In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learn...
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In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learning local regression functions which map a low-resolution patch to a high-resolution patch. Localization strategy is generally adopted in single-image super-resolution with nearest neighbor-based algorithms. However, the poor generalization of the nearest neighbor estimation decreases the performance of such algorithms. Though the problem can be fixed by local regression algorithms, the sizes of local training sets are always too small to improve the performance of nearest neighbor-based algorithms significantly. To overcome the difficulty, the semi-supervised regression algorithm is used here. Unlike supervised regression, the information about test samples is considered in semi-supervised regression algorithms, which makes the semi-supervised regression more powerful. Noticing that numerous test patches exist, the performance of nearest neighbor-based algorithms can be further improved by employing a semi-supervised regression algorithm. Experiments verify the effectiveness of the proposed algorithm.
Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding a...
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Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding algorithm is based on manifold assumption, which defines that the embedded neighbor patches are contained in a single manifold. While manifold assumption does not always hold. In this paper, we present a novel local learning-based image single-frame SR reconstruction algorithm with kernel ridge regression (KRR). Firstly, Gabor filter is adopted to extract texture information from low-resolution patches as the feature. Secondly, each input low-resolution feature patch utilizes K nearest neighbor algorithm to generate a local structure. Finally, KRR is employed to learn a map from input low-resolution (LR) feature patches to high-resolution (HR) feature patches in the corresponding local structure. Experimental results show the effectiveness of our method.
The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angl...
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The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed.
Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are difficult to develop because of factors like UAV motion, scene complexity and so on. In this paper, we propose a new fram...
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Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are difficult to develop because of factors like UAV motion, scene complexity and so on. In this paper, we propose a new framework of multi-motion layer analysis to detect and track moving vehicles in airborne platform. Moving vehicles are firstly detected by registration and temporal differencing to establish motion layers. After motion layers are constructed, they are maintained over time for tracking vehicles. All vehicles are tracked by maintaining their corresponding motion layers. Our experimental results showed that compared with other previous algorithms, our method can achieve better results in terms of detection and tracking performance.
In action recognition, bag of words based approaches have been shown to be successful, for which the quality of codebook is critical. This paper proposes a novel approach to select key poses for the codebook, which mo...
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ISBN:
(纸本)9781457716218
In action recognition, bag of words based approaches have been shown to be successful, for which the quality of codebook is critical. This paper proposes a novel approach to select key poses for the codebook, which models the descriptor space utilizing manifold learning to recover the geometric structure of the descriptors on a lower dimensional manifold space. A PageRank based centrality measure is developed to select key poses on the manifold. In each step, a key pose is selected and the remaining model is modified to maximize the discriminative power of selected codebook. In classification, the ambiguity of each action couple is evaluated through cross validation. An additional subdivision will be executed for ambiguous pairs. Experiments on ut-tower dataset showed that our method is able to obtain better performance than the state-of-the-art methods.
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