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Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability...
While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine...
To assess the multi-state of a rolling bearing more effectively and simultaneously, a unified assessment method is proposed based on chaos fruit fly optimization algorithm hyper-sphere support vector machine (CFOA-HSVM) two measures combination. Aiming to the blindness of parameters selection for HSVM, multiple parameters of HSVM can be searched the optimal values using chaos theory combined with...
Strengthening the party organization construction in college is the objective requirement for development of the reform and opening-up policy and socialist market economy, and is an important guarantee to construct the harmonious campus. The investigation shows that, the college students' party organization plays a certain role in the campus construction and management and gets the positive achievements,...
In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version,...
In this paper, a novel spectral-spatial very high resolution images shadow detection algorithm based on random walker is proposed. First, a set of training samples is obtained by an improved Otsu based thresholding method automatically. Then, a widely used pixel-wise classifier, i.e., the Support Vector Machine (SVM), is applied to obtain an initial binary classification map. Finally, the initial...
Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked...
In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare...
In the government agencies, civil servants are required to have competence or ability to finish the work effectively and efficiently. In fact, the decision-making system for determining position and assignment of civil servants' functional works is still performed manually, so it takes a longer time. Moreover, the results are not totally accurate in terms of their competency. Rough set, hereinafter...
This paper proposes a ship detection method based on weighted support vector machines (SVM) and m-χ decomposition in compact polarimetric (CP) synthetic aperture radar (SAR) imagery. Firstly, the proposed method constructs the weighted feature vectors by extracting CP parameters. Each feature will be weighted by the ReliefF method. Then, ship targets in CP SAR imagery are detected by the weighted...
In this paper, a novel model of Gabor Filtering based Deep Network (GFDN) for hyperspectral image classification is proposed. First, spatial features are extracted via Gabor filtering from the three principal components. Gabor filter can capture physical structures of hyperspectral images, such as specific orientation information. Then, the Gabor features and spectral features are simply staked to...
The likelihood of transitions between pairs of land cover and land use classes in a given time interval and environmental context can be used to impose classification restrictions on an image or to evaluate results. This study presents a methodology for using the likelihood of transitions between classes to improve land cover classification, given a base map (a supposedly accurate map for the same...
This paper presents a new methodology for classification of local climate zones based on ensemble learning techniques. Landsat-8 data and open street map data are used to extract spectral-spatial features, including spectral reflectance, spectral indexes, and morphological profiles fed to subsequent classification methods as inputs. Canonical correlation forests and rotation forests are used for the...
Built-up area has been one of the most important objects to be extracted in remote sensing images. Several factors such as complex structure, diverse texture and varied background, bring the challenges for the task of built-up area extraction. In this paper, a multiple input structure of deep convolution neural network (CNN) is proposed to extract built-up area automatically, which can fuse the information...
This paper proposes a new approach for contextual feature extraction from superpixels in aerial urban scenes. Our method extracts features with many levels of context from superpixels by exploiting different layers of a pre-trained convolutional neural network. Experimental results show the effectiveness of the proposed approach, which outperforms traditional methods based on handcrafted feature extraction...
This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific...
Despite continued sustainability as an academic field of study, researchers of technical communication have struggled with employing appropriate research methods in their studies. In this panel, the panelists will each discuss an aspect of this struggle framed within their own experiences and expertise. Topics will include the quality of evidence in research studies; quality of methodology in workplace...
Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results. On the other hand, there were situations in which discretization decreased overall performance of the system. Therefore, the...
Electric power SDH network is a comprehensive communication network, which takes advantages of SDH technology, and is a widely used technology. How to protect the network more effectively is the concern of the operators. So the main purpose of this paper is to find a way to analysis risk in advance and enhance reliability of electric power SDH network. Firstly, we set up an evaluation index system...
In this paper, we put forward deep neural network ensemble to model and predict Chinese stock market index (including Shanghai composite index and SZSE component index), based on the input indices of recent days. A set of component networks are trained by historical data for this task, where Backpropagation and Adam algorithm are used to train each network efficiently. Bagging approach combines these...
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