A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-fre...A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.展开更多
In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left v...In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left view or right view.In this paper,we transfer the 2D image quality evaluation model to the stereo image quality evaluation,and this method solves the first problem;use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem.At the same time,the input image is preprocessed by phase congruency transformation,which further improves the performance of the algorithm.The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers.The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.展开更多
This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing...This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.展开更多
基金Supported by the National Natural Science Foundation of China(61072135)
文摘A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.
文摘In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left view or right view.In this paper,we transfer the 2D image quality evaluation model to the stereo image quality evaluation,and this method solves the first problem;use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem.At the same time,the input image is preprocessed by phase congruency transformation,which further improves the performance of the algorithm.The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers.The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.
基金Supported by the National Natural Science Foundation of China(No. 60625304)the National Key Project For Basic Research of China(Nos. G2007CB 311003 and 2009CB724002)
文摘This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.
