This paper presents a new Synthetic Aperture Radar（SAR） Automatic Target Recognition（ATR） method based on slow feature analysis. Slow feature analysis（SFA） is a method for learning invariant or slowly varying fe...
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This paper presents a new Synthetic Aperture Radar（SAR） Automatic Target Recognition（ATR） method based on slow feature analysis. Slow feature analysis（SFA） is a method for learning invariant or slowly varying features from multi-dimensional input signal. The SFA-based SAR ATR system does not require any pre-processing, such as filtering or pose estimation of the image. The performance of the method is evaluated via three classification experiments on Moving and Stationary Target Acquisition and Recognition（MSTAR） database. The experiment results show the effectiveness of the proposed method on SAR ATR problem.
Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control system. This paper studies the stochastic stability of a affine nonlinear system within random ...
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With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image reta...
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ISBN:
(纸本)9781479989591
With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image retargeting techniques have been proposed. However, little effective objective quality assessment metric for image retargeting has been proposed. In this paper, we propose an objective image retargeting quality assessment method based on Hybrid Distortion Pooled Model (HDPM) considering image local similarity, content information loss and image structural distortion. The proposed HDPM method measures the retargeted image's local similarity based on matching the similar block by Scale-Invariant Features Transform (SIFT) features and computing the corresponding blocks' similarity by structural similarity (SSIM). Furthermore, the image content information loss in retargeted image, which is regarded as the SIFT feature loss, is taken into account. Besides, we also consider image's structural distortion in the proposed method, which is based on GLCM (Gray-level co-occurrence matrix). To evaluate the effectiveness of the proposed method, extensive experiments have been conducted, and the results show improved consistency between the proposed HDPM method and the corresponding subjective evaluations.
In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing appr...
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ISBN:
(纸本)9781479986897
In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing approach to image coding in the cloud, as it removes knowledge redundancy among images that share the same model. In this paper, we make an attempt to model-based image coding for landmark images, where our model consists of three-dimensional (3-D) point-cloud plus image patches to describe the geometry and surface color of the landmark respectively. The camera parameters of an input image are estimated based on the 3-D point-cloud and the patches in the model, and then prediction image is generated by selecting, warping, and stitching image patches as well as illuminance compensation, the residue between original and prediction images is compressed by P-frame coding in HEVC encoder. We perform experiments on an Internet photo collection to verify the effectiveness of the proposed scheme. Preliminary results display the superior performance of our scheme that achieves as high as 39.9% bits saving compared to HEVC intra on a single image. The proposed scheme indicates a promising approach to image coding in the cloud and is worthy of in-depth investigation.
The Dynamic Adaptive Streaming over HTTP (DASH) enables bitrate adaptation through different representations of the same content. It is common to encode random access point (RAP) pictures at segment boundaries to supp...
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ISBN:
(纸本)9781479983407
The Dynamic Adaptive Streaming over HTTP (DASH) enables bitrate adaptation through different representations of the same content. It is common to encode random access point (RAP) pictures at segment boundaries to support representation switching. As an open group of pictures (GOP) results into a temporary discontinuity of the video playback due to the inability to decode some pictures when switching representations, closed GOP prediction structures are normally used in DASH. This paper proposes two similar methods for using the open GOP prediction structure in DASH representations while a full picture rate is maintained also during representation switching. The first method is enabled with straightforward changes in the decoding of the High Efficiency Video Coding (H.265/HEVC) standard, whereas the second method utilizes the adaptive resolution change feature of the scalable (SHVC) extension of H.265/HEVC. Experiments show that the proposed methods outperform the use of closed GOPs by 5.6% on average in terms of Bjontegaard delta bitrate (BD-rate).
In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical ...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.
Accurate and reliable information on land use is a basis of agricultural disaster warning and emergency action. The natural disasters typhoon, cold disaster, drought, and so on, have great influences on agricultural p...
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ISBN:
(纸本)9781467376631
Accurate and reliable information on land use is a basis of agricultural disaster warning and emergency action. The natural disasters typhoon, cold disaster, drought, and so on, have great influences on agricultural production in Guangdong Province, China. Through literature analysis at home and abroad, it was pointed out that Chinese HJ satellites have become important sources of remote sensing images due to short imaging period and broad coverage (about 2 days and 700km). It also advances the free sharing of OLI data on the new generation of remote sensor of Landsat. Considering the severe orbit deviation and frequent phenology changes of agricultural land use, this paper described and laid emphasis on the necessity and perspective of developing multi-scale (1:500,000, 120,000, 150,000) agricultural land use products of Provincial-City-County within Guangdong through the usage of relatively fixed ground control point database and spectral library based advanced hierarchical classification technologies supported by HJ satellite data. Such a tendency of quick disaster emergency response for land use dynamic implies a technological focus on the usage of above-mentioned advanced HJ satellite technologies. Based on Landsat images, we also built the 11 united ground control points (40 points at most for whole Guangdong Province) and standardized the technologysystem of land use remote sensing mapping with the combination of spectral library-based hierarchy classification technology. Selected key technologies for enhancing land use production mapping efficiency and accuracy that involve Landsat OLI and HJ satellites are presented and discussed. The HJ satellite is an effective information source for land use dynamic mapping under emergency action such as disaster damage evaluation, which can provide a short imaging period with wide spatial coverage and enhance the ability of land use data acquirement from one to three or four times or so in one year. The building of rel
The shadow is a particular phenomenon in SAR images, inflecting some information of the target. However, the shadow edges are blurred in SAR images. Thus, we analyze the causes for the blurring phenomenon of shadow ed...
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ISBN:
(纸本)9781467372985
The shadow is a particular phenomenon in SAR images, inflecting some information of the target. However, the shadow edges are blurred in SAR images. Thus, we analyze the causes for the blurring phenomenon of shadow edges in terms of SAR imaging algorithms in this paper. Taking the range Doppler algorithm for example, we conduct four simulation experiments to compensate for different processing steps and compare the imaging discrepancy among the refocused shadow edges. The conclusions are that the blurring phenomenon of shadow edges in SAR images is mainly reflected in azimuth, and azimuth compression is the major impact-factor to the shadow imaging quality. It is obvious that optimization of azimuth compression should get more attention for shadow enhancement in SAR images.
Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters ...
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Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity togethe...
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ISBN:
(纸本)9781479983926
Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.
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