To improve the accuracy of illumination estimation while maintaining a relative fast execution speed, a novel learning-based color constancy using color edge moments and regularized regression in an anchored neighborh...To improve the accuracy of illumination estimation while maintaining a relative fast execution speed, a novel learning-based color constancy using color edge moments and regularized regression in an anchored neighborhood is proposed. First, scene images are represented by the color edge moments of various orders. Then, an iterative regression with a squared Frobenius norm（F-norm） regularizer is introduced to learn the mapping between the edge moments and illuminants in the neighborhood of the anchored sample.Illumination estimation for the test image finally becomes the nearest anchored point search followed by a matrix multiplication using the associated mapping matrix which can be precalculated and stored. Experiments on two standard image datasets show that the proposed approach significantly outperforms the state-of-the-art algorithms with a performance increase of at least 10. 35% and 7. 44% with regard to median angular error.展开更多
Image reconstruction can help to determine how well an image may be characterized by a small finite set of its moments. Also, we can identify the number of descriptors needed to describe an image. In this work, we pre...Image reconstruction can help to determine how well an image may be characterized by a small finite set of its moments. Also, we can identify the number of descriptors needed to describe an image. In this work, we present a comparative analysis using different set of discrete orthogonal moments in terms of normalized image reconstruction error (NIRE). Color image reconstruction is performed with different color channels and various orders of different discrete orthogonal moments. Finally the results obtained by the reconstruction of three color images with different families of orthogonal moments and an error analysis to compare their capacity of description are presented, also the conclusions obtained from this work are presented.展开更多
Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribu...Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.展开更多
Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes resea...Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.展开更多
基金The National Natural Science Foundation of China(No.61503303,51409215)the Fundamental Research Funds for the Central Universities(No.G2015KY0102)
文摘To improve the accuracy of illumination estimation while maintaining a relative fast execution speed, a novel learning-based color constancy using color edge moments and regularized regression in an anchored neighborhood is proposed. First, scene images are represented by the color edge moments of various orders. Then, an iterative regression with a squared Frobenius norm（F-norm） regularizer is introduced to learn the mapping between the edge moments and illuminants in the neighborhood of the anchored sample.Illumination estimation for the test image finally becomes the nearest anchored point search followed by a matrix multiplication using the associated mapping matrix which can be precalculated and stored. Experiments on two standard image datasets show that the proposed approach significantly outperforms the state-of-the-art algorithms with a performance increase of at least 10. 35% and 7. 44% with regard to median angular error.
文摘Image reconstruction can help to determine how well an image may be characterized by a small finite set of its moments. Also, we can identify the number of descriptors needed to describe an image. In this work, we present a comparative analysis using different set of discrete orthogonal moments in terms of normalized image reconstruction error (NIRE). Color image reconstruction is performed with different color channels and various orders of different discrete orthogonal moments. Finally the results obtained by the reconstruction of three color images with different families of orthogonal moments and an error analysis to compare their capacity of description are presented, also the conclusions obtained from this work are presented.
文摘Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2022 Yeungnam University Research Grant.
文摘Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.
