Pixel Difference Research Articles

Cotton flow velocity measurement based on image cross-correlation and Kalman filtering algorithm for foreign fibre elimination

Velocity estimation of cotton flow is usually used to improve the accuracy of foreign fibre elimination machines. A hybrid approach based on image cross-correlation and Kalman filter is presented in this paper. Two linear CCD cameras at different locations were used to capture the images of the cotton flow synchronously. Afterwards, the captured images were preprocessed and segmented by connected region method to obtain separated cotton images. Subsequently, image cross-correlation registration technology was used to calculate the coordinate transformation parameters. For image registration, the Kalman filter tracking method was utilized to predict the possible location to speed up the image registration and solve the problem when the image registration fails. Lastly, the velocity was calculated by the pixel difference and the actual distance of two cameras. The experiment setup was designed to validate the proposed method. The hybrid approach based on image cross-correlation and Kalman filter algorithm measure the dynamic cotton flow velocity with the average 3.31% error. Moreover, the executing efficiency is prior to regular cross-correlation without Kalman filter prediction.

Facial expression recognition with fused handcraft features based on pixel difference local directional number pattern

Facial expression recognition (FER) has been an active research area in recent years, which plays a vital role in national security and human-computer interaction. Due to the lacking of sufficient expression features and facial images, it is challenging to automatically recognize facial expression with high accuracy. In this paper, we propose a fusion handcraft feature method to improve FER from images. Firstly, a new texture feature extraction method PD-LDN (Pixel Difference Local Directional Number pattern) is proposed, which can extract more local information, reduce noise disturbance and feature dimension. Secondly, the handcrafted features including PD-LDN texture features, geometric features, and BOVW (Bag of Visual Words) semantic features are connected in parallel to an improved autoencoder network for fusion. Finally, the fused features are input into the softmax classifier for recognizing facial expression. We conduct extensive experiments on JAFFE and CK+datasets. Our proposed method shows superior performance than the state-of-the-art approaches on recognizing facial expressions.

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.

P‐4.9: A Scheme of Eliminating Color Layer Noise in RGBW

Displays that employ RGBW primaries have demonstrated greater power efficiency than similar displays with only RGB primaries. Unfortunately, RGBW systems with algorithms that use white to augment luminance generally have color layer noise affected the visual effect and need to be solved. Color layer noise visually shows the phenomenon of uneven color transition, showing a banded change. According to the cause of the color layer noise, we propose an algorithm to reduce the brightness difference between adjacent pixels in the location of color layer noise. Firstly, the gradient feature is used to mark the details in RGB space in order to distinguish the color layer from the texture details. Then the color layer is detected in RGBW space and the brightness of the color layer is smoothed to reduce the brightness difference of the adjacent pixels. The experiment on 6.5 inch RGBW TFT‐LCD screen show that the algorithm can effectively remove the color layer, and the details are lossless.

Quantum Watermark Algorithm Based on Maximum Pixel Difference and Tent Map

Quantum Watermark Algorithm Based on Maximum Pixel Difference and Tent Map

Lossless Watermarking Algorithm Based on Multiple-Base Notational System and Pixel Difference

In order to identify the authenticity of the industrial packaging, the solution to the current watermark technology is mainly to embed the same watermark capacity into each pixel value in the image, so that the watermark capacity is small, and the anti-distortion performance is not good. Data watermarking algorithm based on system and pixel difference. First divide the carrier image into non-overlapping sub-blocks, and calculate the pixel difference of each sub-block; introduce a hybrid system to obtain different bases. According to the pixel difference, the base selection rule is defined; considering the human visual characteristics, the pixel interval adaptive adjustment mechanism is designed to improve the coding technology and complete the watermark information embedding; finally, the watermark information extraction mechanism is established to restore the watermark. The experimental results show that compared with the current data watermarking algorithm, the proposed algorithm has ideal imperceptibility, as well as larger watermark capacity and lower distortion.

Effective Strain Image Sequence Selection by Using Semi-Automated Image Processing Technique

One fourth of the cancer detected in women worldwide is breast cancer which leads this as a major threat for women. There are many methods of detecting cancer among which ultra-sound strain imaging is one of the promising techniques. However, in strain sequence, not all the frames show clear tumor visibility. Consequently, in this paper we tested some well-defined algorithms to find only those frames where the tumor is comparatively clearly visible. We have used Mean Pixel Difference (MPD) and Gray- Level Co-occurrence Matrix (GLCM) to find the frames with better tumor visibility. We have tested our methods in several real-life cases and the results have been examined by a professional doctor. The MPD has an accuracy of 96.2% and the GLCM. Contrast has that of 55.55%.
 GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 8-13

Reduction of pixel deviation effect in three-dimensional light-field display based on the fitting algorithm with dense-viewpoints

In three-dimensional (3D) light-field displays, the quality of a reconstructed image degrades with increasing depth of field due to the pixel deviation effect. A fitting algorithm of dense-viewpoints is proposed to reduce the pixel deviation with high image quality. To analyze the pixel deviation effect, the human vision-based perspectives perceived from 3D light-field display system with the holographic functional screen are mathematically modeled. The optimized synthetic image is coded according to the mapping relationship from the display model and the optimal Gaussian distribution parameters achieved with the maximum likelihood estimation method. The effectivenesses of the proposed model and the optimization method are verified through simulations as well as optical experiments. A human vision-based high resolution 3D light-field display with 45-degree viewing angle and the large depth of field is achieved.

Steganography based on quotient value differencing and pixel value correlation

Pixel value differencing (PVD) steganography techniques produce imperceptible stego-images. Most PVD-based techniques have a fall-off boundary problem (FOBP). To avoid FOBP, a steganographic scheme is presented that combines the quotient value difference (QVD) with pixel value correlation (PVC). It performs data embedding in two stages on 3 × 3 size pixel blocks. The first stage of the embedding procedure performs QVD and remainder substitution on five pixels. These five pixels are central pixel and it's neighbours on the left, right, upper, and lower sides. Based on the new values of these five pixels, the PVC embedding procedure is applied on the remaining four corner pixels of the block. The experimental results are found to be improved. The hiding capacity is high that is 3.92 bits per byte with an acceptable peak signal-to-noise ratio value. A security check was performed using two tests: (i) a regular-singular (RS) test, (ii) a pixel difference histogram (PDH) test. The experimental outcomes demonstrate that PDH curves of the stego-images do not have a zigzag shape, that means the PDH test could not detect this steganography technique. Furthermore, the RS curves show that the RS test could not detect this steganography technique.

Background Subtraction of an Indian Classical Dance Videos using Adaptive Temporal Averaging Method

A slew of motion detection methods have been proposed in recent years. The background includes some constraints such as changes in illumination, shadow, cluttered the background, scene change and speed of dance between hand gestures and body gestures are different. One of the most basic methods for background subtraction is temporal averaging. We looked at a new adaptive temporal averaging approach in this paper. To identify moving objects in video sequences, an adaptive temporal averaging technique is used. Depending upon the speed of the technique we proposed a Gaussian distribution technique. Gaussian distribution done background subtraction depending upon active pixels it differentiates whether it is a background or foreground. The background model's update rate has been modified to be adaptive and determined by pixel difference .Our aim is to improve the method's F-measure by making it more adaptable to various scene scenarios. The experiment results are shown and evaluated. The proposed method and the original method's quality parameters are compared

Face anti-spoofing based on weighted neighborhood pixel difference pattern

The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’ information. We propose a method based on quantifying the difference between neighborhood pixels around the center of a local region, namely, weighted neighborhood difference quantization local binary pattern (LBP), against print attacks, and video-replay attacks. The proposed method quantifies the differences between neighborhood pixels without using the center one and uses a linear weighting scheme to improve the discriminant capability of the traditional LBP. The combination of the proposed algorithm and the spatial pyramid further improves the performance of face spoofing detection. We also conducted a lot of experiments in different color spaces to illustrate the role of color in face spoofing detection. The improved method achieves better results in three challenging face anti-spoofing databases, CASIA FASD, Replay-Attack, and Replay-Mobile, respectively.

Quadruple histogram shifting-based reversible information hiding approach for digital images

Nowadays, data hiding techniques are one of the most growing researching areas. This paper introduces a quadruple histogram shifting-based reversible data embedding approach for digital images. Pixel differences distribution is utilized for maximizing the hiding payload capacity while preserving minimum degradations for stego-images. The improved histogram-based reversible data embedding algorithm is designed with the potential of maximizing the hiding payload capacity while keeping a perfect stego-image quality with data lossless. The proposed algorithm utilizes a 3 × 3 -box predictive filter and can hide 2-bits per pixel to increase the payload capacity. Firstly, an embedding phase has been performed by applying a numerical ordering strategy of the pixels to be predicted by the 3-by-3 box filter. The proposed algorithm is performing this ordering strategy in four stages to avoid affecting the follow-up prediction of all other pixels. In the other side, an extracting phase has been performed by reversing the four stages to generate the embedded data. The comparison with state-of-the-art schemes is performed to validate and guarantee the prevalence of the improved histogram-based reversible data embedding approach. The obtained test results demonstrated and confirmed that the proposed improved histogram-based reversible data embedding approach not only can improve the payload capacity but also can save the stego-image quality.

Simulation and assessment of a water pollution accident caused by phenol leakage

Abstract Frequent occurrence of water pollution accidents has caused serious ecological and environmental damage. The purpose of this study was to assess the impacts of water pollution accidents on the human body and the ecosystem and to use a new calculation method of mean absolute deviation (MAD) to measure model error. A water pollution accident caused by the phenol leakage was simulated using the Environmental Damage Model of Water Pollution Accidents. In order to analyze the impacts of the phenol leakage event on the human body and the ecosystem, pollution level and its classification method were defined, and the result showed that the leakage event of phenol could have a destructive impact and present great danger in some areas, which covered 32–56% of the study area. The measurement of the model error indicated that lower resolution meant smaller MAD. Quantity Deviation and Hierarchical Deviation did not change with the resolution, and low resolution meant small Pixel Deviation. MAD not only reflected the deviation between layers but also embodied the impact of individual data on the whole deviation.

Self-Supervised Monocular Depth Learning in Low-Texture Areas

For the task of monocular depth estimation, self-supervised learning supervises training by calculating the pixel difference between the target image and the warped reference image, obtaining results comparable to those with full supervision. However, the problematic pixels in low-texture regions are ignored, since most researchers think that no pixels violate the assumption of camera motion, taking stereo pairs as the input in self-supervised learning, which leads to the optimization problem in these regions. To tackle this problem, we perform photometric loss using the lowest-level feature maps instead and implement first- and second-order smoothing to the depth, ensuring consistent gradients ring optimization. Given the shortcomings of ResNet as the backbone, we propose a new depth estimation network architecture to improve edge location accuracy and obtain clear outline information even in smoothed low-texture boundaries. To acquire more stable and reliable quantitative evaluation results, we introce a virtual data set in the self-supervised task because these have dense depth maps corresponding to pixel by pixel. We achieve performance that exceeds that of the prior methods on both the Eigen Splits of the KITTI and VKITTI2 data sets taking stereo pairs as the input.

HSB-SPAM: An Efficient Image Filtering Detection Technique

Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.

Reference-Free DIBR-Synthesized Video Quality Metric in Spatial and Temporal Domains

Depth image-based rendering (DIBR) techniques play an important role in free viewpoint videos (FVVs), which have a wide range of applications including immersive entertainment, remote monitoring, education, etc. FVVs are usually synthesized by DIBR techniques in a &#x201C;blind&#x201D; environment (without a reference video). Thus, an effective reference-free synthesized video quality assessment (VQA) metric is vital. At present, many image quality assessment (IQA) algorithms for DIBR-synthesized images have been proposed, but limited researches have been concerned about the quality assessment of DIBR-synthesized videos. To this end, this paper proposes a novel reference-free VQA method for synthesized videos, which operates in Spatial and Temporal Domains, dubbed as STD. The design fundamental of the proposed STD metric considers the effects of two major distortions introduced by DIBR techniques on the visual quality of synthesized videos. First, considering the geometric distortion introduced by DIBR technologies can increase high-frequency contents of the synthesized frame, the influence of the geometric distortion on the visual quality of a synthesized video can be effectively evaluated by estimating high-frequency energies of each synthesized frame in spatial domain. Second, temporal inconsistency caused by DIBR techniques brings the temporal flicker distortion, which is one of the most annoying artifacts in DIBR-synthesized videos. In temporal domain, we quantify temporal inconsistency by measuring motion differences between consecutive frames. Specifically, optical flow method is first used to estimate the motion field between adjacent frames. Then, we calculate the structural similarity of adjacent optical flow fields and further adopt the structural similarity value to weight the pixel differences of adjacent optical flow fields. Experiments show that the above two features are able to well perceive the visual quality of DIBR-synthesized videos. Furthermore, since the two features are extracted from spatial and temporal domains, respectively, we integrate them using a linear weighting strategy to obtain our STD metric, which proves advantageous over two components and the competing state-of-the-art I/VQA methods. The source code is available at <uri>https://github.com/wgc-vsfm/DIBR-video-quality-assessment</uri>.

Depth of field analysis for a three-dimensional light-field display based on a lens array and a holographic function screen

Compared with traditional integral imaging systems, light-field displays (LFDs) based on a holographic functional screen (HFS) shows significant advantages in terms of the number of viewpoints, large viewing angle, and high viewing resolution. LFDs usually suffer from a limited depth range, and the reason majorly originates from the pixel deviation effect in the reconstruction process. Here, an analysis method for the depth of field (DOF) based on the pixel deviation effect observed on the HFS plane is presented. Simulation experiments are carried out to show that the percentage of pixel deviation is a more accurate measure to calculate the DOF. The feasibility of this method is verified in optical experiments.

A Study and Analysis of Improved Binary Pattern Technique in Dynamic Images

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.

Chi-square-based steganalysis method against modified pixel-value differencing steganography

Pixel-value differencing (PVD) steganography can embed a large quantity of information and effectively resist regular/singular (RS) detection. Recent steganography techniques that are based on PVD have only focused on improving embedding capacity and image quality. In terms of security, PVD steganography has typically only been tested against well-known RS detection analysis or pixel difference histogram attacks. Modified PVD (MPVD) is an improved method that eliminates certain detectable features of PVD, thereby increasing security. State-of-the-art studies in steganalysis have attempted to detect MPVD steganography by using features of the differences between cover images and stego images, and they have generated models to test suspicious images. However, these methods have low accuracy when the embedding capacity is low. In this study, we propose a chi-square goodness-of-fit-based steganalysis technique that can be applied to MPVD steganography. Experimental results demonstrate that the method effectively detects MPVD steganography even when the embedding ratio is low. Furthermore, the accuracy, precision, and implementation of the method are better than those of existing state-of-the-art techniques in detecting data hidden using MPVD steganography. Therefore, the proposed method contributes to the security analysis of PVD-extended steganography.

Multi-directional block based PVD and modulus function image steganography to avoid FOBP and IEP

Since the inception of pixel value differencing (PVD) image steganography, it has drawn considerable interest among the researchers of this field. However, most of the PVD based techniques suffer from either falling-off boundary problem (FOBP) or incorrect extraction problem (IEP). Therefore, to address these two issues, this paper proposes a multi-directional pixel value differencing and modulus function (MDPVDMF) based technique. During the embedding process, the original image (OI) is partitioned into 2 × 2 size pixel blocks. Then, data embedding is performed by exploiting the horizontal, vertical, and diagonal directions for each block. For a 2 × 2 pixel block, two difference values can be obtained in any of the three directions. Next, using the difference values and the remainders of the pixel pairs, the secret bits are embedded. The experiment has been conducted to compute the performance of the proposed technique with regards to the image quality metrics like peak signal-to-noise ratio (PSNR), embedding capacity (EC), and FOBP. Results show that PSNR is optimal for vertical pairs with 39.17 dB whereas the EC is optimal for the diagonal pairs with 3.10 bits per pixel (BPP). Further, the proposed technique has shown exceptional attack resistance ability to regular & singular (RS) attack, salt & pepper (S&P) noise, pixel difference histogram (PDH) analysis, and subtractive pixel adjacency matrix (SPAM) steganalysis.