This paper presents an eye and iris detection algorithm for human facial images. The authors combine three features of the eye to develop the algorithm：1） the pixels surrounding the eyes are more variable than other...This paper presents an eye and iris detection algorithm for human facial images. The authors combine three features of the eye to develop the algorithm：1） the pixels surrounding the eyes are more variable than other parts of the face; 2） eye pixels are darker than their neighbors; 3） eyes often exhibit radial symmetric properties. Through the first feature,two rough regions of both eyes are detected on the face. Eye masks are then formed based on the second feature,and a fast radial symmetry transform is applied to the two rough regions of both eyes. Finally,accurate iris centers are located by searching the maximum value of the radial symmetry transform results. Using 450 human facial images from the Caltech face database,experiments show that the success rate of the proposed method is 91.7%. The effectiveness of the method was also verified through detection of video frames.展开更多
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
基金Research Project of Pilot Fatigue Monitoring System Based on Computer Vision (No.MHR06Z16)
文摘This paper presents an eye and iris detection algorithm for human facial images. The authors combine three features of the eye to develop the algorithm：1） the pixels surrounding the eyes are more variable than other parts of the face; 2） eye pixels are darker than their neighbors; 3） eyes often exhibit radial symmetric properties. Through the first feature,two rough regions of both eyes are detected on the face. Eye masks are then formed based on the second feature,and a fast radial symmetry transform is applied to the two rough regions of both eyes. Finally,accurate iris centers are located by searching the maximum value of the radial symmetry transform results. Using 450 human facial images from the Caltech face database,experiments show that the success rate of the proposed method is 91.7%. The effectiveness of the method was also verified through detection of video frames.
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.
