Row Of Image Research Articles

Two Level Image Encryption using Pseudo Random Number Generators

The escalating significance of securing data over the network has encouraged development of secure encryption algorithms. Cryptography is a technique that assists in development of such secure algorithms. It involves converting intelligible data into an unintelligible form to protect it from intruders. In this paper, the authors propose a simple and secure process to secure images. The image encryption process makes use of two Pseudo Random Number generators. In the first step, Linear Feedback Shift Register algorithm is used to swap the rows of the original image. This is followed by the swapping of the columns to produce an intermediary cipher image. In the second step, Blum Blum Shub algorithm is used to substitute the intensity of each pixel of the intermediary cipher image, to produce the final encrypted image. Various analysis tests are then performed to check the quality of the encrypted image. The test results prove the efficiency of the proposed image encryption process.

Vineyard detection from unmanned aerial systems images

In viticulture, the adoption of precision agriculture techniques is nowadays increasingly essential to reach required high product quality standards. New reliable tools for mapping crop variability indexes in a vineyard or a single parcel are necessary to deploy site-specific management practices. In this paper, a new method to automatically detect vine rows in gray-scale aerial images is presented. The developed image processing algorithm is constituted by three main steps based on dynamic segmentation, Hough Space Clustering and Total Least Squares techniques. The procedure’s reliability has also been proven in the presence of disturbance elements, like dense inter-row grassing, bushes and trees shadows, by properly detecting vine rows in the vineyard images. Moreover, its adaptive features allow it to obtain optimal results in the presence of uneven image illumination due, for example, to the presence of clouds or steep terrain slopes. The extracted row and inter-row information, besides being the basis for vineyard characterization maps computation, like vine plants vigor maps, could also be used as a reference for other precision viticulture tasks such as, for example, path planning of unmanned ground vehicles.

Cross grouping strategy based 2DPCA method for face recognition

Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method+LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA+LDA.

ХУДОЖНІ ВИМІРИ БОЖЕСТВЕННОГО І ДЕМОНІЧНОГО В РОМАНІ ОЛЕСЯ ІЛЬЧЕНКА «МІСТО З ХИМЕРАМИ»

In the article Oles Ilchenko’s novel “The city with chimeras” is investigated in terms of divine and demonic categories’ offered in the text. The artistic structure of the novel is designed with several semantic levels based on the row of archetypical images and motives which became the subjects of special interest in this work. The artistic transformation of cultural constants of temple, home, city and digital symbolics are explained in the article. The metaphorical comprehension of city space as home, universe and organism are investigated. The special attention is paid to the conception of history that demonstrates the connection of human existence and metaphysical sphere. The author makes the system of textual transitions between these levels of novel’s artistic structure. There are intertextual phenomena, the images of buildings and animals.

A superparticle filter for lane detection

We extend previously defined particle filters for lane detection by using a more general lane model supporting the use of two independent particle filters for detecting left and right lane borders separately, by combining multiple particles, traditionally used for identifying a winning particle in one image row, into one superparticle, and by using local linear regression for adjusting detected border points. The combination of multiple particles makes it possible to extend the traditional emphasis of particle-filter-based lane detectors (on identifying sequences of isolated border points) towards a local approximation of lane borders by polygonal or smooth curves further detailed in our local linear regression. The paper shows by experimental studies that results, obtained by the proposed novel lane detection procedure, improve compared to previously achieved particle-filter-based results especially for challenging lane detection situations. The presentation of several methods for comparative performance evaluation is another contribution of this paper.

Realizability of precompensators in linear multivariable systems: A structural approach

In this work, given a linear multivariable system, the problem of static state feedback realization of dynamic compensators is considered. Necessary and sufficient conditions for the existence of a static state feedback that realizes the dynamic compensator (square or full column rank compensator) are stated in structural terms, i. e., in terms of the zero-pole structure of the compensator, and the eigenvalues and the row image of the controllability matrix of the compensated system. Based on these conditions a formula is presented to find the state feedback matrices realizing a given compensator. It is also shown that the static state feedback realizing the compensator is unique if and only if the closed-loop system is controllable.

Erratum to: Amyloidogenic α-synuclein seeds do not invariably induce rapid, widespread pathology in mice

In our original paper, the rows of images in Fig. 1b were inverted and the bottom right (pSer129 stained) image was from the hypothalamic region and not the brainstem region as indicated in the figure legend. We have now correctly rearranged the rows of images to correspond with the text in the figure. We have also replaced the pSer129 immunostained image from the symptomatic, aging M83 mouse to reflect a region in the brainstem as indicated in the original legend.

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

Encrypting the compressed image by chaotic map and arithmetic coding

Due to the efficient and secure requirements of image transmission, a number of research works have been done to encrypt the compressed image. Inspired by the arithmetic coding and chaotic map which are used to compress and encrypt image, respectively. In this paper, a scheme is proposed to encrypt the compressed image by chaotic map and arithmetic coding. This scheme compresses the image row by row which is firstly permuted by two logistic maps before arithmetic coding. It not only enhances the security of arithmetic coding, but also improves the compression ratio. To further improve the security of binary data which has been compressed, we use the chaotic maps to encrypt the data, and set different parameters and initial value for chaotic maps. In order to possess high sensitivities of key and plain-image, the keys that are employed to determine the parameter and initial value of chaotic maps are related to the plain-image. The experimental results validate the effect of the proposed scheme and demonstrate that the compressed and encrypted image is secure and convenient for transmission.

A Novel Encryption Scheme for Digital Image - Based on One Dimensional Logistic Map

In this paper, an implementation of digital image encryption scheme based on one dimensional logistic map is proposed. The chaotic cryptography technique concentrates in general on the symmetric key cryptographic technique. In the proposed algorithm, a random key table lookup criterion was combined with a one-dimensional chaotic map were used for high degree 2-stage security image encryption while maintaining acceptable overhead delay time. The proposed algorithm is based on image row shuffling and pixel-wise XOR encryption. To increase the security of row shuffling variable rotation and inversion were applied to each shuffled row, based on the difference between old and new row location. The experimental results showed that the proposed algorithm is effective and applicable. The combination of logistic map and key table lookup shows advantages of large random key space and high-level of security. The resulting cipher image is suitable for practical use in secure image storing and transmission.

Robust curb detection with fusion of 3D-Lidar and camera data.

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes.

(2D)2PCA 알고리즘을 이용한 pRBFNNs 패턴분류기 기반 얼굴인식 시스템 설계

본 연구에서는 (2D)2PCA 알고리즘을 이용한 pRBFNNs 패턴분류기 기반 얼굴인식 시스템을 설계하였다. 기존의 1차원 PCA는 행과 열의 곱으로 표현한 이미지의 차원을 축소한다. 하지만 (2D)2PCA(2-Directional 2-Dimensional Principal Components Analysis)는 이미지의 행과 열에서 각각 차원축소를 수행한다. 그 다음 제안된 지능형 패턴분류기로 축소된 이미지를 사용하여 성능을 평가한다. (pRBFNNs)로 성능 평가를 한다. 제안된 다항식 기반 RBFNNs은 조건부, 결론부, 추론부 세가지의 기능적 모듈로 구성되어 있고 조건는 퍼지 클러스터링을 사용하여 입력 공간을 분할하고, 결론부는 RBFNNs의 연결가중치로 일차 선형식으로 표현한다. 또한 차분진화 알고리즘을 이용하여 제안된 분류기의 파라미터, 즉 입력의 수, 퍼지 클러스터링의 퍼지화 계수를 최적화 한다. 얼굴인식에 많이 사용되는 Yale과 AT&T를 사용하여 인식률을 평가하였다. 실험 평가를 위해 IC&CI 연구실 데이터를 추가하여 실험하였다.<BR><BR>

A fast image encryption algorithm based on only blocks in cipher text

In this paper, a fast image encryption algorithm is proposed, in which the shuffling and diffusion is performed simultaneously. The cipher-text image is divided into blocks and each block has k ×k pixels, while the pixels of the plain-text are scanned one by one. Four logistic maps are used to generate the encryption key stream and the new place in the cipher image of plain image pixels, including the row and column of the block which the pixel belongs to and the place where the pixel would be placed in the block. After encrypting each pixel, the initial conditions of logistic maps would be changed according to the encrypted pixel's value; after encrypting each row of plain image, the initial condition would also be changed by the skew tent map. At last, it is illustrated that this algorithm has a faster speed, big key space, and better properties in withstanding differential attacks, statistical analysis, known plaintext, and chosen plaintext attacks.

Compression of high-density EMG signals for trapezius and gastrocnemius muscles

BackgroundNew technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals. This article aims the compression of HD EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. It also shows methodological aspects of compressing HD EMG signals for non-pinnate (upper trapezius) and pinnate (medial gastrocnemius) muscles, using image compression techniques.MethodsHD EMG signals were placed in image rows, according to two distinct electrode orders: parallel and perpendicular to the muscle longitudinal axis. For the lossless case, the images obtained from single-differential signals as well as their differences in time were compressed. For the lossy algorithm, the images associated to the recorded monopolar or single-differential signals were compressed for different compression levels.ResultsLossless compression provided up to 59.3% file-size reduction (FSR), with lower contraction forces associated to higher FSR. For lossy compression, a 90.8% reduction on the file size was attained, while keeping the signal-to-noise ratio (SNR) at 21.19 dB. For a similar FSR, higher contraction forces corresponded to higher SNRConclusionsThe computation of signal differences in time improves the performance of lossless compression while the selection of signals in the transversal order improves the lossy compression of HD EMG, for both pinnate and non-pinnate muscles.

New Steganographic Technique using Neural Network

technique is used to hide the information, a string of characters containing the information, in a carrier image. The information is coded into individual rows of the constituent primaries of the carrier. Using this technique the neural network is employed to detect the presence of the message in the individual rows of the carrier image and to retrieve the contents of the message hidden in the carrier. This technique is able to maintain good visual quality of the carrier image. The results of this technique revealed high PSNR values and significantly less MSE values for the unmodified carrier and the steganographic image.

Gender Specification Using Touch less Fingerprint Recognition

Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints.Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. This article touches on two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance, and active capacitance).Most of the current security and attendance systems are shifting towards automated biometric systems, the most popular biometrics being fingerprints. In Automated Fingerprint Identification Systems (AFIS), the fingerprint of an individual needs to be identified with that stored in the database. In this is a method which deals with fingerprint identification in the transform domain is considered and the main focus is on the reduction of the processing time. First, the mean of rows (or columns) of the fingerprint image is computed, this converts a two dimensional image signal into one dimension. The one dimensional Walsh transform of the row (or column) vector is generated and is distributed in a complex plane which is subjected to sectorization to generate the feature vector.The feature vector of a given test image is compared to those present in the database. The scores from row and column transform methods are fused using OR and MAX functions. The results with accuracy of more than 73% (for 16 sectors) and high computational speed show that the method can be used in fingerprint identification in application with requirements of less processing time.

Modifications of iterative schemes used for curvature correction in noninvasive biomedical imaging

Iterative polynomial fitting along image rows and columns has recently been used to remove curvature bias in multispectral image sets of the human forearm and phantoms. However, this method is only applicable if foreground and background features satisfy strong separation conditions. In this method, we verify that the iterative polynomial approach converges toward bivariate polynomial fitting, and, hence, the resulting fit corresponds to low-pass filtering the image. In contrast to the iterative fitting, the bivariate polynomial fit can be performed on images with missing or excluded parts. Indeed, our observation enables us to modify the scheme and significantly weaken the required assumptions on foreground/background separation allowing a wider range of application.

Efficient and high performance FPGA-based rectification architecture for stereo vision

This paper proposes an efficient and high performance rectification architecture to be used as a preprocessing module in a complete stereo vision system before the matching correspondence calculus. A complete rectification process is implemented in order to remove the radial and tangential distortion effects due to lenses and to align the left and right raw images acquired by a stereo camera for the epipolar constraining. Thus, the epipolar lines are made collinear with each other and with the image scanning lines in order to reduce the complexity of the matching problem to a one-dimension correspondence search.The image transformation operations required by the rectification process are computed as matrix calculus through a pipelined and efficient hardware design. Unlike the memory mapped rectification function implementations, the proposed solution does not require any external memory block for the storage of pre-computed rectification maps. Moreover, conforming to the camera model adopted by the Stereo MATLAB Calibration Toolbox which is renowned as the most widely used software toolset for estimating the calibration parameters of a stereo camera, the proposed rectification architecture is a ready-to-use hardware solution to be used in stereo vision real-time embedded systems after calibrating the employed stereo camera following the MATLAB Calibration Toolbox procedure.When implemented in a Xilinx XC4VLX60-12ff1148 FPGA chip, the proposed circuit rectifies 640×480 and 1280×720 stereo images at a frame rate of 367fps and 120fps, respectively. The proposed fully pipelined solution uses an efficient raw image buffer system which is opportunely sized in order to store the minimum number of image rows able to guarantee the synchronization between the image buffering and the rectification elaboration without any interruption of the pipelined processing flow. When the proposed rectification system was used for processing the stereo images acquired by the Point Grey Research Bumblebee BB2-03S2 stereo camera, just 32 BRAM blocks were necessary to implement the raw image buffer; thus, after a latency of 136 us (15,387 clock cycles), a continuous flow of left and right rectified image pixels is guaranteed in output, for each inputted left and right couple of raw image pixels, at each clock cycle.When compared to the other implementations present in literature, the proposed solution offers the advantage of not using any external memory with respect to the memory-mapped rectification solutions while offering a more efficient and complete solution reaching the highest speed performance with respect to the on-the-fly computed rectification implementations present in literature.

2D2LPI

In this paper, a new method of representing images called two directional two dimensional locality preserving indexing called 2D2LPI is presented. It is an extension of the two dimensional locality preserving indexing (2DLPI) method. The authors argue that the recently proposed 2DLPI reduces the dimensions of images in row direction and we propose an alternate way of reducing the dimension in column direction. Later the authors propose a method to reduce the size of an image both in row and column directions. To corroborate the efficacy of the proposed two directional two dimensional approach the authors design a model for person identification based on single instance of finger knuckle print and subsequently the authors propose a feature level fusion of multi-instance finger knuckle print for person identification. Also to study the suitability of the proposed approach on a different domain, a study on video summarization is also presented in this paper. The results of the proposed method are compared with that of the state of the art techniques such as 2D2PCA, 2D2LPP and it is found that the proposed 2D2LPI model is more competitive in terms of accuracy.

Characterising flow patterns in soils by feature extraction and multiple consensus clustering

The quality of surface water and groundwater is closely related to flow paths in the vadose zone. Therefore, dye tracer studies are often carried out to visualise flow patterns in soils. These experiments provide images of stained soil profiles and their evaluation demands knowledge in hydrology as well as in image analysis and statistics. The classical analysis consists of image classification in stained and non-stained parts and calculation of the dye coverage (i.e. the proportion of staining). The variation of this quantity with depth is interpreted to identify dominant flow types. While some feature extraction from images of dye-stained profiles is necessary, restricting the analysis to the dye coverage alone might miss important information. In our study we propose to use several index functions to extract different (ideally complementary) features. We associate each image row with a feature vector (i.e. a certain number of image function values) and use these features to cluster the image rows to identify similar image areas. Because images of stained profiles might have different reasonable clusterings, we calculate multiple consensus clusterings. Experts can explore these different solutions and base their interpretation of predominant flow type on quantitative (objective) criteria.