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Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However, one needs to encode a syntax to indicate the chosen...
Agricultural land use is essential for prosperity and sustainability of a city. However, the performance of detecting agricultural land use based on vegetation parameters will be substantially reduced when the vegetation changed in type, quantity and condition. This study believed that soil moisture, a fundamental soil parameter, is able to detect agricultural land use. A method was proposed to evaluate...
In this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms...
In the multi-objective decision-making problems, the weight problem research occupies a very important position. The weight is the quantitative distribution of the different aspects' importance of the object to be evaluated, and the weight of each evaluation factor in the overall evaluation is differentiated, and is of great significance in practice. In this paper, by using the fuzzy analytic hierarchy...
The aim of this study is to develop a new method for hand gesture recognition using Leap Motion via deterministic learning. Efficient and accurate extraction and representation of gesture features are achieved. The recognition approach consists of two stages: a training stage and a recognition stage. In the training stage, hand gesture features representing hand motion dynamics, including spatial...
In order to ensure the safety and efficiency of the transportation of LNG carrier, and for the lack of evaluation method of LNG seafarers' competency, the evaluation index system of professional competency of LNG seafarers has been established. By using the questionnaire data that obtained from the adoption of intuitionistic fuzzy entropy method dealing with the importance of index, the objective...
The research focus of designing local patch descriptors has gradually shifted from handcrafted ones (e.g., SIFT) to learned ones. In this paper, we propose to learn high performance descriptor in Euclidean space via the Convolutional Neural Network (CNN). Our method is distinctive in four aspects: (i) We propose a progressive sampling strategy which enables the network to access billions of training...
We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene. Particularly in indoor videos such as captured by robotic platforms or handheld and bodyworn RGBD cameras, nearby video frames provide diverse viewpoints...
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...
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