Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robust...Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.展开更多
To resolve the completeness and independence of an invariant set derived by the traditional method, a systematic method for deriving a complete set of pseudo-Zernike moment similarity （translation, scale and rotation...To resolve the completeness and independence of an invariant set derived by the traditional method, a systematic method for deriving a complete set of pseudo-Zernike moment similarity （translation, scale and rotation） invariants is described. First, the relationship between pseudo-Zernike moments of the original image and those of the image having the same shape but distinct orientation and scale is established. Based on this relationship, a complete set of similarity invariants can be expressed as a linear combination of the original pseudo-Zernike moments of the same order and lower order. The problem of image reconstruction from a finite set of the pseudo-Zernike moment invariants （PZMIs） is also investigated. Experimental results show that the proposed PZMIs have better performance than complex moment invariants.展开更多
Rotation,scaling and translation(RST)attacks can desynchronize watermark detection,which causes failure in many watermarking systems.In this paper,an image adaptive RST invariant watermark(AWPZM)is proposed by using t...Rotation,scaling and translation(RST)attacks can desynchronize watermark detection,which causes failure in many watermarking systems.In this paper,an image adaptive RST invariant watermark(AWPZM)is proposed by using the rotation invariant property of pseudo-Zernike moments(PZM)and odd-even quantization.PZM of the original image is computed first,and then those suitable for watermark generation are selected.Then,magnitudes of them are odd-even quantized to generate the watermark.In detection,a normalized hamming function is employed to determine the similarity of the watermark.Experimental results show its robustness to rotation and scaling.For traditional attacks,such as JPEG compression,added noise and filtering,the similarities are all above 0.95.展开更多
基金Supported by the Program for Changjiang Scholars and Innovative Research Team (2008)Program for New Centoury Excellent Talents in University(NCET-09-0045)+1 种基金the National Nat-ural Science Foundation of China (60773044,61004059)the Natural Science Foundation of Beijing(4101001)
文摘Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.
基金The National Natural Science Foundation of China(No.61071192,61073138)
文摘To resolve the completeness and independence of an invariant set derived by the traditional method, a systematic method for deriving a complete set of pseudo-Zernike moment similarity （translation, scale and rotation） invariants is described. First, the relationship between pseudo-Zernike moments of the original image and those of the image having the same shape but distinct orientation and scale is established. Based on this relationship, a complete set of similarity invariants can be expressed as a linear combination of the original pseudo-Zernike moments of the same order and lower order. The problem of image reconstruction from a finite set of the pseudo-Zernike moment invariants （PZMIs） is also investigated. Experimental results show that the proposed PZMIs have better performance than complex moment invariants.
基金This work was supported by the National Natural Science Foundation of China(Grant No.60572152)the Ph.D.Programs Foundation of the Ministry of Education of China(No.20060701004).
文摘Rotation,scaling and translation(RST)attacks can desynchronize watermark detection,which causes failure in many watermarking systems.In this paper,an image adaptive RST invariant watermark(AWPZM)is proposed by using the rotation invariant property of pseudo-Zernike moments(PZM)and odd-even quantization.PZM of the original image is computed first,and then those suitable for watermark generation are selected.Then,magnitudes of them are odd-even quantized to generate the watermark.In detection,a normalized hamming function is employed to determine the similarity of the watermark.Experimental results show its robustness to rotation and scaling.For traditional attacks,such as JPEG compression,added noise and filtering,the similarities are all above 0.95.
