Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas.Deep feature extraction is important for multispectral image classification and is evolving as a...Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas.Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection.However,many deep learning framework based approaches do not consider both spatial and textural details into account.In order to handle this issue,a Convolutional Neural Network(CNN)based multi-feature extraction and fusion is introduced which considers both spatial and textural features.This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features.Then the fused image is classified into change and unchanged regions.The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors.The proposed method was compared with six state-of-theart change detection methods and analyzed.The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels,the mixed pixel problem is solved.Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors,higher overall accuracy and kappa coefficient.展开更多
Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.On...Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.展开更多
A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This s...A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).展开更多
文摘Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas.Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection.However,many deep learning framework based approaches do not consider both spatial and textural details into account.In order to handle this issue,a Convolutional Neural Network(CNN)based multi-feature extraction and fusion is introduced which considers both spatial and textural features.This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features.Then the fused image is classified into change and unchanged regions.The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors.The proposed method was compared with six state-of-theart change detection methods and analyzed.The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels,the mixed pixel problem is solved.Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors,higher overall accuracy and kappa coefficient.
文摘Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.
文摘A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).
