scholarly journals A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

2021 ◽  
Vol 1 (3) ◽  
pp. 7-11
Author(s):  
Dinesh Kumar ◽  
Kailash Patidar ◽  
Gourav Saxena ◽  
Rishi Kushwaha
Keyword(s):  
Peripheral Vision ◽  
Visual Cryptography ◽  
Feature Space ◽  
Research Area ◽  
Image Feature ◽  
Sigmoid Function ◽  
Secret Data ◽  
Security Enhancement ◽  
Encrypt Data ◽  
Suggested Strategy

Visual encryption technology becomes the latest research area in which a lot of scopes persist. Presently such a particular cryptosystem procedure is now used by numerous other countries around the world for the private transmission of formal records, financial documents, content visuals, digital voting, and so on. Visualization Cryptographic algorithms one of the protected methods of transferring pictures online. The main benefit of image encryption has been that it disguises peripheral vision with encrypt data secret data with no computation usually needed. In this work a hybrid visual cryptography method using a sigmoid function (HVMSF) for enhancing the security in gray images. HVMSF strategy utilizes a chaos framework to scramble pixel values as well as blocks while using the Modified Arnold Cat Map method (MACM) as well as the Henon Map method (HMM). The methodology includes a confusion procedure wherein the location of each image pixel is shuffled by utilizing MACM. The shuffling of image pixel leads to the creation of a subset pixel which will be protected for transmitting. This proposed HVMSF mainly tries to overcome the limitation of the previous approaches by applying sigmoid function in image feature space for contrast enhancement throughout the consequent source images. The experimental outcomes precisely show that the suggested strategy can further give additional effectiveness to ensure the protection of transmitting information out over previous techniques.

scholarly journals A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

2021 ◽  
pp. 7-11
Author(s):  
Dinesh Kumar ◽  
◽  
Kailash Patidar ◽  
Mr. Gourav Saxena ◽  
Mr. Rishi Kushwaha ◽  
...  
Keyword(s):  
Peripheral Vision ◽  
Visual Cryptography ◽  
Feature Space ◽  
Research Area ◽  
Image Feature ◽  
Sigmoid Function ◽  
Secret Data ◽  
Security Enhancement ◽  
Encrypt Data ◽  
Suggested Strategy

Visual encryption technology becomes the latest research area in which a lot of scopes persist. Presently such a particular cryptosystem procedure is now used by numerous other countries around the world for the private transmission of formal records, financial documents, content visuals, digital voting, and so on. Visualization Cryptographic algorithms one of the protected methods of transferring pictures online. The main benefit of image encryption has been that it disguises peripheral vision with encrypt data secret data with no computation usually needed. In this work a hybrid visual cryptography method using a sigmoid function (HVMSF) for enhancing the security in gray images. HVMSF strategy utilizes a chaos framework to scramble pixel values as well as blocks while using the Modified Arnold Cat Map method (MACM) as well as the Henon Map method (HMM). The methodology includes a confusion procedure wherein the location of each image pixel is shuffled by utilizing MACM. The shuffling of image pixel leads to the creation of a subset pixel which will be protected for transmitting. This proposed HVMSF mainly tries to overcome the limitation of the previous approaches by applying sigmoid function in image feature space for contrast enhancement throughout the consequent source images. The experimental outcomes precisely show that the suggested strategy can further give additional effectiveness to ensure the protection of transmitting information out over previous techniques.

Feature Extraction of Video Using Artificial Neural Network

2017 ◽  
Vol 11 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Yoshihiro Hayakawa ◽  
Takanori Oonuma ◽  
Hideyuki Kobayashi ◽  
Akiko Takahashi ◽  
Shinji Chiba ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Recognition ◽  
High Performance ◽  
Feature Space ◽  
Middle Layer ◽  
Image Feature ◽  
Consecutive Series ◽  
Form Analysis ◽  
Low Dimensional

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.

scholarly journals Recognizing Cursive Typewritten Text Using Segmentation-Free System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad S. Khorsheed
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Feature Space ◽  
Recognition System ◽  
Image Feature ◽  
Text Recognition ◽  
Statistical Features ◽  
Free System ◽  
Novel Method ◽  
Image Pixels

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.

A Content Based Image Retrieval Model Using Feature Space Dividing

2014 ◽  
Vol 596 ◽  
pp. 388-393
Author(s):  
Guan Huang
Keyword(s):  
Image Retrieval ◽  
Fractional Order ◽  
Feature Space ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Time Cost ◽  
Retrieval Model ◽  
Matching Accuracy ◽  
Query Image ◽  
Proposed Model

This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

scholarly journals Security Enhancement of RSA Algorithm using Increased Prime Number Set

2020 ◽  
Vol 9 (3) ◽  
pp. 4235-4240
Keyword(s):  
Prime Number ◽  
Data Privacy ◽  
Search Algorithm ◽  
Prime Numbers ◽  
Secret Data ◽  
Number Count ◽  
Security Enhancement ◽  
Rsa Algorithm ◽  
Cryptographic Algorithm ◽  
Secure Data

In this era of digital age a lot of secret and non-secret data is transmitted over the internet. Cryptography is one of the many techniques to secure data on network. It is one of the techniques that can be used to ensure information security and data privacy. It is used to secure data in rest as well as data in transit. RSA in the most commonly used cryptographic algorithm and it is also used for the creation on Digital Certificates. RSA algorithm is now not considered to be as secure due to advancement in technology and newer attack vectors. This paper proposed an algorithm for security enhancement of RSA algorithm by increasing prime numbers count. Proposed algorithm has been implemented to encrypt and decrypt the data and execution results for encryption and decryption time have been compared for increased prime numbers count. This proposed algorithm of RSA can be used to replace the existing RSA algorithm in digital signature certificates as well as in all other places where the base RSA algorithm is currently being used. In the proposed technique, as the number of prime number count increases, prime factor calculation becomes difficult. If the attacker has encryption key (e) and Product of prime numbers (N) then it is not easy to find out the prime number combinations and hence decryption key (d) will be more secure by using proposed algorithm. This will be more difficult because given a number n, it is easy to find two numbers whose product is equal to n using Shor's algorithm and Grover’s Search Algorithm but it is not very difficult and time taking to exactly determine m numbers whose product is equal to n.

scholarly journals A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Hong Yang ◽  
Yasheng Zhang ◽  
Wenzhe Ding
Keyword(s):  
Feature Extraction ◽  
Structural Feature ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
Feature Space ◽  
Image Feature ◽  
Learning Method ◽  
Sample Data ◽  
Low Dimensional ◽  
Space Target

Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

scholarly journals SAC-NMF-Driven Graphical Feature Analysis and Applications

2020 ◽  
Vol 2 (4) ◽  
pp. 630-646
Author(s):  
Nannan Li ◽  
Shengfa Wang ◽  
Haohao Li ◽  
Zhiyang Li
Keyword(s):  
Matrix Factorization ◽  
3D Model ◽  
Hidden Variables ◽  
Three Dimensional ◽  
Feature Space ◽  
Research Area ◽  
Feature Analysis ◽  
Push Forward ◽  
Fundamental Research ◽  
Non Negative Matrix Factorization

Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth.

scholarly journals A text-Image feature mapping algorithm based on transfer learning

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1139-1148
Author(s):  
Deng Pan ◽  
Hyunho Yang
Keyword(s):  
Transfer Learning ◽  
Latent Dirichlet Allocation ◽  
Information Gain ◽  
Image Data ◽  
Feature Space ◽  
Image Feature ◽  
Similar Data ◽  
Feature Mapping ◽  
Mapping Algorithm ◽  
Text Feature

Abstract The traditional uniform distribution algorithm does not filter the image data when extracting the approximate features of text-image data under the event, so the similarity between the image data and the text is low, which leads to low accuracy of the algorithm. This paper proposes a text-image feature mapping algorithm based on transfer learning. The existing data is filtered by ‘clustering technology’ to obtain similar data with the target data. The significant text features are calculated through the latent Dirichlet allocation (LDA) model and information gain based on Gibbs sampling. Bag of visual word (BOVW) model and Naive Bayesian method are used to model image data. With the help of the text-image co-occurrence data in the same event, the text feature distribution is mapped to the image feature space, and the feature distribution of image data under the same event is approximated. Experimental results show that the proposed algorithm can obtain the feature distribution of image data under different events, and the average cosine similarity is as high as 92%, the average dispersion is as low as 0.06%, and the accuracy of the algorithm is high.

The Research on Image Segmentation Based on the Minimum Error Probability Bayesian Decision Theory

2011 ◽  
Vol 121-126 ◽  
pp. 1151-1155
Author(s):  
Zhi Yuan Chen ◽  
Gang Luo ◽  
Zhi Gen Fei
Keyword(s):  
Image Segmentation ◽  
Em Algorithm ◽  
Mixture Model ◽  
Error Probability ◽  
Feature Space ◽  
Image Feature ◽  
Bayesian Decision ◽  
Minimum Error ◽  
Novel Method ◽  
Image Edge

The image segmentation technology has been extensively applied in many fields. As the foundation of image identification, the effective image segmentation plays a significant role during the course of subsequent image processing. Many theories and methods have been presented and discussed about image segmentation, such as K-means and fuzzy C-means methods, method based on regions information, method based on image edge detection, etc. In this work, it is proposed to apply Bayesian decision-making theory based on minimum error probability to gray image segmentation. The approach to image segmentation can guarantee the segmentation error probability minimum, which is generally what we desire. On the assumption that the gray values accord with the probability distribution of Gaussian finite mixture model in image feature space, EM algorithm is used to estimate the parameters of mixture model. In order to improve the convergence speed of EM algorithm, a novel method called weighted equal interval sampling is presented to obtain the contracted sample set. Consequently, the computation burden of EM algorithm is greatly reduced. The final experiments demonstrate the feasibility and high effectiveness of the method.

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