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Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo...
Classification in remote sensing, similar to semantic segmentation in computer vision, is aimed to assign a label to each pixel in images to indicate which class it belongs to. Fully convolutional networks (FCN), one of semantic segmentation methods, is proposed to tackle this problem in fully PolSAR images in this paper. To exploit the polarimetric information in PolSAR images, H-A-α polarimetric...
The new advanced very high resolution (VHR) synthetic aperture radar (SAR) sensor is a kind of high-tech imaging radar developed rapidly in recent years, and it can get even less than 1 m high resolution SAR image. The feature of the VHR SAR image is different from the low or medium resolution SAR image and it contains more abundant information, so the traditional SAR image classification methods...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
Land cover change detection has long been a hot field in polarimetric synthetic aperture radar (SAR) applications. In certain cases, we care not only the changed areas but also from which type to another. This paper presents a supervised urban land cover change types identification method using a series of polarimetric descriptors from SAR observables and polarimetric decomposition. The normalized...
In order to estimate the amount of oil that can be recovered from oil sands slurry, technically referred to as processability number, we propose a method based on image processing in this paper. Our study begins with a review of human observations in conducting this task to determine visual features pertinent for assessing ore slurry quality. Subsequently we extract potentially useful image features...
In this paper, we propose a BSP-Based Support Vector Regression Machine Parallel Framework which can implement the most of distributed Support Vector Regression Machine algorithms. The major difference in these algorithms is the network topology among distributed nodes. Therefore, we adopt the Bulk Synchronous Parallel model to solve the strongly connected graph problem in exchanging support vectors...
According to course teaching characteristic and actual status of Network Operating System, describes some reformation measures about teaching content, teaching methods, assessment and textbook construction. As a result of practice, it could provide reference value for Network Operating System teaching.
A large number of accidents happened each day in many industries all over the world, which result in injury to people, environment pollution and consequential reputation damage. By investigation into accidents, we can identify root causes of accidents and develop implement corrective actions, which prevent accidents from reoccurring. Accident root cause analysis method is introduced in this paper...
University culture is an important part of human advanced culture. Culture is the soul of university, university just use this kind of culture to cultivate talents. From five dimensions of university cultural development, which is mental culture, matcrial culture, institutional culture, environmental culture and featured items, people use basic fuzzy analysis method to evaluate university cultural...
Texture feature has been widely used in image segmentation, classification, retrieval and many others. Among various approaches to texture feature extraction, Gabor filtering has emerged as one of the most popular in recent years. Gabor filter-based texture feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smoothing parameters of the...
Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature,...
This paper proposes a post-processing method for image segmentation to take advantage of information not directly available from the image. Specifically, the proposed method improves the segmentation of an image by making use of shape information learned from training shapes in ground truth images. To obtain shape prior, training shapes are first aligned by congealing, and then landmark interpolation...
Target discrimination is the key step of automatic target detection in synthetic aperture radar (SAR) images. Aiming at the issue of aircraft discrimination in high resolution SAR images, a novel discrimination method is proposed with using texture features. First of all the method of gray level co-occurrence matrix is used to generate eight discrimination texture features: mean, variance, deficit...
This paper presents a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most of local threshold algorithms are only based on intensity analysis. In many applications where an image contains objects with a similar shape, besides the intensity information, prior known shape attributes could be exploited to improve the segmentation...
Scale-space representation of an image is a significant way to generate features for classification. However, for a specific classification task, the entire scale-space may not be useful; only a part of it is typically effective. Toward this end, we design a data dependent classification kernel function, which is a weighted mixture of kernels defined on individual scales. In order to choose the optimum...
Multi-layer perceptron feed-forward neural network is adopted to predicate the diameter error of workpiece in turning process on the basis of the characteristics of diameter error. Turning experiment is designed to obtain the original training data and testing data. After analyzing the advantages and disadvantages of gradient descent algorithm and traditional genetic algorithm, gradient descent algorithm...
Risk assessment of information security is an important assessment method and decision mechanism in the process of making information security system. The risk evaluation is a process of computing risk value by means of risk assessment, which is from assessment and evaluation of assets, threats, and vulnerabilities to the risk evaluation of information asset. Focused on the uncertainty and complexity...
First, the paper analyzes the standard BP Algorithm. Toward the limitation of the standard BP algorithm, an improved BP algorithm is obtained by modifying the action function, regulating the learning rate and choosing the initial weights. And then a recognition example about license plate recognition technology is given to prove the improved BP algorithm. From the exam results, the improved BP algorithm...
Now the online monitor and diagnose of capacitive equipments still remains in a simple data processing level. The level of application of diagnosis and monitoring will be improved if advanced mathematical tools for analysis used in it. The paper introduces a new methods of using the BP neural network to predict the dielectric loss angle. Selecting inputs, outputs and parameters of the BP neural network...
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