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检索条件"任意字段=MIPPR 2007: Automatic Target Recognition and Image Analysis;and Multispectral Image Acquisition"

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multispectral remote sensing image cross simulation based on nonlinear spectral fitting model

Multispectral remote sensing image cross simulation based on...

引用

7th Symposium on multispectral image Processing and Pattern recognition (mippr) - multispectral image acquisition, Processing, and analysis

作者： Shen, Jinxiang Yang, Liao Chen, Xi Li, Junli Chinese Acad Sci Xinjiang Ecol & Geog Inst Urumqi 830011 Xinjiang Peoples R China

ISBN: (纸本)9780819485762

The remote sensing image recorded the ground object spectral responses with a special spectral, temporal, and spatial resolution. There are some complex relationship may exist between the remote sensing images with different spectral, spatial, and temporal scale. In this study, we try to use a nonlinear regression model - Cubist regression tree model to mining the spectral relationship between the image bands. The Landsat5 TM image was used as reference image to collect samples to train Cubist model, and then the target image - SPOT5 image was used to predict its lacked TM-liked band1 and band7 with the TM-trained Cubist model. The experiments shows that the Cubist nonlinear regression model could simulate TM band1 and band7 with a high accuracy and the TM-trained Cubist model also could be used to predict SPOT5 lacked TM-liked band1 and band7.

关键词： multispectral cross simulation SPOT5 TM Cubist

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Fast matching method between infrared image and optical image

Fast matching method between infrared image and optical imag...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Liu, Jing Sun, Jiyin Zhu, Junlin Luo, Xiaochun Xian Res Inst Xian 710025 Shanxi Peoples R China

ISBN: (纸本)9780819469502

In this paper, we present a high performance, coarse-to-fine scheme for image matching of infrared image and optical image. In order to eliminate the gray level difference and tolerance to distortion and noise existing in practice, this scheme uses the edge feature and combine a new similarity measures with modified HausdorfF distance to achieve the coarse-to-fine matching scheme. Our proposed method firstly extracts the feature points based on the method of Monte Carlo that reduce the computation load for the next matching, and then a new similarity measure is defined for coarse matching. Based on existing method of Monte Carlo evaluation the Huasdorff distance (MCHD), we define Monte Carlo modified Hausdorff distance (MCM-HD) to achieves the fine matching. Experiment are performed on a large test set and the result show that the fast search method diminishes the number of positions for calculate of Hausdorff distance, thus the computational load reduces and it is helpful to use Hausdorff distance in real time image matching. Compare with MCHD algorithm, our proposed method effectively improves the precision and reduces the execution time.

关键词： image matching Hausdorff distance (HD) Monte Carlo template matching wavelet infrared image optical image

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Object region extraction based on graph cut and application in image retrieval

Object region extraction based on graph cut and application ...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Guo, Li Wang, Lingjun Sun, Xinghua Yang, Jingyu Nanjing Univ Sci & Technol Sch Comp Nanjing 210094 Peoples R China

ISBN: (纸本)9780819469502

This paper introduces the technique of graph cut into the extraction of object region and applies the corresponding result of object region extraction into the image retrieval based on object region. The main idea of image retrieval based on object region is to use the feature of object region instead of the feature of global image to participate in the image retrieval. In the field of graphics there is a technique called graph cut, which can be used to figure out the contour of object under the interaction of users. The graph cut algorithm can be used to verify the correctness of object region extraction, and the users' input about seeds can be simulated according to the initial object region extracted. The usage of graph cut can make the object region extracted more precisely and thus the performance of image retrieval based on object region can be improved. Experiments show that the object region extraction algorithm based on graph cut is valid and the subsequent image retrieval results accord with the human visual perception much more than the ones without the usage of graph cut.

关键词： object region extraction graph cut image retrieval global image precision versus recall

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Facet-based adaptive anisotropic diffusion for image selective smoothing

Facet-based adaptive anisotropic diffusion for image selecti...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Wang, Guodong Sang, Nong Yan, Luxin Shen, Xubang Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multi Spectral Informat Proc Techno Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

In each step of anisotropic diffusion smoothing, noises must be managed to get better results. The mostly used method is Gaussian filtering. However, the standard deviation of the Gaussian filter can't be accurately obtained and it should change during the iterative process. Another problem is how to select a proper standard deviation to reducing noises while preserving edges. Actually, facet model fitting can be taken as a natural way to overcome the drawbacks mentioned above. Facet model fitting has the low-pass filtering performance adaptive to the image during evolution of diffusion;it can also achieve balanced results for noise reduction and edge preserving. Experiments show the method can preserve more edges as well as obtain higher peak signal-to-noise ratio as compared to other anisotropic diffusion based selective smoothing approaches.

关键词： facet-model image selective smoothing anisotropic diffusion

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SAR image edge detection combining radon transform with RDWT

SAR Image edge detection combining radon transform with RDWT

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Yang, Xinyan Jiao, Licheng Wang, Jiajun Soochow Univ Sch Elect & Informat Engn Suzhou 215021 Peoples R China Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819469502

The Synthetic Aperture Radar (SAR) is a promising sensor to obtain high-resolution images. But the presence of speckled noise brings many difficulties to SAR image processing, especially in edge detection. The edge detection methods available cannot solve this problem perfectly. An effective edge detection algorithm based on the directional information is presented, in which, two transforms are introduced, one is Fast Slant Stack transform, a new radon transform with the advantage of speed and invertibility, the other is Redundant Discrete Wavelet Transform(RDWT) whose best performance in edge detection is shift-variance characteristic. Besides, overlapped windows and soft threshold utilized to reduce the wrong detection probability and improve the location precision. Finally, comparisons with related methods are given in this paper. Experimental results prove the proposed algorithm can obtain valid edge information in SAR images.

关键词： SAR image edge detection fast slant stack transform RDWT

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Simultaneous acquisition of hyper-spectral image using the computed tomography imaging interferometer - art. no. 67865F

Simultaneous acquisition of hyper-spectral image using the c...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Lin, Yu Liao, Ningfang Wang, Xinquan Cui, Deqi Liang, Minyong Luo, Yongdao Beijing Inst Technol Dept Opt Engn Beijing 100081 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we introduce a new approach of simultaneous acquisition of hyper-spectral image by means of using Computed Tomography Imaging Interferometer. Detecting rapidly varying targets both spatially and spectrally using imaging spectrometer is an international phenomenon in remote sensing in recent years and worthwhile in many domains such as pollution inspection and biochemical arms detection. Computed Tomography Imaging Interferometer (CTII) we put forward previously is a kind of imaging spectrometer with merits of high resolution, high throughout and high signal-to-noise rate. In our current work, some transmogrifications have been brought into the existed model of CTII. More channels are constructed in the new type of CTII-Simultaneous CTII (SiCTII). The rotation system in each channel is made irrotianal. Snapshot can be brought in the acquisition by means of opening and closing channels simultaneously. The Algebraic Reconstruction Technique (ART) and Artificial Neural Network (ANN) are considered in the image reconstruction. Referring to the characteristics of SiCTII, a conclusion can be made that SiCTII is a kind of imaging spectrometer for rapid acquisition and can be applied in the instances, in which the spatial resolution is not highly demanded and rapid data acquisition is demanded.

关键词： imaging spectrometer imaging interferometer Fourier transform imaging spectrometer Computed Tomographic imaging spectrometer

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Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments

Research on benthic scene recognition using multi-scale self...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Yang Guoliang Peng Fuyuan Li Xutao Zhao Kun Chen Jingdong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we analyzed the increment distribution and the self-similarity behavior of texture images of three kinds of particular underwater objects during the mineral hunting process. The experimental data has shown that the H exponent of real underwater natural texture is not a constant over all scale range, but a variable with respect to the measure scale or time index. In order to investigate the multi-scale self-similarity behavior of the objects, we had extended the traditional FBM so that the self-similarity parameter H is taken as a variable H(s) with respect to measure scale s. The class-separability of self-similarity feature is measured, and the feature selection criterion is given. Pattern classification simulation experimental results have shown the effectiveness of the selected feature set combining the self-similarity parameter H, (3), the variance D(H,) and the increment variance AD. The correct ratio is up to 96% on average, which can be used in automatic detection and recognition for AUVs to complete their tasks.

关键词： seabed image model recognition multi-scale self-similarity feature selection

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A structured sub-pixel target detection method base on manifold learning method

A structured sub-pixel target detection method base on manif...

引用

7th Symposium on multispectral image Processing and Pattern recognition (mippr) - multispectral image acquisition, Processing, and analysis

作者： Du, Bo Zhang, Dengyi Li, Pingxiang Chen, Tao Wu, Ke Wuhan Univ Sch Comp Wuhan 430072 Peoples R China Wuhan Univ LIESMARS Wuhan 430072 Peoples R China China Univ Geosci Inst Geophys & Geomat Wuhan 430072 Peoples R China

ISBN: (纸本)9780819485762

The manifold learning theory is firstly used to transform the hyperspectral images into a low-dimension feature spaces. The reconstruction error is computed to get discriminative information. Then a structured matched subspace detector is developed. This method can effectively avoid the contamination by targets and spectral anomalies to backgrounds subspace and detect sub-pixel targets with better performance than traditional methods.

关键词： Manifold Learning target detection hyperspectral images

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Template coarse matching of SAR and optical images based on wavelet subbands and Hausdorff distance - art. no. 67862X

Template coarse matching of SAR and optical images based on ...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Mao, Xia Huang, Kang Liang, Yaoming Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China

ISBN: (纸本)9780819469502

This paper presents a suited approach to deal with the template coarse matching of synthetic aperture radar (SAR) and optical images based on wavelet subbands and Hausdorff distance. Firstly, we analyze the discrepancy of imaging character between SAR and optical images. Secondly, we occupy the max flat wavelet decomposition to obtain the template images, which also can suppress the speckle in SAR. Thirdly, improved canny algorithm is employed to extract the edge feature of SAR and optical images after image enhancement, respectively. Finally, this paper applies the modified Hausdorff distance with the optimum coefficient to the similarity measurement for the matching. The experimental results demonstrate the validity of the proposed methodology.

关键词： coarse matching SAR optical image wavelets Hausdorff distance

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Scene understanding with tri-training

Scene understanding with tri-training

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Zhu, Lin Zhou, Jie Song, Jingyan Tsinghua Univ Automat Dept Beijing 100084 Peoples R China

ISBN: (纸本)9780819469502

Scene understanding needs not only detecting objects in the scene, but also obtaining the relationship between the objects and the scene, for example the reasonable size and occurrence possibility of objects at one position in the scene. With this relationship, the traditional object detection approach, which may misclassify objects with wrong sizes or position of the scene, can be greatly improved. In this paper, a novel scale model is proposed to describe the understanding of the scene. The scale model consists of the occurrence possibility and the reasonable size of pedestrian in each position of the scene. The scale model is learned by counting the pedestrian examples with different sizes in different positions of the scene for a period of time, instead of computing the geometry and viewpoint information in a single image. The examples are detected automatically by a detector which is trained with tri-training based semi-supervised approach. Experimental results indicate that the scale model of the scene can be learned with semi-supervised detection without the information of the 3D geometry and the assumption of plain ground.

关键词： image understanding scale model semi-supervised training tri-training

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