still a huge bottleneck for achieving a satisfactory performance. In this paper, we integrate class estimation of unlabeled training data with deep learning model which generates a novel semi-supervised convolutional neural network (SSCNN) trained by both the labeled training data and unlabeled data. In the framework of SSCNN, the deep convolution feature extraction and the class estimation of the unlabeled data are jointly learned. Specifically, deep convolution features are learned from the labeled training data and unlabeled data with confident class estimation. After the deep features are obtained, the label propagation algorithm is utilized to estimate the identities of unlabeled training samples. The alternative optimization of SSCNN makes the class estimation of unlabeled data more and more accurate due to the learned CNN feature more and more discriminative. We compared the proposed SSCNN with some representative semi-supervised learning approaches on MINIST and Cifar-10 databases. Extensive experiments on landmark databases show the effectiveness of our semi-supervised deep learning framework.
关键词： convolutional neural network,semi-supervised learning,label propagation
更新于2025-09-23 15:21:01
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Optimal Approach for Image Recognition Using Deep Convolutional Architecture
摘要： In the recent time, deep learning has achieved huge popularity due to its performance in various machine learning algorithms. Deep learning as hierarchical or structured learning attempts to model high-level abstractions in data by using a group of processing layers. The foundation of deep learning architectures is inspired by the understanding of information processing and neural responses in human brain. The architectures are created by stacking multiple linear or nonlinear operations. The article mainly focuses on the state-of-the-art deep learning models and various real-world application-speci?c training methods. Selecting optimal architecture for speci?c problem is a challenging task; at a closing stage of the article, we proposed optimal approach to deep convolutional architecture for the application of image recognition.
关键词： Deep neural networks,Image recognition,Image processing,Transfer learning,Convolutional neural networks,Deep learning
更新于2025-09-23 15:21:01
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Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN)
摘要： Computed tomography (CT) imaging is the preferred imaging modality for diagnosing lung-related complaints. Automatic lung segmentation is the most common prerequisite to develop a computerized diagnosis system for analyzing chest CT images. In this paper, a convolutional deep and wide network (CDWN) is proposed to segment lung region from the chest CT scan for further medical diagnosis. Earlier lung segmentation techniques depend on handcrafted features, and their performance relies on the features considered for segmentation. The proposed model automatically segments the lung from complete CT scan in two laps: (1) learning the required ?lters to extract hierarchical feature representations at convolutional layers, (2) dense prediction with spatial features through learnable deconvolutional layers. The model has been trained and evaluated with low-dose chest CT scan images on LIDC-IDRI database. The proposed CDWN reaches the average Dice coef?cient of 0.95 and accuracy of 98% in segmenting the lung regions from 20 test images and maintains consistent results for all test images. The experimental results con?rm that the proposed approach achieves a superior performance compared to other state-of-the-art methods for lung segmentation.
关键词： Medical imaging,Image processing and analysis,Deep learning,Automatic lung segmentation,Convolutional neural network
更新于2025-09-23 15:21:01
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Functionalized Materials for Integrated Photonics: Hybrid Integration of Organic Materials in Silicon- based Photonic Integrated Circuits for Advanced Optical Modulators and Light-sources
摘要： It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.
关键词： robustness,generalization,convolutional neural networks,adversarial,K-Support norm
更新于2025-09-23 15:19:57
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Implementation of deep convolutional neural network for classification of multiscaled and multiangled remote sensing scene
摘要： With the evolution of convolutional neural networks, extraction of deep features for accurate classification of Remote Sensed (RS) images have gained lot of momentum. However, due to variation in the scale of high resolution remote sensed images, accurate classification still remains a challenging task. Moreover, along with the scale, variation in the angle also decreases the accuracy of extraction of deep features using convolutional neural network. In this paper, a Multiscale and Multiangle convolutional neural network (MSMA-CNN) is proposed which extracts deep features of the RS images by employing several convolutional, pooling and fully connected layers which are discriminant, nonlinear and invariant. In MSMA-CNN, along with the spatial features, spectral features are also considered for classification of remote sensing scenes thus, making the entire system robust. The RS images are scaled at different levels using Gaussian Pyramid Decomposition and rotated at different angles and further features are derived using maximally stable extremal regions (MSER) at spectral and spatial level which are further concatenated and fed to the MSMA-CNN. A regularization parameter is added to get the results for test images as close as the trained images. A hybrid MSMA-CNN structure is designed by altering various parameters of the CNN structure to get improved optimized performance. To demonstrate the effectiveness of the proposed method, we compared the results on six challenging high-resolution remote sensing datasets and achieve a classification accuracy of 92.25% which shows significant improvement compared to the other state-of-the-art scene classification methods in terms classifcational accuracy and computational cost.
关键词： Deep feature extraction,classification of remote sensed images,multiscale and multiangle remote sensed images,convolutional neural networks
更新于2025-09-23 15:19:57
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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networksa??
摘要： Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.
关键词： Convolutional neural network,Machine learning,Hyperspectral microscopy,Food safety,Foodborne pathogen,Rapid classification
更新于2025-09-23 15:19:57
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Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection
摘要： In recent years, deep learning-based crack detection methods have been widely explored and applied due to their high versatility and adaptability. In civil engineering applications, recent research on crack detection through deep convolutional neural network (DCNN) includes road pavement crack detection, bridge inspection, defects detection in shield tunnel lining, etc. Despite the increasing popularity of DCNN on crack detection, many challenges have yet to be properly addressed. For crack detection using three-dimensional (3D) range (i.e., elevation) images, disturbances such as surface variation can negatively affect the detection performance. Besides, some typical non-crack patterns such as grooves can be easily misidentified as cracks, i.e., false positives. Another issue lies in the selection of hyperparameters related with the design of a DCNN architecture. For example, the hyperparameters which are related with network structure (e.g., kernel size, network depth and width) and training (e.g., mini-batch size and learning rate) can impact the network performance to a significant extent. Therefore, they need to be properly determined for optimal performance. However, for deep learning-based roadway crack classification using laser-scanned range images, a comprehensive discussion on the hyperparameter selection/tuning has not been thoroughly performed. This study develops a hyperparameter selection process involving a series of experiments on laser-scanned range images with high diversities, investigating the optimal joint hyperparameter configuration on network structure and training for DCNN-based roadway crack classification. In a comparative study, 36 DCNN architectures with varying layouts are developed for crack classification. These architecture candidates differ in kernel sizes (e.g., 3 × 3, 7 × 7, and 11 × 11), network depths (from 5 to 8 weight layers), and widths (from 16 to 96 kernels in each convolutional layer). The 7-layer DCNN with constant 7 × 7 kernels and increasing network widths yields the highest classification performance among the proposed 36 DCNN classifiers, which may be because it can best reflect the complexity of the acquired laser-scanned roadway range images. Once the optimal architecture layout is determined, further discussion on the selection of min-batch sizes, learning rates, dropout factor and leaky rectified linear unit (LReLU) factor is performed. Experimental results show the optimal architecture with associated training configuration can achieve consistent and accurate performance, under the contamination of surface variations and grooved patterns in laser-scanned range images. Discussion on the hyperparameter selection can provide insights for the development of DCNN in similar applications using laser-scanned range images.
关键词： Roadway crack,Groove,Laser-scanned range image,Hyperparameter selection,Deep convolutional neural network
更新于2025-09-23 15:19:57
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Micro-cracks detection of solar cells surface via combining short-term and long-term deep features
摘要： The machine vision based methods for micro-cracks detection of solar cells surface have become one of the main research directions with its efficiency and convenience. The existed methods are roughly classified into two categories: current viewing information based methods, prior knowledge based methods, however, the former usually adopt hand-designed features with poor generality and lacks the guidance of prior knowledge, the latter are usually implemented through the machine learning, and the generalization ability is also limited since the large-scale annotation dataset is scarce. To resolve above problems, a novel micro-cracks detection method via combining short-term and long-term deep features is proposed in this paper. The short-term deep features which represent the current viewing information are learned from the input image itself through stacked denoising auto encoder (SDAE), the long-term deep features which represent the prior knowledge are learned from a large number of natural scene images that people often see through convolutional neural networks (CNNs). The subjective and objective evaluations demonstrate that: 1) the performance of combing the short-term and long-term deep features is better than any of them alone, 2) the performance of proposed method is superior to the shallow learning based methods, 3) the proposed method can effectively detect various kinds of micro-cracks.
关键词： solar cell,stacked denoising auto encoder,long-term,convolutional neural networks,short-term,micro-cracks detection
更新于2025-09-23 15:19:57