Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression ...
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Generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where data samples a...
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In this paper, we present a new speech enhancement method based on robust principal component analysis. In the proposed method, noisy signal is transformed into time-frequency domain where background noise is assumed ...
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A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to cons...
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Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of...
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The study of the second order motion in biological vision is a new source of inspiration for algorithms and research directions in computer vision. In this paper, the second order motion can be divided into three typi...
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The study of the second order motion in biological vision is a new source of inspiration for algorithms and research directions in computer vision. In this paper, the second order motion can be divided into three typical group according to the modulation types: spatial modulate motion, temporal modulate motion and spatio-temporal modulate motion. Experiments are conducted on the first order motion perception based on correlation model and the second order motion perception by correlation model preceded with a nonlinear process called texture grabber. The computational results are consistent with the previous suggestion that the second order motions are processed by nonlinear system.
An important class of radiometric degradations we are faced with often in practice is image blurring. Special attention is paid to the recognition of the blurred image by moment invariant approach. Some important rule...
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ISBN:
(纸本)0780385543
An important class of radiometric degradations we are faced with often in practice is image blurring. Special attention is paid to the recognition of the blurred image by moment invariant approach. Some important rules of complex moments for the blurred image are presented. Based on these rules, a useful subset of moment invariants is introduced, that are not affected by the blur, rotation, scale, and translation of the images. The experiments have shown that these invariants can be successfully used in recognition of the blurred image.
Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain high...
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Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.
The advent of highly accurate protein structure prediction methods has fueled an exponential expansion of the protein structure database. Consequently, there is a rising demand for rapid and precise structural homolog...
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Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmen...
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Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmented method can be used in a wide variety of billet scenes. According to high temperature and complex scene in the rolling line, we use an effective clustering and projection characteristics to determine the terminal condition of recursive segmentation. Then we can label character candidate regions in turn by this effective characteristics, and select the regions we want to achieve. The experiments show that this method makes full use of the characteristics of region and clustering. It can improve the quality of detection, and the detection result meets the need of practical application.
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