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.
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.
MINIMIZING DISTORTION IN STEGANOG-RAPHY BASED ON IMAGE FEATUREijcsit
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.
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.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
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.
Performance Improvement of Vector Quantization with Bit-parallelism HardwareCSCJournals
Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.
Data Steganography for Optical Color Image CryptosystemsCSCJournals
In this paper, an optical color image cryptosystem with a data hiding scheme is proposed. In the proposed optical cryptosystem, a confidential color image is embedded into the host image of the same size. Then the stego-image is encrypted by using the double random phase encoding algorithm. The seeds to generate random phase data are hidden in the encrypted stego-image by a content-dependent and low distortion data embedding technique. The confidential image and secret data delivery is accomplished by hiding the image into the host image and embedding the data into the encrypted stego-image. Experimental results show that the proposed data steganographic cryptosystem provides large data hiding capacity and high reconstructed image quality.
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
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.
MINIMIZING DISTORTION IN STEGANOG-RAPHY BASED ON IMAGE FEATUREijcsit
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.
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.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
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.
Performance Improvement of Vector Quantization with Bit-parallelism HardwareCSCJournals
Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.
Data Steganography for Optical Color Image CryptosystemsCSCJournals
In this paper, an optical color image cryptosystem with a data hiding scheme is proposed. In the proposed optical cryptosystem, a confidential color image is embedded into the host image of the same size. Then the stego-image is encrypted by using the double random phase encoding algorithm. The seeds to generate random phase data are hidden in the encrypted stego-image by a content-dependent and low distortion data embedding technique. The confidential image and secret data delivery is accomplished by hiding the image into the host image and embedding the data into the encrypted stego-image. Experimental results show that the proposed data steganographic cryptosystem provides large data hiding capacity and high reconstructed image quality.
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
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.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
USING BIAS OPTIMIAZATION FOR REVERSIBLE DATA HIDING USING IMAGE INTERPOLATIONIJNSA Journal
In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method and the Neighbour Mean Interpolation (NMI) technique as scaling up to the original image occurs. Experimental results presented in this paper show that the proposed method has significantly improved embedding capacities over the approach proposed by Jung and Yoo.
A (2, N) VISUAL CRYPTOGRAPHIC TECHNIQUE FOR BANKING APPLICATIONSIJNSA Journal
In this paper a novel (2, n) visual cryptographic scheme has been proposed which may be useful in banking operations in the “either or survivor” mode where n is the number of generated shares, from which n-1 is the number of account holders in an account and one share should be kept to the bank authority. In this technique one account holder should stack his/her share with the share of the bank authority and the secret image for user authentication will be revealed. In this technique two consecutive pixels are taken as the one time input for the share generation process. This technique generates shares with less space overhead compared to existing techniques and may provide better security. It is also easy to implement like other techniques of visual cryptography.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
A Correlative Information-Theoretic Measure for Image SimilarityFarah M. Altufaili
A hybrid measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (low PSNR). The proposed measure, denoted by SjhCorr2, uses a combination of two parts: the first part is information - theoretic, while the second part is based on 2D correlation. The concept
of symmetric joint histogram is used in the information - heoretic part. The new measure shows the advantages of statistical approaches and information - theoretic approaches. The proposed similarity approach is robust under noise. The new measure outperforms the classical SSIM in detecting image similarity at low PSN.
A CHAOTIC CONFUSION-DIFFUSION IMAGE ENCRYPTION BASED ON HENON MAPIJNSA Journal
This paper suggests chaotic confusion-diffusion image encryption based on the Henon map. The proposed chaotic confusion-diffusion image encryption utilizes image confusion and pixel diffusion in two levels. In the first level, the plainimage is scrambled by a modified Henon map for n rounds. In the second level, the scrambled image is diffused using Henon chaotic map. Comparison between the logistic map and modified Henon map is established to investigate the effectiveness of the suggested chaotic confusion-diffusion image encryption scheme. Experimental results showed that the suggested chaotic confusion-diffusion image encryption scheme can successfully encrypt/decrypt images using the same secret keys. Simulation results confirmed that the ciphered images have good entropy information and low correlation between coefficients. Besides the distribution of the gray values in the ciphered image has random-like behavior.
Improving Performance of Back propagation Learning Algorithmijsrd.com
The standard back-propagation algorithm is one of the most widely used algorithm for training feed-forward neural networks. One major drawback of this algorithm is it might fall into local minima and slow convergence rate. Natural gradient descent is principal method for solving nonlinear function is presented and is combined with the modified back-propagation algorithm yielding a new fast training multilayer algorithm. This paper describes new approach to natural gradient learning in which the number of parameters necessary is much smaller than the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of algorithm and improve its performance.
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.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
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.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
USING BIAS OPTIMIAZATION FOR REVERSIBLE DATA HIDING USING IMAGE INTERPOLATIONIJNSA Journal
In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method and the Neighbour Mean Interpolation (NMI) technique as scaling up to the original image occurs. Experimental results presented in this paper show that the proposed method has significantly improved embedding capacities over the approach proposed by Jung and Yoo.
A (2, N) VISUAL CRYPTOGRAPHIC TECHNIQUE FOR BANKING APPLICATIONSIJNSA Journal
In this paper a novel (2, n) visual cryptographic scheme has been proposed which may be useful in banking operations in the “either or survivor” mode where n is the number of generated shares, from which n-1 is the number of account holders in an account and one share should be kept to the bank authority. In this technique one account holder should stack his/her share with the share of the bank authority and the secret image for user authentication will be revealed. In this technique two consecutive pixels are taken as the one time input for the share generation process. This technique generates shares with less space overhead compared to existing techniques and may provide better security. It is also easy to implement like other techniques of visual cryptography.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
A Correlative Information-Theoretic Measure for Image SimilarityFarah M. Altufaili
A hybrid measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (low PSNR). The proposed measure, denoted by SjhCorr2, uses a combination of two parts: the first part is information - theoretic, while the second part is based on 2D correlation. The concept
of symmetric joint histogram is used in the information - heoretic part. The new measure shows the advantages of statistical approaches and information - theoretic approaches. The proposed similarity approach is robust under noise. The new measure outperforms the classical SSIM in detecting image similarity at low PSN.
A CHAOTIC CONFUSION-DIFFUSION IMAGE ENCRYPTION BASED ON HENON MAPIJNSA Journal
This paper suggests chaotic confusion-diffusion image encryption based on the Henon map. The proposed chaotic confusion-diffusion image encryption utilizes image confusion and pixel diffusion in two levels. In the first level, the plainimage is scrambled by a modified Henon map for n rounds. In the second level, the scrambled image is diffused using Henon chaotic map. Comparison between the logistic map and modified Henon map is established to investigate the effectiveness of the suggested chaotic confusion-diffusion image encryption scheme. Experimental results showed that the suggested chaotic confusion-diffusion image encryption scheme can successfully encrypt/decrypt images using the same secret keys. Simulation results confirmed that the ciphered images have good entropy information and low correlation between coefficients. Besides the distribution of the gray values in the ciphered image has random-like behavior.
Improving Performance of Back propagation Learning Algorithmijsrd.com
The standard back-propagation algorithm is one of the most widely used algorithm for training feed-forward neural networks. One major drawback of this algorithm is it might fall into local minima and slow convergence rate. Natural gradient descent is principal method for solving nonlinear function is presented and is combined with the modified back-propagation algorithm yielding a new fast training multilayer algorithm. This paper describes new approach to natural gradient learning in which the number of parameters necessary is much smaller than the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of algorithm and improve its performance.
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.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
BEHAVIOR STUDY OF ENTROPY IN A DIGITAL IMAGE THROUGH AN ITERATIVE ALGORITHM O...ijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern
recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images
through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined.
The behavior of Shannon entropy is analyzed and then compared, taking into account the number of
iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The
use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m-
dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used
to group the the iterations, in order to caractrizes the performance of the algorithm
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 Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This paper presents two scenarios of image steganalysis, in first scenario, an alternative feature set for steganalysis based on
rate-distortion characteristics of images. Here features are based on two key observations: i) Data embedding typically increases the
image entropy in order to encode the hidden messages; ii) Data embedding methods are limited to the set of small, imperceptible distortions.
The proposed feature set is used as the basis of a steganalysis algorithm and its performance is investigated using different
data hiding methods. In second scenario, a new blind approach of image Steganalysis based on contourlet transform and nonlinear
support vector machine. Properties of Contourlet transform are used to extract features of images, the important aspect of this paper is
that, it uses the minimum number of features in the transform domain and gives a better accuracy than many of the existing stegananlysis
methods. The efficiency of the proposed method is demonstrated through experimental results. Also its performance is compared
with the Contourlet based steganalyzer (WBS). Finally, the results show that the proposed method is very efficient in terms of its detection
accuracy and computational cost.
This paper presents a simple technique to perform inverse halftoning using the deep learning framework. The proposed method inherits the usability and superiority of deep residual learning to reconstruct the halftone image into the continuous-tone representation. It involves a series of convolution operations and activation function in forms of residual block elements. We investigate the usage of pre-activation function and standard activation function in each residual block. The experimental section validates the proposed method ability to effectively reconstruct the halftone image. This section also exhibits the proposed method superiority in the inverse halftoning task compared to that of the handcrafted feature schemes and former deep learning approaches. The proposed method achieves 30.37 dB and 0.9481 on the average peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, respectively. It gives the improvements around 1.67 dB and 0.0481 for those values compared to the most competing scheme.
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
An artificial neural network approach for detecting skin cancerTELKOMNIKA JOURNAL
This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature
extraction method of the first order for feature extraction based on texture in order to get high degree of
accuracy with method of classification using artificial neural network (ANN). The method used is training
and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed
that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma
with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training
used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of
melanoma images and 17 as suspected melanoma images.
INFORMATION SATURATION IN MULTISPECTRAL PIXEL LEVEL IMAGE FUSIONIJCI JOURNAL
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more informative as well as perceptible to human eye. Multispectral image fusion is the
process of combining images from different spectral bands that are optically acquired. In this paper, we
used a pixel-level image fusion based on principal component analysis that combines satellite images of the
same scene from seven different spectral bands. The purpose of using principal component analysis
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement
1. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
DOI:10.5121/ijsc.2015.6202 17
FUZZY ENTROPY BASED OPTIMAL
THRESHOLDING TECHNIQUE FOR IMAGE
ENHANCEMENT
U.Sesadri1
,B. Siva Sankar2
, C. Nagaraju3
Assistant.Professor&Head of CSE, Vaagdevi Institute of Technology and Science,
Proddature1
M.Tech student of Vaagdevi Institute of Technology and Science, Proddatur2
Assoc. Professor&Head of CSE, YSRCE of YVU, Proddatur3
ABSTRACT
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.
KEY WORDS
fuzzy entropy, segmentation, soft computing, MAD and optimal thresholding
1. INTRODUCTION
Soft computing approaches have been applied to numerous real-world problems. Applications can
be found in image segmentation, pattern recognition, image enhancement and industrial
inspection, speech processing, robotics, naturallanguage understanding, etc. Thethresholding
technique is a reckless, modern and computationally low-cost segmentation technique that is
always serious and decisive in image enhancementuses. The value of image thresholding is not
always satisfactory because of the presence of noise and vagueness and ambiguity among the
classes. In [1], a three-level thresholding method has been presented for image enhancement
based on probability partition, fuzzy partition and entropy concept. This concept can be easily
extended to N-level (N>3) thresholding. However it fails for multi-resolution real images. In [2]
the image thresholding by using cluster organization from the histogram of an imageisproposed
based on inter-class variance of the clusters to be merged and the intra-class variance of the new
merged cluster. In [3], multi-level thresholding technique is presented for color image
segmentation by maximizing the conditional entropy but Fuzziness in images due to noise poses a
2. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
18
great challengein image segmentation and thresholding.In future the abovemethod may be
comprehensive to deal with noisy images by use offuzzy tools etc. In [4] this paper presents a
new histogram thresholding methodology using fuzzy and rough set theories. The power of the
proposed methodology lies in the fact that it does not make any prior assumptions about the
histogram unlike many existing techniques.In [5], fuzzy c means threshold clustering method was
used for underwater images.This emphasizesthe necessity of image segmentation, which splits an
image intoparts that take strong correlations with objects to reflect theactual information collected
from the real world when compare tooriginal image fuzzy method gives cut down value. In [6]
Unsupervised Image Thresholding using Fuzzy Measures is presented. This method successfully
segmenting the images ofbimodal and multi-model histograms for the automatic selection of
seedsubsets to decide the effective ROI. In [7], Bacterial Foraging (BF) algorithm based on
Tsallis objective function is presented for multilevel thresholding in image segmentation. In [8],
two-stage fuzzy set theoretic approach to image thresholding that uses the measure of fuzziness to
evaluate the fuzziness of an image and to find an optimal threshold value is proposed. But this
method is not appropriate to color images. In [9], fast image segmentation methods based on
swarm intelligence and 2-D Fisher criteria thresholding were used for image segmentation. In
[10], was used a procedure like thresholding by fuzzy c-means (THFCM) algorithm for image
segmentation to find an automatic threshold value. In [11], An Automatic Multilevel
Thresholding Method for Image segmentation was proposed based on Discrete Wavelet
Transforms and Genetic Algorithm. It worksonly for synthetic and real images. In [12] used
thresholding technique with genetic algorithm to find optimal thresholds between the various
objects and the background. In [13], an image segmentation framework which applied automatic
thresholding selection by means of fuzzy set theory and density model. In [14],Otsu andcanny
edge detectionswere the two techniques used for image segmentation. In [15], image
segmentation was implemented based on thresholding on Gaussian and salt & pepper noises. It is
not appropriate for other noises.
2. BI-LEVEL THRESHOLDING
The below gray-levelbimodal histogram shown in Fig 1) corresponds to an image, f1(x, y),
composed of objects overlapping with background of theimage. One exact way to extract the
objects from the background image is to select a threshold ‘Th’ that separates the modes. And
then any point (x, y) for which f1(x, y)>Th, is called an object point; if not, the point is called a
background point.
In given image, let the gray levels are L and the range is from {0, 1, 2... (L-1)}. Then the gray
level occurrence probability k is defined by following equation:
=
ℎ()
3. (0 ≤ ≤ ( − 1))
Where ℎ()is corresponding gray level number of pixels, N is the image total number of pixels
that is equal to ∑ ℎ().
The thresholding problem of bi-level can be described as follows
!(#) = $% + $% (1)
4. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
19
Where
$% = − '
(
)*
(
, ( = '
*,
-
-
$% = − '
(
)*
(
, ( = '
*,
And the eq. (1) maximizes optimal threshold in the gray level.
Fig1
This method produced better results for there the object and background pixels have gray levels
and grouped into two dominant modes.However bi-level thresholding technique fails for
overlapping image objects with background.
3. MULTILEVEL THRESHOLDING
The more general case of bi-level thresholding technique, where the multiple overlapping objects
characterizes the image histogram.Fig (2) shows thatuni-modelmultiple thresholding classifies a
point (x, y) as belonging to the one object class in Th1f1(x, y) ≤Th2, to the other object class if
f1(x, y) Th2, and to the background if f1(x, y)≤ Th1. The segmentation problems require
multiple thresholds for the quality enhancement. The multilevel threshold is the extension to the
Kapur’s entropy criterion and this method is treating as the optimal multilevel thresholding
problem.In the given image [th1, th2, th3…thm] the optimal threshold is calculated as follows and
it maximizes the following objective function:
(.#ℎ1, #ℎ2, … . , #ℎ1) = $% + $% + $%2 + ⋯ + $%4
Where
$% = − '
(
)*
(
, ( = '
-5
-5
5. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
20
$% = − '
(
)*
(
, ( = '
-52
-52
$%2 = − '
(2
)*
(2
, (2 = '
-56
-56
$%4 = − '
(4
)*
(4
, (4 = '
*,
Fig 2
This method also works for overlapping images by considering multiple threshold values but
however it is not suitable for low contrasted images.
4. FUZZY ENTROPY TECHNIQUE
The fuzzy entropy is a statistical measure of randomness in animage, the pixel values, and Dmi
occur with probabilitiespr(Dmi), which are given by the bin heights of the normalizedhistogram.
The maximum fuzzy entropy principle based on probability partition is defined as Let Dm = {
(i,j) : i=0,1,….M-1; j = 0,1,..N-1}, Gr = {0, 1… l-1}, where M, N and l are 3 positive integers.
Thus a digitized picture defines a map7: 9 → ;
. Let In(x,y) be the gray level value of the
image at the pixel (x,y)
7*(, ) ∈ ;
∀(, ) ∈ 9
9 = { (, ): 7*(, ) = , (, ) ∈ 9
K= 0, 1, 2... M-1
ℎ =
@A
B∗D
, = 0,1, … . . , − 1
Where* denotes the number of pixels in9. The following conclusions can be easily formed:
E 9 = 9, 9F ∩
4
9 = ∅( ≠ J)
0 ≤ ℎ ≤ 1, ' ℎ = 1, = 0,1, … . − 1
4
6. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
21
K =
(9K) =
∗
K
L
4 =
(94) =
∗
4
L
M =
(9M) =
∗
M
L
K
L +
4
L +
M
L = 1
NK() =
O
P
P
Q
P
P
R
1 ≤ 1
1 −
( − 1)2
(
1 − 1) ∗ (S1 − 1)
1 ≤ S1
( −
1)2
(
1 − 1) ∗ (
1 − S1)
S1 ≤
1
0
1
V
N4() =
O
P
P
P
P
P
Q
P
P
P
P
P
R
0 ≤ 1
( − 1)2
(
1 − 1) ∗ (S1 − 1)
1 ≤ S1
1 −
( −
1)2
(
1 − 1) ∗ (
1 − S1)
S1 ≤
1
1
1 ≤ 2
1 −
( − 2)2
(
2 − 2) ∗ (S2 − 2)
2 ≤ S2
( −
2)2
(
2 − 2) ∗ (
2 − S2)
S2 ≤
2
0
2
V
NM() =
O
P
P
Q
P
P
R
0 ≤ 2
( − 2)2
(
2 − 2) ∗ (S2 − 2)
2 ≤ S2
1 −
( −
2)2
(
2 − 2) ∗ (
2 − S2)
S2 ≤
2
1
2
V
Where six parameters p1, q1, r1, p2, q2, r2 satisfy the following condition:
0 1 ≤ S1 ≤
1 ≤ 2 ≤ S2 ≤
2 255
The fuzzy entropy function of every class is specifiedunder:
$%K = − '
∗ NK()
K
2XX
∗ ln [
∗ NK()
K
$%4 = − '
∗ N4()
4
2XX
∗ ln [
∗ N4()
4
7. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
22
$%M = − '
∗ NM()
M
2XX
∗ ln [
∗ NM()
M
Then the sum fuzzy entropy function is specified as follow:
$%(1, S1,
1, 2, S2,
2) = $%K + $%4 + $%M
Along with six variables p1, q1, r1, p2, q2, r2 the total fuzzy entropy is varied. An optimal
combination of (p1, q1, r1, p2, q2, r2) can be found.So that the total fuzzy entropy HE (p1, q1, r1,
p2, q2, r2) has the maximum value.
Fig 3
5. QUALITY PARAMETERS
5.1. Mean:mean is defined in terms of theaverage gray levels of the imagewith human
observations. In gray image the mean is calculated as
!*(]) =
^_
∑ ∑ `1(, )
_
a
^
b
Where P, Q are the width and height in terms of the gray level pixels the image and g1(x, y) is
gray value.
5.2. Standard deviation: the standard deviation of gray level image iscalculated as follows
c#(d) = e
^_
∑ ∑ (`1(, ) − ])
_
a
^
b
2
Where P, Q are the width and height of the image,] is mean of the image, g1(x,y) is gray level
value of the image,c#(d) is standard deviation
5.3. Mean Absolute deviation: The gray levels Mean Absolute deviation (MAD) from the
median is calculated as follows
f9 =
^_
∑ ∑ ⃒`1(, ) − !,*⃒
_
a
^
b
Where M, N are the width and height of the image in terms of pixels, median is the median gray
value of the image mask.
5.4. The Manhattan distance function computes the distance that would be traveled to get
from one data point to theother point. The Manhattan distance between two images is the sum of
the differences of their corresponding components.
Manhattan Distance = Abs (a-x) + Abs (b-y) +Abs(c-z)
8. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
23
Where (a,b,c) and (x,y,z) are two referenced points to bematched.
6. EXPERIMENTAL RESULTS
In this paper, we have presented a new gray level thresholdingalgorithm based on the close
relationship betweenthe image thresholding problem and the fuzzylogic. In order to evaluate the
performance of the proposed fuzzy entropy technique, it has been tested using imageswith low
contrast andspeckle noise.The parameters like mean, standard deviation, MAD and Manhattan
distance function are calculated and results are shown in the form tables and graphs. Table1 to
table3 show the bi-level, multi-level and fuzzy entropy technique’s mean standard deviation,
mean-absolute-deviation and Manhattan distance function values respectively. The fuzzy entropy
technique is applied on low contrasted name plate with altered noise levels. Fuzzy entropy
technique is better for low contrasted name plate with noise up to 65% of speckle noise. From the
evaluation of the resultant images,were solved that the fuzzy entropy thresholding technique
yields betterimages than those obtained by the widely used Bi-level thresholding and multi-level
thresholding methods.
Fig 4: Comparison of Three Methods with Speckle Noise
9. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
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Tabe1: bi-level thresholdingTable2: Multi-level thresholdingTable3: fuzzy entropy thresholding
Graph1: Mean Graph2: standard deviation
Graph3: mean absolute deviationGraph 4: Manhattan distance
7. CONCLUSION
In this paper the optimal threshold values are determined by three thresholding techniques of bi-
level, multi-level and fuzzy entropy and they are tested with a variety of representing low
contrasted as well as natural images with noise for Image enhancement. In proposed method,
fuzzy entropy thresholding is used for image enhancement and compared results with existing bi-
level and multi-level thresholding methods and proved that proposed method best fit for low
contrasted with speckle noise images. The tables and graphs are constructed by standard
deviation, mean, MAD and Manhattan distance function. Experiments on low contracted, noisy
and real images have proved the robustness of the proposed technique. Yet the fuzzy entropy
technique is not appropriate for salt pepper and Gaussian noises.
11. International Journal on Soft Computing (IJSC) Vol.6, No. 2, May 2015
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AUTHORS
U. Sesadri is currently working as Assistant. Professor and HOD in the Department of
CSE in Vaagdevi Institute of Technology science, Proddatur, KadapaDistrict,Andhra
Pradesh, India. He received his M.Sc. in Mathematics from SV University, Tirupati, M.E
in Computer Science and Engineering from Sathyabama University, Chennai and pursing
PhD in Digital Image Processing from V.T. University, Belgaum. He got 7 years of
teaching experience. He attended five National Level workshops and two international
level conferences. He organized 10 National levelworkshops and two National level paper
presentations.
Bodicherla Siva Sankar is doing his M. tech in Vaagdevi Institute of Technology,
Proddatur, Kadapa, A.P, India. He received his B.Tech degree in Information Technology
from J.N.T. University Anantapur and he has attended two workshops on cloud computing
Big Data and Research challenges in Digital Image Processing.
Dr. C. Naga Raju is currently working as Associate Professor and Head of the
Department of Computer Science and Engineering at YSR Engineering College of
Yogivemana University, Proddatur, Kadapa District, and Andhra Pradesh, India. He
received his B.Tech Degree in Computer Science and Engineering from J.N.T.University,
Anantapur, and M.Tech Degree in Computer Science from J.N.T.University Hyderabad
and PhD in digital Image processing from J.N.T.University Hyderabad. He has got 18
years of teaching experience. He received research excellence award, teaching excellence award and
Rayalaseemavidhyaratna award for his credit. He wrote text book on C Data structures and Pattern
Recognition. He has six PhD scholars. He has published fifty six research papers in various National and
International Journals and about thirty research papers in various National and International Conferences.
He has attended twenty seminars and workshops. He delivered 10 keynote addresses. He is member of
various professional societies like IEEE, ISTE and CSI.