Pixel Pairs Research Articles

High capacity turtle shell‐based data hiding

Data hiding is a technique for sending secret information under the cover of the digital media. It is usually used to protect privacy and sensitive information when such information is transmitted via a public network. To date, high capacity remains one of the most important research aspects of data hiding. In this study, a new, turtle shell-based data hiding scheme is proposed to improve embedding capacity further while guaranteeing good image quality. In the proposed, turtle shell-based scheme, a reference matrix is composed and a location table is generated. Then, according to the reference matrix and the location table, each pixel pair is processed to conceal four secret bits. The experimental results indicated that the proposed scheme achieved higher embedding capacity and lower distortion of images than some existing schemes.

A New Probabilistic Approach Based on Markov Process for Matching Problem

Stereo matching is one of the main topics in computer vision. It consists to find in two images of a same scene, taken from different viewpoints, the pairs of pixels which are the projections of a same scene point. Since the last twenty years, many local and global methods have been proposed to solve this problem. Such stereo configurations reduce the search for correspondences from two-dimensions (the entire image) to one-dimension; the epipolar constraint can be used to facilitate matching by reducing the search. The application area of the matching problem is very wide, including biological sequences, the comparison of different files, speech recognition.In this paper, we will present a new probabilistic approach method based on Markov process for the matching of two epipolar images; where each scan-line is delimited by two successive points of interest.

Multi‐round dynamic swap optimisation for table‐based steganography

The reference-table-based (RTB) steganography employs two pixels to conceal a secret digit, according to a predetermined reference table. Most of the RTB steganographic schemes discussed in the literature do not consider the non-uniformity of the secret digits and the locating numbers indicated by the pixel pairs. In contrast, the non-uniformity of the locating numbers exists for most of host images. When a non-uniform secret sequence is embedded, the quality control of the stego image is quite challengeable when compared with a uniform secret sequence. To the best of the authors knowledge, most of the existing algorithms focus on the uniform sequences. In this study, they propose a multi-round dynamic swap optimisation (MRDSO) algorithm for RTB steganography, which is more suitable for non-uniform secret sequences. By optimising the permutation of the reference table, the proposed algorithm increases the scenarios that the locating number indicated by the pixel pair is equal to the secret digit, which means that these host pixels can carry the secret digit without any distortion. The experimental results show that the proposed MRDSO algorithm increases the quality of the stego image by 0.1–1.7 dB.

Two-Stage Change Detection for Synthetic Aperture Radar

Coherent change detection using paired synthetic aperture radar (SAR) images is often performed using a classical coherence estimator that is invariant to the true variances of the populations underlying each paired sample. While attractive, this estimator is biased and requires a significant number of samples to yield good performance. Increasing sample size often results in decreased image resolution. Thus, we propose the use of Berger's coherence estimate because, with the same number of pixels, the estimator effectively doubles the sample support without sacrificing resolution when the underlying population variances are equal or near equal. A potential drawback of this approach is that it is not invariant since its distribution depends on the pixel pair population variances. While Berger's estimator is inherently sensitive to the inequality of population variances, we propose a method of insulating the detector from this acuity. A two-stage change statistic is introduced to combine a noncoherent intensity change statistic given by the sample variance ratio, followed by the alternative Berger estimator, which assumes equal population variances. The first-stage detector identifies pixel pairs that have nonequal variances as changes caused by the displacement of sizeable object. The pixel pairs that are identified to have equal or near-equal variances in the first stage are used as an input to the second stage. The second-stage test uses the alternative Berger coherence estimator to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method yields higher contrast SAR change detection images than the classical coherent change detector (state of the art), the alternative coherent change detector, and the intensity change detector. Experimental results are presented to show the effectiveness and robustness of the proposed algorithm for SAR change detection.

A Robust Imaging Algorithm for Squint Mode Multi-Channel High-Resolution and Wide-Swath SAR With Hybrid Baseline and Fluctuant Terrain

In this paper, the squint mode multi-channel (MC) synthetic aperture radar (SAR) with hybrid baseline and fluctuant terrain is proposed and studied for high-resolution and wide-swath (HRWS) imaging. During the imaging process, due to the cross-track baseline and fluctuant terrain, the azimuth signal reconstruction is the kernel problem for this imaging mode. To deal with this problem, in this paper a robust azimuth signal reconstruction approach is proposed, where terrain elevation of scene is considered. At first, the pre-processing of the linear range cell migration correction (RCMC) and topography-independent phase compensation is implemented in the azimuth time domain. After that, combining the azimuth echo signal characteristics, the local polynomial Fourier transform (LPFT) is utilized to obtain the coarse-focused SAR image. Then, based on joint pixel pair vector and robust Capon beamforming (RCB), a Doppler ambiguity suppression approach is proposed to reconstruct the Doppler ambiguity-free azimuth signal in LPFT frequency domain, during which the influence of the cross-track baseline component and fluctuant terrain is eliminated using the coarse digital elevation model (DEM) for the imaging scene. At last, the chirp scaling imaging algorithm is utilized to focus the SAR image. The effectiveness of the proposed imaging approach is demonstrated via simulated and real measured squint mode MC-HRWS SAR data.

Structure matching driven by joint-saliency-structure adaptive kernel regression

Matching outlier structures with missing correspondences and/or local large deformations is very difficult in image registration. In this paper, we define structure matching as an iterative local adaptive kernel regression which locally reconstructs moving image's dense deformation fields from the discrete displacement fields computed by multi-resolution block matching. First, a new joint saliency map (JSM) is proposed to match a structure-tensor-based local saliency distribution for each overlapping pixel pair and highlight the corresponding saliency structures (called joint saliency structures, JSSs) between the images. To explore the consistency of normal JSSs and their deformations around the outliers, we use JSM to guide the dense deformation reconstruction by emphasizing the JSSs’ discrete displacement vectors in kernel regression. The JSS adaptive kernel regression adapts anisotropic kernel's shape and orientation to reference image's structure and weights more contribution from JSS's displacement vectors for the iterative regression, whereby moving image's local deformations can be compliant with reference image's corresponding normal structures. The experimental results demonstrate that the proposed method achieves almost the best performance in structure matching of all challenging image pairs with outlier structures compared with other state-of-the-art intensity-based nonrigid registration algorithms.

Intra-Prediction and Generalized Graph Fourier Transform for Image Coding

Intra-prediction is employed in block-based image coding to reduce energy in the prediction residual before transform coding. Conventional intra-prediction schemes copy directly from known pixels across block boundaries as prediction. In this letter, we first cluster differences between neighboring pixel pairs. Then, for each pixel pair, we add the cluster mean to the known pixel for prediction of the neighboring unknown pixel. The cluster indices are transmitted per block, allowing the decoder to mimic the same intra-prediction. We then propose an optimized transform for the prediction residual, based on a generalized version of previously developed Graph Fourier Transform (GFT). Experimental results show that our generalized intra-prediction plus transform coding outperforms combinations of previous intra-prediction and ADST coding by 2.5 dB in PSNR on average.

CMOS Image Sensor Based Physical Unclonable Function for Coherent Sensor-Level Authentication

In the applications of biometric authentication and video surveillance, the image sensor is expected to provide certain degree of trust and resiliency. This paper presents a new low-cost CMOS image sensor based physical unclonable function (PUF) targeting a variety of security, privacy and trusted protocols that involves image sensor as a trusted entity. The proposed PUF exploits the intrinsic imperfection during the image sensor manufacturing process to generate unique and reliable digital signatures. The proposed differential readout stabilizes the response bits extracted from the random fixed pattern noises of selected pixel pairs determined by the applied challenge against supply voltage and temperature variations. The threshold of difference can be tightened to winnow out more unstable response bits from the challenge-response space offered by modern image sensors to enhance the reliability under harsher operating conditions and loosened to improve its resiliency against masquerade attacks in routine operating environment. The proposed design can be classified as a weak PUF which is resilient to modeling attacks, with direct access to its challenge-response pair restricted by the linear feedback shift register. Our experiments on the reset voltages extracted from a 64 $\times$ 64 image sensor fabricated in 180 nm 3.3 V CMOS technology demonstrated that robust and reliable challenge-response pairs can be generated with a uniqueness of 49.37% and a reliability of 99.80% under temperature variations of $15\sim 115~^{\circ}{\rm C}$ and supply voltage variations of $3\sim 3.6 {\rm V}$ .

A Low-Power Edge Detection Image Sensor Based on Parallel Digital Pulse Computation

An all-digital low-power CMOS edge detection image sensor array is presented. Each pixel contains a voltage-controlled ring oscillator to achieve low-power and cost-efficient digital-only edge detection. While conventional edge detection methods require high computing power and large chip area to process intensity maps, this work implements an all-digital parallel processing algorithm that detects differences between neighboring pixel pairs on chip, hence reducing the aforementioned power and cost overheads. In particular, a simple column-shared frequency comparator enables low-power operation by eliminating arithmetic computations with large memory requirement. Such a simple edge detection algorithm allows the processor area to be less than 16% of the entire image sensor, therefore maximizing the proportion of active optical area. The prototype image sensor presented in this work is fabricated using a four-metal 180-nm CMOS image sensor process and contains 105 $\times$ 92 pixels. An individual pixel size is $8\times \mu\mbox{m}^{2}$ with a fill factor of 11.69%, while the total chip area is $1\times 1.3 \mbox{mm}^{2}$ . The image sensor exhibits a frame rate of 30 frames/s and a power consumption of 8 mW, which is 27.7 nW/pixel/frame at $V_{DD}$ of 1.6 V.

Extended Adaptive Pixel Pair Matching for data hiding in medical images

Data embedding in medical images require perfect reversibility with which the embedding distortions can be completely removed to achieve a high fidelity recovery of the cover images. Adaptive Pixel Pair Matching is a high capacity embedding scheme that allows data embedding in any desired larger no tational system still achieving exact reconstruction of secret. The principle behind Extended Adaptive Pixel Pair Matching (EAPPM) is to use a pixel pair as the reference co-ordinate and select a co-ordinate in its neighbourhood set according to the secret digit, as the stego pixel pair using which the extraction function recovers the secret data. The cover image is then reconstructed using the Look-Up Table (LUT) built with values of modulus distance. This reversibility can lead to multiple layer embedding, but cannot be fully exploited to increase the payloads as the usage of larger notational systems reduces the quality of the image. Hence this paper extends the APPM process to be performed over coefficients of the Transform domain in a repetitive fashion improving embedding capacity still maintaining high image quality as required for medical images.

A reversible data hiding scheme based on the Sudoku technique

Data hiding, also known as information hiding, plays an important role in information security for various purposes. Reversible data hiding is a technique that allows distortion-free recovery of both the cover image and the secret information. In this paper, we propose a new, reversible data hiding scheme that is based on the Sudoku technique and can achieve higher embedding capacity. The proposed scheme allows embedding more secret bits into a pair of pixels while guaranteeing the good quality of the stego-image. The experimental results showed that the proposed scheme obtained higher embedding capacity than some other previous schemes. In addition, our proposed scheme maintained the good visual quality of the stego-image (i.e., PSNR>46dB), which outperforms some existing schemes.

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

In this paper, we present an approach for Arabic and Latin script and its type identification based on Histogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writing orientation analysis. Then, they are extended to word image partitions to capture fine and discriminative details. Pyramid HOG are also used to study their effects on different observation levels of the image. Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs of pixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potential informative features combinations which maximizes the classification accuracy. The output is a relatively short descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set of words, extracted from standard databases, show that our identification system is robust and provides good word script and type identification: 99.07% of words are correctly classified.

Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.

Semisupervised Hyperspectral Image Classification via Neighborhood Graph Learning

In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.

A ROBUST MATCHING METHOD FOR UNMMANED AERIAL VEHICLE IMAGES WITH DIFFERENT VIEWPOINT ANGLES BASED ON REGIONAL COHERENCY

Abstract. One of the main challenges confronting high-resolution remote sensing image matching is how to address the issue of geometric deformation between images, especially when the images are obtained from different viewpoints. In this paper, a robust matching method for Unmanned Aerial Vehicle images of different viewpoint angles based on regional coherency is proposed. The literature on the geometric transform analysis reveals that if transformations between different pixel pairs are different, they can't be expressed by a uniform affine transform. While for the same real scene, if the instantaneous field of view or the target depth changes is small, transformation between pixels in the whole image can be approximated by an affine transform. On the basis of this analysis, a region coherency matching method for Unmanned Aerial Vehicle images is proposed. In the proposed method, the simplified mapping from image view change to scale change and rotation change has been derived. Through this processing, the matching between view change images can be converted into the matching between rotation and scale changed images. In the method, firstly local image regions are detected and view changes between these local regions are mapped to rotation and scale change by performing local region simulation. And then, point feature detection and matching are implemented in the simulated image regions. Finally, a group of Unmanned Aerial Vehicle images are adopted to verify the performance of proposed matching method respectively, and a comparative analysis with other methods demonstrates the effectiveness of the proposed method.

A data-driven approach to quality assessment for hyperspectral systems

The increasing use of products based on airborne hyperspectral data for decision-making calls for a thorough quality assessment. Due to the complexity of the corresponding processing chain, as well as the variety of physical processes involved, such a task is usually only performed in specific cases and on specific parts of the processing chain. In particular, the quality assessment of data-products is still an open issue. A generic quality assessment method – based on an cross-comparison of errors – is proposed in this paper.Airborne hyperspectral – also called imaging spectroscopy – data is commonly acquired by means of whisk- or push-broom sensors, and requires several strips – or flight-lines – to cover the full area of interest. A comparison of the discrepancies between overlapping parts of these flight-lines is used to retrieve an assessment of the measurement reproducibility. This mapping can be performed on pre-processed data which avoids the need to separately investigate all input parameters and their associated models, hence bypassing the ‘curse of dimensionality’.The first step involves retrieving the pairs of pixels corresponding to the same areas imaged from overlapping flight-lines. Even when an ortho-rectification of the data has been carried out, various phenomena, such as errors in the underlying digital elevation model, lead to flight-line mis-registrations. For heterogeneous land-covers, a pixel to pixel registration step has therefore to be performed in order to allow a cross-comparison of pixels: a suitable methodology is proposed along with its validation. The second step corresponds to the relative errors analysis itself. A set of quantitative quality indicators – corresponding to different types of land-products – is presented. These methods are illustrated with an example along with a discussion.This approach can be used on any reasonably well contrasted scene to retrieve a quality assessment for any raster product independently of its data type as well as for the reflectance data itself.

A Novel Data Hiding Approach by Pixel-Value-Difference Steganography and Optimal Adjustment to Secure E-Governance Documents

In this paper, we have proposed a new data hiding approach using Pixel Value Difference (PVD) seganography for digital image. PVD steganography, proposed by Wu and Tsai, use non-overlapping block of two pixels to find the edge areas to hide secret message by adjusting the pixel pairs. In PVD method only the edge between two pixels within a block is detected. In order to find out more edges and to increase the capacity of PVD method we have used non-overlapping blocks of three pixels and an optimal adjustment process. The proposed method compared with different PVD approaches with respect to capacity and PSNR. This novel method could be applied to hide E-Governance related data for secure transfer.Keywords: E-Governance, Optimal-Adjustment, Pixel-Value-Difference, Steganography

On adaptive distortion control in reversible watermarking using modified reversible contrast mapping

The use of reversible contrast mapping (RCM) on pair of pixels in images for reversible watermarking (RW) offers a flexible integer transform that leads to high embedding rate at low mathematical complexity and without requiring additional data compression. Mathematically RCM may be interpreted as a form of adaptive linear transformation on pair of pixels that controls distortion and leads to the retention of the structural information of the watermarked image. A generalized form of RCM, analogous to M-ary modulation in communication, is developed here for a set of points and their corresponding embedding spaces are shown geometrically with and without considering distortion control. Then an optimized distortion control framework adaptive to the choice of operating points is considered to improve data hiding capacity under embedding distortion constraint. Simulation results show that the combination of different M-ary approaches i.e. using the points representing the different transformation functions outperform the embedding rate, visual quality and security of the hidden information compared to the existing RCM, difference expansion (DE) and prediction error expansion (PEE) methods during over embedding. Numerical results show that an average of 13 % improvement in visual quality, 25 % improvement in security of the hidden data is achieved at 0.8 bpp embedding rate over existing PEE work. Performance in robustness against common signal processing operations, namely noise addition, smoothing filtering and some form of geometric operation like random bending attack are also studied. All these studies and the effectiveness are demonstrated with a large set of simulation results.

Characterizing Asphalt Pavement 3-D Macrotexture Using Features of Co-occurrence Matrix

This paper presents a method to characterize asphalt pavement macrotexture using the gray level co-occurrence matrix (GLCM). Data collected at 37 field sites are included in the analysis, representing 6 types of asphalt surface layers such as dense asphalt concrete (DAC), stone matrix asphalt (SMA), rubber asphalt concrete (RAC), ultra-thin wearing course (UTWC), micro-surfacing (MS), and open graded friction course (OGFC). This paper documents the investigation into the differences of GLCM indicators under various pixel pair spatial relationships. Then, the average of each GLCM indicator in some pixel pair spatial relationships is selected for mean texture depth (MTD) correlation. The correlation analysis shows there are 2 GLCM indicators, f_8 and f_9, have strong relationship with MTD, which are entropy of the gray level sum distribution and the gray level combination distribution of pixel pairs of pavement macrotexture respectively. The larger the values of f_8 and f_9, the more complex of the pavement macrotexture. The correlation coefficients between MTD and f_8, f_9 are 0.9601 and 0.9493 respectively. The exponential models are better choice for connecting f_8 and f_9 with MTD, which are highly significant. The mean square errors (MSE) of the exponential models with f_8 and f_9 are 0.00343 and 0.00351 respectively.

Quantitative analysis of ultrasonic images of fibrotic liver using co-occurrence matrix based on multi-Rayleigh model

In medical ultrasonic images of liver disease, a texture with a speckle pattern indicates a microscopic structure such as nodules surrounded by fibrous tissues in hepatitis or cirrhosis. We have been applying texture analysis based on a co-occurrence matrix to ultrasonic images of fibrotic liver for quantitative tissue characterization. A co-occurrence matrix consists of the probability distribution of brightness of pixel pairs specified with spatial parameters and gives new information on liver disease. Ultrasonic images of different types of fibrotic liver were simulated and the texture-feature contrast was calculated to quantify the co-occurrence matrices generated from the images. The results show that the contrast converges with a value that can be theoretically estimated using a multi-Rayleigh model of echo signal amplitude distribution. We also found that the contrast value increases as liver fibrosis progresses and fluctuates depending on the size of fibrotic structure.