A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. S...A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.展开更多
Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower...Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).展开更多
Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust...Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.展开更多
A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images,...A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.展开更多
A novel method for multi-image matching by synthesizing image and object-space information is proposed. Firstly, four levels of image pyramids are generated according to the rule that the next pyramid level is generat...A novel method for multi-image matching by synthesizing image and object-space information is proposed. Firstly, four levels of image pyramids are generated according to the rule that the next pyramid level is generated from the previous level using the average gray values of the 3 by 3 pixels, and the first level of pyramid image is generated from the original image. The initial horizontal parallaxes between the reference image and each searching image are calculated at the highest level of the image pyramid. Secondly, corresponding image points are searched in each stereo image pair from the third level of image pyramid, and the matching results in all stereo pairs are integrated in the object space, by which the mismatched image points can be eliminated and more accurate spatial information can be obtained for the subsequent pyramid image matching. The matching method based on correlation coefficient with geometric constraints and global relaxation matching is introduced in the process of image matching. Finally, the feasibility of the method proposed in this paper is verified by the experiments using a set of digital frame aerial images with big overlap. Compared with the traditional image matching method with two images, the accuracy of the digital surface model (DSM) generated using the proposed method shows that the multiimage matching method can eliminate the mismatched points effectively and can improve the matching success rate significantly.展开更多
A new method for the retrieval of ocean wave parameters from SAR imagery is developed,based on the shape-from-shading(SFS)technique.Previously,the SFS technique has been used in the reconstruction of 3D landform infor...A new method for the retrieval of ocean wave parameters from SAR imagery is developed,based on the shape-from-shading(SFS)technique.Previously,the SFS technique has been used in the reconstruction of 3D landform information from SAR images,in order to generate elevation maps of topography for land surfaces.Here,in order to retrieve ocean wave characteristics,we apply the SFS methodology,together with a method to orient the angular measurements of the azimuth slope and range slope,in the measurement of ocean surface waves.This method is applied to high resolution fine-quad polarization mode(HH,VV,VH and HV)C-band RADARSAT-2 SAR imagery,in order to retrieve ocean wave spectra and extract wave parameters.Collocated in situ buoy measurements are used to validate the reliability of this method.Results show that the method can reliably estimate wave height,dominant wave period,dominant wave length and dominant wave direction from C-band SAR images.The advantage of this method is that it does not depend on modulation transfer functions(MTFs),in order to measure ocean surface waves.This method can be used in monitoring ocean surface wave propagation through open water areas into ice-covered areas,especially the marginal ice zone(MIZ)in polar oceans.展开更多
基金Projects(90820302, 60805027) supported by the National Natural Science Foundation of ChinaProject(200805330005) supported by Research Fund for Doctoral Program of Higher Education, ChinaProject(2009FJ4030) supported by Academician Foundation of Hunan Province, China
文摘A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.
基金Project (Nos. 60302012 60202002) supported by the NationaNatural Science Foundation of China and the Research GrantCouncil of the Hong Kong Special Administrative Region (NoPolyU 5119.01E) China
文摘Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).
文摘Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.
基金partially supported by the National Natural Science Foundation of China under Grants No. 61071146, No. 61171165the Natural Science Foundation of Jiangsu Province under Grant No. BK2010488+1 种基金sponsored by Qing Lan Project, Project 333 "The Six Top Talents" of Jiangsu Province
文摘A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.
基金Supported by the National Natural Science Foundation of China (Nos. 40771176, 40721001)
文摘A novel method for multi-image matching by synthesizing image and object-space information is proposed. Firstly, four levels of image pyramids are generated according to the rule that the next pyramid level is generated from the previous level using the average gray values of the 3 by 3 pixels, and the first level of pyramid image is generated from the original image. The initial horizontal parallaxes between the reference image and each searching image are calculated at the highest level of the image pyramid. Secondly, corresponding image points are searched in each stereo image pair from the third level of image pyramid, and the matching results in all stereo pairs are integrated in the object space, by which the mismatched image points can be eliminated and more accurate spatial information can be obtained for the subsequent pyramid image matching. The matching method based on correlation coefficient with geometric constraints and global relaxation matching is introduced in the process of image matching. Finally, the feasibility of the method proposed in this paper is verified by the experiments using a set of digital frame aerial images with big overlap. Compared with the traditional image matching method with two images, the accuracy of the digital surface model (DSM) generated using the proposed method shows that the multiimage matching method can eliminate the mismatched points effectively and can improve the matching success rate significantly.
基金This work was supported by the National Natural Science Foundation of China(Grant NO.41276187)the Global Change Research Program of China(Grant No.2015CB953901)+3 种基金the Startup Foundation for Introducing Talent of NUIST(Grant No.20110310)Program for Innovation Research and Entrepreneurship Team in Jiangsu Provincethe CFOSAT project,the Canadian Program on Energy Research and Developmentthe Canadian Space Agency GRIP program funding for wave-ice interactions
文摘A new method for the retrieval of ocean wave parameters from SAR imagery is developed,based on the shape-from-shading(SFS)technique.Previously,the SFS technique has been used in the reconstruction of 3D landform information from SAR images,in order to generate elevation maps of topography for land surfaces.Here,in order to retrieve ocean wave characteristics,we apply the SFS methodology,together with a method to orient the angular measurements of the azimuth slope and range slope,in the measurement of ocean surface waves.This method is applied to high resolution fine-quad polarization mode(HH,VV,VH and HV)C-band RADARSAT-2 SAR imagery,in order to retrieve ocean wave spectra and extract wave parameters.Collocated in situ buoy measurements are used to validate the reliability of this method.Results show that the method can reliably estimate wave height,dominant wave period,dominant wave length and dominant wave direction from C-band SAR images.The advantage of this method is that it does not depend on modulation transfer functions(MTFs),in order to measure ocean surface waves.This method can be used in monitoring ocean surface wave propagation through open water areas into ice-covered areas,especially the marginal ice zone(MIZ)in polar oceans.
