There are two defects in WOW. One is image feature is not considered when hiding information through minimal distortion path and it leads to high total distortion. Another is total distortion grows too rapidly with hidden capacity increasing and it leads to poor anti-detection when hidden capacity is large. To solve these two problems, a new algorithm named MDIS was proposed. MDIS is also based on the minimizing additive distortion framework of STC and has the same distortion function with WOW. The feature that there are a large number of pixels, having the same value with one of their eight neighbour pixels and the mechanism of secret sharing are used in MDIS, which can reduce the total distortion, improve the antidetection and increase the value of PNSR. Experimental results showed that MDIS has better invisibility, smaller distortion and stronger anti-detection than WOW.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
Incorporating Index of Fuzziness and Adaptive Thresholding for Image Segmenta...IJECEIAES
Binary Segmentation of an image played an important role in many image processing application. An image that was having no bimodal (or nearly) histogram accompanied by low-contrast was still a challenging segmentation problem to address. In this paper, we proposed a new segmentation strategy to images with very irregular histogram and had not significant contrast using index of fuzziness and adaptive thresholding. Index of fuzziness was used to determine the initial threshold, while adaptive thresholding was used to refine the coarse segmentation results. The used data were grayscale images from related papers previously. Moreover, the proposed method would be tested on the grayscale images of malaria parasite candidates from thickblood smear that had the same problem with this research. The experimental results showed that the proposed method achieved higher segmentation accuracy and lower estimation error than other methods. The method also effective proven to segment malaria parasite candidates from thickblood smears image.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
Incorporating Index of Fuzziness and Adaptive Thresholding for Image Segmenta...IJECEIAES
Binary Segmentation of an image played an important role in many image processing application. An image that was having no bimodal (or nearly) histogram accompanied by low-contrast was still a challenging segmentation problem to address. In this paper, we proposed a new segmentation strategy to images with very irregular histogram and had not significant contrast using index of fuzziness and adaptive thresholding. Index of fuzziness was used to determine the initial threshold, while adaptive thresholding was used to refine the coarse segmentation results. The used data were grayscale images from related papers previously. Moreover, the proposed method would be tested on the grayscale images of malaria parasite candidates from thickblood smear that had the same problem with this research. The experimental results showed that the proposed method achieved higher segmentation accuracy and lower estimation error than other methods. The method also effective proven to segment malaria parasite candidates from thickblood smears image.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
A Robust Method for Moving Object Detection Using Modified Statistical Mean M...ijait
Moving object detection is low-level, important task for any visual surveillance system. One of the aim of this paper is to, to describe various approaches of moving object detection such as background subtraction, temporal difference, as well as pros and cons of these techniques. A statistical mean technique [10] has been used to overcome the problem in previous techniques. Even statistical mean method also suffers with the problem of superfluous effects of foreground objects. In this paper, the presented method tries to overcome this effect as well as reduces the computational complexity up to some extent. In this paper, a robust algorithm for automatic, noise detection and removal from moving objects in video sequences is presented. The algorithm considers static camera parameters.
DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTSijaia
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting thermophysical property data of metal systems, whose plot points are represented primarily by circles, triangles, and squares. We built a highly accurate single class U-Net convolutional neural network model to identify 97 % of image objects in a defined set of test images, locating the centers of the objects to within a few pixels of the correct locations. We found an optimal way in which to mark our training data masks to achieve this level of accuracy. The optimal markings for object classification, however, required more information in the masks to identify particular types of geometries. We show a range of different patterns used to mark the training data masks, and how they help or hurt our dual goals of location and classification. Altering the annotations in the segmentation masks can increase both the accuracy of object classification and localization on the plots, more than other factors such as
adding loss terms to the network calculations. However, localization of the plot points and classification of the geometric objects require different optimal training data.
Experimental analysis of matching technique of steganography for greyscale an...ijcsit
Steganography and steganalysis are vital matter in information hiding. Steganography refers to the
technology of hiding data into digital media without depiction of any misgiving, while steganalysis is the
detection of the existence of steganography. In this paper, the impact of sequential matching method for
every possible location in pixel is analyzed experimentally. After matching the steganalysis is check by
image quality measures (IQM) and Statistics, Histogram Analysis and visual attack. This analysis provides
comprehensive study and understanding of basic matching technique and is helpful for those who want to
work in the field of image steganography.
APPLICATION OF IMAGE FUSION FOR ENHANCING THE QUALITY OF AN IMAGEcscpconf
Advances in technology have brought about extensive research in the field of image fusion.
Image fusion is one of the most researched challenges of Face Recognition. Face Recognition
(FR) is the process by which the brain and mind understand, interpret and identify or verify
human faces.. Image fusion is the combination of two or more source images which vary in
resolution, instrument modality, or image capture technique into a single composite
representation. Thus, the source images are complementary in many ways, with no one input
image being an adequate data representation of the scene. Therefore, the goal of an image
fusion algorithm is to integrate the redundant and complementary information obtained from
the source images in order to form a new image which provides a better description of the scene
for human or machine perception. In this paper we have proposed a novel approach of pixel
level image fusion using PCA that will remove the image blurredness in two images and
reconstruct a new de-blurred fused image. The proposed approach is based on the calculation
of Eigen faces with Principal Component Analysis (PCA). Principal Component Analysis (PCA)
has been most widely used method for dimensionality reduction and feature extraction
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
DCT and Simulink Based Realtime Robust Image WatermarkingCSCJournals
Ownership of digital content has become a serious matter, due to the exponential raise in the global repository of digital multimedia content, like images are to be considered in this paper. The validated proof as an imperceptible and robust watermark is needed to be embedded in the digital images. This paper proposes a simulation of DCT with Fuzzy Logic based HVS model for Realtime Robust Image Watermarking technique using Simulink.
Prediction of Interpolants in Subsampled Radargram Slicesijtsrd
This paper provides an algorithmic procedure to predict interpolants of subsampled images. Given a digital image, one can subsample it by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75 of the information in the original image is lost in the subsampled image. The question that arises here is whether it is possible to predict these lost pixel values, which are called interpolants so that the reconstructed image is in accordance with the original image. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are i interpolant prediction using neighborhood pixel value averaging and ii interpolant prediction using extended morphological filtering. T. Kishan Rao | E. G. Rajan | Dr. M Shankar Lingam "Prediction of Interpolants in Subsampled Radargram Slices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38207.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/38207/prediction-of-interpolants-in-subsampled-radargram-slices/t-kishan-rao
Target Detection by Fuzzy Gustafson-Kessel AlgorithmCSCJournals
Many commercially available radar systems offer a range of filter options but the problem of clutter rejection for target detection is still present in a number of situations. Rejection of clutter and detection of targets from radar captured data is a challenging task. Raw data captured by radar are not always scaled. A normalization technique has been proposed which transforms the radar captured data into 8 bit. As 8 bit data is easy to analyze and visualize. A modification on Fuzzy c-means has been done by developing Fuzzy Gustafson–Kessel (FGK) algorithm and the result shows robustness of this proposed method.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes
severe damage to retina and may lead to complete or partial visual loss. In case of diabetic
retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked
out of the damaged blood vessels and are deposited in the intra-retinal space. They are
normally seen as whitish marks of various shape and are called as exudates. Exudates are
primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible
in nature, the disease must be detected in early stages to prevent visual loss. But detection of
exudates in early stages of the disease is extremely difficult only by visual inspection because of
small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such
method.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement ijsc
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
Pedestrian detection under weather conditions using conditional generative ad...IAESIJAI
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
A Secure Color Image Steganography in Transform Domain ijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
DETECTION OF HARD EXUDATES USING SIMULATED ANNEALING BASED THRESHOLDING MECHA...cscpconf
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes severe damage to retina and may lead to complete or partial visual loss. In case of diabetic retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked out of the damaged blood vessels and are deposited in the intra-retinal space. They are normally seen as whitish marks of various shape and are called as exudates. Exudates are primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible in nature, the disease must be detected in early stages to prevent visual loss. But detection of exudates in early stages of the disease is extremely difficult only by visual inspection because of small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such method.
Reference-free differential histogram-correlative detection of steganography:...nooriasukmaningtyas
Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless modified.
A Robust Method for Moving Object Detection Using Modified Statistical Mean M...ijait
Moving object detection is low-level, important task for any visual surveillance system. One of the aim of this paper is to, to describe various approaches of moving object detection such as background subtraction, temporal difference, as well as pros and cons of these techniques. A statistical mean technique [10] has been used to overcome the problem in previous techniques. Even statistical mean method also suffers with the problem of superfluous effects of foreground objects. In this paper, the presented method tries to overcome this effect as well as reduces the computational complexity up to some extent. In this paper, a robust algorithm for automatic, noise detection and removal from moving objects in video sequences is presented. The algorithm considers static camera parameters.
DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTSijaia
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting thermophysical property data of metal systems, whose plot points are represented primarily by circles, triangles, and squares. We built a highly accurate single class U-Net convolutional neural network model to identify 97 % of image objects in a defined set of test images, locating the centers of the objects to within a few pixels of the correct locations. We found an optimal way in which to mark our training data masks to achieve this level of accuracy. The optimal markings for object classification, however, required more information in the masks to identify particular types of geometries. We show a range of different patterns used to mark the training data masks, and how they help or hurt our dual goals of location and classification. Altering the annotations in the segmentation masks can increase both the accuracy of object classification and localization on the plots, more than other factors such as
adding loss terms to the network calculations. However, localization of the plot points and classification of the geometric objects require different optimal training data.
Experimental analysis of matching technique of steganography for greyscale an...ijcsit
Steganography and steganalysis are vital matter in information hiding. Steganography refers to the
technology of hiding data into digital media without depiction of any misgiving, while steganalysis is the
detection of the existence of steganography. In this paper, the impact of sequential matching method for
every possible location in pixel is analyzed experimentally. After matching the steganalysis is check by
image quality measures (IQM) and Statistics, Histogram Analysis and visual attack. This analysis provides
comprehensive study and understanding of basic matching technique and is helpful for those who want to
work in the field of image steganography.
APPLICATION OF IMAGE FUSION FOR ENHANCING THE QUALITY OF AN IMAGEcscpconf
Advances in technology have brought about extensive research in the field of image fusion.
Image fusion is one of the most researched challenges of Face Recognition. Face Recognition
(FR) is the process by which the brain and mind understand, interpret and identify or verify
human faces.. Image fusion is the combination of two or more source images which vary in
resolution, instrument modality, or image capture technique into a single composite
representation. Thus, the source images are complementary in many ways, with no one input
image being an adequate data representation of the scene. Therefore, the goal of an image
fusion algorithm is to integrate the redundant and complementary information obtained from
the source images in order to form a new image which provides a better description of the scene
for human or machine perception. In this paper we have proposed a novel approach of pixel
level image fusion using PCA that will remove the image blurredness in two images and
reconstruct a new de-blurred fused image. The proposed approach is based on the calculation
of Eigen faces with Principal Component Analysis (PCA). Principal Component Analysis (PCA)
has been most widely used method for dimensionality reduction and feature extraction
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
DCT and Simulink Based Realtime Robust Image WatermarkingCSCJournals
Ownership of digital content has become a serious matter, due to the exponential raise in the global repository of digital multimedia content, like images are to be considered in this paper. The validated proof as an imperceptible and robust watermark is needed to be embedded in the digital images. This paper proposes a simulation of DCT with Fuzzy Logic based HVS model for Realtime Robust Image Watermarking technique using Simulink.
Prediction of Interpolants in Subsampled Radargram Slicesijtsrd
This paper provides an algorithmic procedure to predict interpolants of subsampled images. Given a digital image, one can subsample it by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75 of the information in the original image is lost in the subsampled image. The question that arises here is whether it is possible to predict these lost pixel values, which are called interpolants so that the reconstructed image is in accordance with the original image. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are i interpolant prediction using neighborhood pixel value averaging and ii interpolant prediction using extended morphological filtering. T. Kishan Rao | E. G. Rajan | Dr. M Shankar Lingam "Prediction of Interpolants in Subsampled Radargram Slices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38207.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/38207/prediction-of-interpolants-in-subsampled-radargram-slices/t-kishan-rao
Target Detection by Fuzzy Gustafson-Kessel AlgorithmCSCJournals
Many commercially available radar systems offer a range of filter options but the problem of clutter rejection for target detection is still present in a number of situations. Rejection of clutter and detection of targets from radar captured data is a challenging task. Raw data captured by radar are not always scaled. A normalization technique has been proposed which transforms the radar captured data into 8 bit. As 8 bit data is easy to analyze and visualize. A modification on Fuzzy c-means has been done by developing Fuzzy Gustafson–Kessel (FGK) algorithm and the result shows robustness of this proposed method.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes
severe damage to retina and may lead to complete or partial visual loss. In case of diabetic
retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked
out of the damaged blood vessels and are deposited in the intra-retinal space. They are
normally seen as whitish marks of various shape and are called as exudates. Exudates are
primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible
in nature, the disease must be detected in early stages to prevent visual loss. But detection of
exudates in early stages of the disease is extremely difficult only by visual inspection because of
small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such
method.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement ijsc
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
Pedestrian detection under weather conditions using conditional generative ad...IAESIJAI
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
A Secure Color Image Steganography in Transform Domain ijcisjournal
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MINIMIZING DISTORTION IN STEGANOG-RAPHY BASED ON IMAGE FEATURE
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
DOI: 10.5121/ijcsit.2019.11106 77
MINIMIZING DISTORTION IN STEGANOG-RAPHY
BASED ON IMAGE FEATURE
Dong Wu
Department of Information Engineering, Lingnan Normal University,
ZhanJiang, China
ABSTRACT
There are two defects in WOW. One is image feature is not considered when hiding information through
minimal distortion path and it leads to high total distortion. Another is total distortion grows too rapidly
with hidden capacity increasing and it leads to poor anti-detection when hidden capacity is large. To solve
these two problems, a new algorithm named MDIS was proposed. MDIS is also based on the minimizing
additive distortion framework of STC and has the same distortion function with WOW. The feature that
there are a large number of pixels, having the same value with one of their eight neighbour pixels and the
mechanism of secret sharing are used in MDIS, which can reduce the total distortion, improve the anti-
detection and increase the value of PNSR. Experimental results showed that MDIS has better invisibility,
smaller distortion and stronger anti-detection than WOW.
KEYWORDS
Information Hiding; Minimal Distortion; Secret Sharing; Eight Neighbour Pixels; PSNR; Anti-Detection
1. INTRODUCTION
With the rapid development of information technology, people enjoy the convenience of the
information society but also suffer the threats of information disclosure and attacks. How to
ensure the security of information has become the focus of information research fields [1]. In
addition to the traditional information encryption technology, more and more scholars began to
focus on the research of steganography. In recent years, image-based steganography has
developed rapidly. In general it can be divided into two types of methods in which one is based
on spatial domain and another based on frequency domain. The methods based on spatial domain
have received much attention because of its large hidden capacity and simple implementation.
The more classic spatial information hiding algorithms are LSB (Least Significant Bit) and
Patchwork [2] early. These two algorithms are simple to implement, but have small hidden
capacity and poor anti-detection. In 2011, Gul et al. proposed an algorithm named HUGO(Highly
Undetectable steGO), which has larger capacity and higher anti-detection compared to LSB and
Patchwork. At the same year, Filler et al. proposed a complete practical methodology for
minimizing additive distortion in steganography with general (non-binary) embedding operation
using STC (Syndrome-Trellis Codes) [3]. Based on this framework, most of exist algorithms
including HUGO can be implemented by designing different distorting functions. In 2012, in
their later work, Holub and Fridrich proposed a new spatial algorithm named WOW which has
also large capacity and high anti-detection based on the former framework.
Experimental results in [4] show that the overall performance of the WOW is superior to HUGO,
LSB and Patchwork. After deep research, we found there are two defects in WOW. The total
distortion grows rapidly with the increase of hidden capacity, and the anti-detection of the
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
78
algorithm also decreases drastically. In this paper, we first introduce the basic idea and the defects
of WOW, then proposed a new information hiding algorithm named MDIS. MDIS and WOW use
the same distortion function. However, in the process of embedding the secret information using
the minimal distortion path, the information hiding rule is redesigned based on the facts that many
pixels always have the same value with one of its eight neighbour pixels. At the same time, the
mechanism of secret sharing [5] is also used in MDIS. There are two advantages in MDIS
compared to WOW. MDIS get smaller total distortion and higher anti-detection after using
feature mining of over image. It is also ensured that the anti-detection does not decrease
drastically when the hiding capacity increases after using the mechanism of secret sharing.
Experimental results show that MDIS is superior to WOW in terms of PNSR, total distortion and
anti-detection.
2. BASIC IDEA AND DEFECTS OF WOW
2.1. BASIC IDEA OF WOW
In 2012, WOW was proposed by Holub and Fridrich based on the STC framework. For a given
cover image(X) and secrete information (M), the stego image(Y) is got through the following
steps:
(1)Calculating the hiding capacity according to the cover image and secrete information. If
secrete information is m bits, the number of rows and columns is n1 and n2 in cover image, then
the hiding capacity is as follows:
Payl oad m n n1 2
/= × (1)
(2)Calculating the minimal distortion path through distortion function according to STC
framework. The distortion function is as follows:
1 2
1 1
( , ) ( , )
n n
i j i j i j i j
i j
D X Y X Y X Yρ
= =
= − (2)
In formula (2), Xi,j and Yi,j respectively represent the value of pixel(i,j) in cover image and stego
image. ρij is the distortion parameter when Xij becomes Yij . In WOW, WDFB-D Filter is used to
calculate the value of ρij . The specific calculation method is shown in formulas (3) and (4).
( ) ( ) ( ) ( )
[ , ]* *k k k k k k
ij i jR R R R Kε = − ≈ (3)
1
( ) ( )
1
( )ρ ε
−
=
=
pF
p k p
ij ij
k
(4)
In formula (3), for given filter { }(1 ) ( )
, ...,= F
nB K K , the value in the kth direction is
( ) ( )
*k k
R K X= . If the value of pixel (i,j) changes, then the new value in the kth direction is
[ , ]
k
i j
R
. In formula (4), the value of P is generally -1 and F represents the filtering direction.
(3)Information hiding is performed through change the value (plus or minus one) of pixels in
minimal distortion path.
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
79
2.2. DEFECTS OF WOW
WOW is best in existing spatial algorithms. However, we found that there are two defects in
WOW. One is that only the change of the value of pixels is considered in total distortion, but the
feature of cover image is not utilized to server for hiding information. Another is that the total
distortion grows obviously with the increase of hidden capacity, and the anti-detection of the
algorithm also decreases drastically. Here we analyze these two defects in detail.
(1)Analyzing the feature of cover image
Spatial redundancy is ubiquitous in image data and it is usually caused by the spatial coherence
between the colors of sampling points in a same scene surface. Especially the value of some pixel
is always the same with one of its eight neighbor pixels. In experiment 1, we selected randomly
200 images with different sizes and types, and then respectively counted the number of pixels,
having the same value with one of their eight neighbor pixels. The way of counting is shown in
formula (5) and the results are seen in table 1.
1 2
1 1
( X( i , j ) ( i , j ) ) 1
n n
i j
i f X Num Num
= =
′ ′== = + (5)
In formula (5), n1 and n2 is the number of rows and columns. X(i’,j’) is one of the eight neighbor
pixels which has the same value with pixel X(i,j).
In order to compare and analyze intuitively, the results of multiple images are averaged. The
specific statistical results are shown in Table 1:
Table 1. The number of pixels and percentage who have the same value with one of
their eight neighbor pixels
Image type Image size Number Percentage(Number/n1*n2)
landscape 600*800 226704 47.23%
people 680*1024 478094 68.66%
plants 780*1024 532946 66.73%
fruits 640*780 299970 60.09%
As can be seen from Table 1, there are nearly 50% pixels in the all randomly selected types of
images that have the same values with one of their eight neighbor pixels. If we can utilize this
feature to hide information, the change to cover image will be reduced. Then the total distortion
also can be reduced and the anti-detection ability of the algorithm can be improved too.
To illustrate this feature of image can be applied to the algorithm, we designed the experiment 2.
We selected randomly 50 images as cover images and use them to hide information with WOW.
Then we counted the number of pixels whose value changed in minimal distortion path and
among these pixels also counted the number of pixels who have the same values with one of their
eight neighbor pixels. The statistical results are shown in Table 2.
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
80
Table 2. The number of pixels whose values changed in minimal distortion path
Hiding Capacity Nchanged Nsame Percentage(Nsame/ Nchanged)
0.1 9040 1179 13.04%
0.2 20283 3074 15.16%
0.3 32970 5581 16.93%
0.4 46888 8666 18.48%
0.5 62107 12396 19.96%
0.6 78546 16829 21.43%
0.7 96299 22145 23.00%
0.8 114750 28375 24.73%
We can know that there are nearly 20% pixels whose values are same with one of their eight
neighbor pixels in the minimal distortion path in WOW from table 2. And with the hidden
capacity increasing, this image feature becomes more obvious. We can use this feature for
information hiding algorithm. For each pixel in minimal distortion path has this feature, it
represents a bit of hide information. In other word, without changing any value of pixels,
embedding hiding information is done. Then the total distortion will be reduced and anti-
detection will be increased.
(2)The total distortion growing rapidly with the increasing of hidden capacity
In wow, with the increasing of hiding capacity, the total distortion grows rapidly and caused a
decrease in anti-detection property. We have done experiments to compare the total distortion
with different hidden capacities and the results are shown in figure 1 and figure 2. Although the
total distortion of WOW is smallest compared to other algorithms (seen from figure 1), the total
distortion in WOW grows very fast with the increasing of hidden capacity (seen from figure 2).
Especially when the hiding capacity is greater than 0.5, the value of total distortion almost
becomes twice for hidden capacity increased by 0.1. We introduce the mechanism of secret
information sharing in MDIS to solve this problem.
Figure 1. Total distortion of all algorithms Figure 2. Total distortion of WOW
3. THE IDEA OF MDIS
We proposed a new algorithm named MDIS based on the above analysis of two defects in WOW.
In MDIS, we use the same distortion function with WOW, but we redesign the hiding rule
through the minimal distortion path. We combine the feature that there are lots of pixels whose
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
81
values are same with one of their eight neighbor pixels and hiding information together. In
addition we also join the mechanism of secret information sharing in MDIS. The basic idea of
MDIS is as follows:
(1) Combining Hiding Information and the Feature of Image Together
In MDIS, the minimal distortion path is found according to distortion function and hiding
capacity. For each pixel in the minimal distortion path, we first check if there exists one of eight
neighbor pixels whose value is same with this pixel. If the condition is true, it means there is a bit
(0 or 1) of hidden information and we need not do anything to hide information. If it is false, then
we select its adjacent pixel having the smallest distance and not in the main direction [6]
of the
image to do the operation (plus 1 or minus 1) to hide information.
We do this for the following reasons. First we avoid changing in the main direction to reduce the
impact of modification in cover image. Second, we choose to modify the adjacent pixels is to not
follow the traditional thinking in order to improve the security and reduce the probability of being
detected. Experimental results show that this hiding strategy provides MDIS better anti-detection
than WOW.
In the minimal distortion path, the algorithm for locating the position of the pixel where the secret
information is embedded is as follows:
For each pixel P(x,y) in the minimal distortion path
iSame = 0; //by default, no neighbor pixels whose value is same with this pixel
iDiff = 255; //by default, the distance is 255
changePos = (x,y); //By default, the pixel where the secret information is embedded
Foreach P(x1,y1) in sets of eight neighbor pixels
If (x1,y1) is not valid continue;
If P(x1,y1)== P(x,y) iSame=1;break;
iTempDiff = abs(P(x,y), P(x1,y1); //get the distance of these two pixels
if (iTempDiff < iDiff) && isNotMainDirection(x,y,x1,y1)
iDiff = iTempDiff;
changePos = (x1,y1); //the inner loop ends here
if (0 ==iSame)
p(changePos) = p(changePos) + 1;
or p(changePos) = p(changePos) - 1; //hiding information
(2) With the mechanism of secret information sharing
In MDIS, there is a parameter named MaxPayload. When the hidden capacity exceeds
MaxPayload, the secret information is divided into multiple parts using modulo operation. Each
part will be hidden in a same cover image. Of course, the hiding capacity in each cover image is
not exceeds MaxPayload. The design of dividing information includes three steps. Step one is
getting the value of N (round up) and N is equal to hidden capacity/MaxPayload. Step two is
choosing a random number M (M>=N) between 1to 9. Step three is using M, N and P (the
location of every bit of hide information) to divided hidden information into N parts according to
the value of P%M. Details are shown in figure 3:
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
82
Figure 3. N=4,M=9;the first part information(P%M =0,4,8),the second part
information(P%M =1,5) ,the third part information(P%M =2,6),the forth part
information(P%M =3,7)
After using secret sharing, there will be multiple stego images. So receiver must get complete
hidden information through the inverse of modulo operation from these stego images.
4. EXPERIMENTAL RESULTS AND ANALYSIS
In order to compare the performance of two hidden algorithms, MDIS and WOW, we focus on the
PNSR, total distortion and anti-detection in different hidden capacity.
4.1. COMPARING AND ANALYZING THE PNSR IN DIFFERENT HIDDEN CAPACITY
The hidden capacity of MDIS, WOW and HUTO is 0-0.8, but the hiding capacity of LSB is 0-
0.125. Here we choose PSNR to compare and analyze. PSNR is an important technical indicator
which can be used to determine whether the visual effect is good. PSNR also is an effective
parameter standard to judge the invisibility of the algorithm. The larger the value of PSNR, the
better the hiding effect is. We choose four standard images as cover images to do the test, four
standard test images are shown in figure 4.
(a)Lena (b) Barbara (c) Cameraman (d) baboon
Figure 4. Test images
We calculate the value of PSNR in different hidden capacity. The experimental results are shown
in table 3 and figure 6. From the results used Lena as cover image in table 3, we know that the
value of PSNR in MDIS is highest in the same hiding capacity and followed by WOW, HUGO
and LSB.
Table 3. The value of PSNR in different hidden capacity using Lena as cover image
Hiding Capacity PSNR(MDIS) PSNR(WOW) PSNR(HUGO) PSNR(LSB)
0.05
73.2421 72.5481 71.7548
55.1286
0.125
68.9979 67.9929 67.3914
51.1288
0.4
63.4265 62.0965 61.6407
——
0.8 60.1731 58.463 58.1732 ——
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 1, February 2019
83
There is a graphical representation of average PSNR of four standard images in different hidden
capacity and different hiding algorithms in figure 5. We know that the trend of PSNR is consistent
using all algorithms. The larger the hidden capacity, the smaller the value of PSNR is. But under
the same cover image and the same hidden capacity, the performance of MDIS is best. That
means the invisibility of MDIS is best.
Figure 5. The analysis of PSNR in MDIS, WOW and HUGO
4.2. THE ANALYSIS OF TOTAL DISTORTION IN STEGO IMAGE
Because there is a same distortion function in WOW and MDIS, so it is very meaningful to
compare the total distortion in the same hidden capacity. In the same hidden capacity, the one
which has larger distortion has worse anti-detection. Using the tool of MATLAB and choosing
randomly 50 images, we design two experiments. The first experiment is comparing and
analyzing the total distortion without secret sharing. The second experiment is comparing and
analyzing the total distortion with secret sharing. In the second experiment, there are multiply
stego images, so we get the average total distortion. For MDIS, if it is not using secret sharing, we
named it as MDIS-NS, else named it as MDIS. And we set MaxPayLoad = 0.3. The experimental
results are shown in figure 6.
Figure 6. The total distortion of WOW, MDIS-NS and MDIS
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From figure 6, although there is a same distortion function, even not using secret sharing, the total
distortion in MDIS is smaller than in WOW in a same hidden capacity. As the hidden capacity
increases, the gap between the two algorithms becomes more and more obvious. When the hidden
capacity is greater than 0.5, the total distortion in WOW is almost twice as in MDIS. That means
our operation for hiding information play a great role and the effect is very obvious. After using
secret sharing, the average total distortion in multiply stego images in MDIS is much smaller than
in WOW, and the effect is very prominent. Obviously, MDIS has much better performance in
total distortion than WOW.
4.3. COMPARING AND ANALYZING ANTI-DETECTION
In our experiments, we choose two features: SPAM and SRM to analyze the anti-detection of
algorithms [7, 8]
. We design two experiments to test the anti-detection in different hidden capacity
of three algorithms (MDIS-NS, MIDS and WOW). We set MaxPayload = 0.3 and use EOOB [9]
as
the measurement of anti-detection. The higher the value of EOOB, the higher the probability of
detection error, the better anti-detection performance of the algorithm has.
Figure 7. The anti-detection based on SPAM Figure 8. The anti-detection based on SRM
From figure 7 and figure 8, we can find that the trend of EOOB is very similar based on SPAM
and SRM. With the increasing of hidden capacity, the EOOB in MDIS-NS and WOW has been
falling too. But there is higher EOOB in MDIS-NS. Another point, the EOOB in MDIS is
basically kept parallel after the hidden capacity exceeds 0.3. That means the anti-detection does
not decrease with the increase of the hidden capacity. This is a very big improvement compared to
WOW. So, we can conclude that the performance of MDIS-NS and MDIS are better than WOW.
5. CONCLUSIONS
We proposed a new information hiding algorithm name MDIS, combining minimal distortion and
image feature together, based on two defects of WOW. At the same time, we joint a secret
information sharing mechanism in MDIS. The experimental results show that MDIS has better
performance than WOW. MDIS has higher PSNR, smaller total distortion and higher EOOB than
WOW. That means MDIS has better invisibility, smaller distortion and stronger anti-detection
than WOW. There is still room for improvement in MDIS. The cover images must be used when
the secret information is restored.
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ACKNOWLEDGEMENTS
The authors are grateful to the anonymous reviewers and the helpful suggestion given by the
partners. The research was supported by the Natural Science Foundation of Lingnan Normal
University (no.QL1307).
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AUTHORS
Dong Wu is currently a teacher in the Department of Information Engineering,
Lingnan Normal University, ZhanJiang, China. His research interests include image
processing, information hiding, and data mining.