Compressed sensing theory by developing a signal sparse features, under the condition of far less than the Nyquist sampling rate, the correct signal is acquired with random sampling the discrete samples, and then thro...
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Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from ...
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Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from that of optical satellite imagery, whose imaging is a slanting imaging of electromagnetic microwave reflection. The different characters between radar image and optical satellite images are very distinct, such as the layover distortion of ground-truth and speckle noise, which degrades the image to such an extent that the features are very unclear and difficult to be extracted. So the factors such as the hypsography, ground truth, sensor altitude and imaging time should be taken into account for radar image and optical image matching. In this paper, we develop an image match algorithm based on reference map multi-area selection using fuzzy sets. Image matching is generally a procedure that calculates the similarity measurement between sensed image and the corresponding intercepted image in reference map and it searches the maximum position in the correlation map. Our method adopts a converse matching strategy which selects multi-areas in optical reference map using fuzzy sets as model images, then match them on the sensed image respectively by normalized cross correlation matching algorithm and fuse the match results to get the optimum registered position. Multi-areas selection mainly considers two influence factors such as ground-truth texture features and the hypsography (DEM) of imaging region, which will suppress the influence of great variance imaging region. Experiment results show the method is effective in registering performance and reducing the calculation.
In this paper, we consider lossy image compression, which is based on wavelet theory. We introduce image restoration technology into the wavelet compression. By applying image restoration to the low-frequency componen...
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Motion segmentation is one of the fundamental problems in digital image processing area. The estimation of velocity field is one of the most notable approaches on the first step of motion segmentation. A novel motion ...
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this paper presents a novel fast single-pass contour tracing algorithm in a binary image. The proposed algorithm is viewed as follow steps: firstly a set of contour segments of all object contours can be generated and...
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This paper presents a novel hardware implementation of the adaptive JPEG-LS in field programmable gate array (FPGA), based on the low complexity lossless compression for images (LOCO-I) compression scheme. Differently...
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In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new...
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ISBN:
(纸本)9789898565419
In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.
Infrared image simulation plays an important role in many fields. This paper presents a method for dynamic infrared image simulation under various natural conditions. Firstly, we analyze the infrared imaging rationale...
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An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relation...
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Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to ch...
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