Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect target...
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ISBN:
(纸本)9780819469601
Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect targets. image intensity surfaces for the neighborhood of every pixel of the original long-wave infrared image and mid-wave infrared are well-fitted by mapped LS-SVM respectively. And long-wave and mid-wave infrared image gradient images are obtained by LS-SVM based on radial basis kernels function. Fusion rule is set up according to the features of gradient images. At last, segment fused image and targets can be detected with contrast threshold. Compared with wavelet fusion detection algorithm and morphological fusion detection algorithm, when a target is affected by baits, the experimental results demonstrate that the proposed approach in the paper based on LS-SVM to fuse and detect weak and small target is reliable and efficient.
In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed i...
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In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed in detail. Then some measures are given to eliminate the false corners. Finally a relaxation matching scheme is presented. The experiments show the effectiveness and feasibility of our matching method.
We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two...
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We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the global consistency error evaluated on the Berkeley segmentation database.
Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment im...
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Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.
An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. inst.ad of color quantization algorithm, an automatic classification method based on adaptive mean shift ...
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An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. inst.ad of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
We propose a new algorithm to find minimal rough set reducts by using Particle Swarm Optimization (PSO). Like Genetic Algorithm, PSO is also a type of evolutionary algorithm. But compared with GA, PSO does not need co...
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A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative ...
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A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.
We present a method that partitions a single image into two layers, requiring that one layer has similar properties in terms of pixel colour variation to a provided template patch. First the paper provides a new view ...
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ISBN:
(纸本)1904410146
We present a method that partitions a single image into two layers, requiring that one layer has similar properties in terms of pixel colour variation to a provided template patch. First the paper provides a new view on defining a similarity function for a pixel with its small neighbourhood to be part of the texture described by the template patch. This results in better description of pixels near the texture boundary. Second, it is shown how the Maximally Stable Extremal Regions (MSERs), originally designed for wide baseline stereo matching, can be used to locally merge pixels having the same intensity and thus reduce the dimension of the graph representing the image. The MSERs help in texture description and yield significant reduction of memory and computation time. Finally the graph is fed into the ***/*** algorithm to cut the graph into two parts. Performance of the method is presented on some images from the Berkeley database. Finally, restrictions in using the method are discussed.
The performances of a well-known GHR car-following model was investigated byusing numerical simulations in describing the acceleration and deceleration process induced by themotion of a leading car. It is shown that i...
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The performances of a well-known GHR car-following model was investigated byusing numerical simulations in describing the acceleration and deceleration process induced by themotion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily closetogether if their speed are identical, and it waves aside even though the separation is larger thanits desired distance. Based on these investigations, a modified GHR model which features a newnonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value wasproposed accordingly to overcome these deficiencies. In addition, the analysis of the additivenonlinear term and steady-state flow of the new model were studied to prove its rationality.
The performances of a well-known GHR car-following model was investigated by using numerical simulations in describing the acceleration and deceleration process induced by the motion of a leading car. It is shown that...
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The performances of a well-known GHR car-following model was investigated by using numerical simulations in describing the acceleration and deceleration process induced by the motion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily close together if their speed are identical , and it waves aside even though the separation is larger than its desired distance. Based on these investigations, a modified GHR model which features a new nonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value was proposed accordingly to overcome these deficiencies. In addition, the analysis of the additive nonlinear term and steady-state flow of the new model were studied to prove its rationality.
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