Pixel Position Research Articles

Camera calibration method through multivariate quadratic regression for depth estimation on a stereo vision system

Machine vision systems have demonstrated practical applications in various fields where the perception of the environment is essential. Stereo vision systems are one of the most used methods to perform three-dimensional mapping. These systems have several considerations in order to fulfill this task; One of them is camera calibration. This is why this paper proposes a novel camera calibration method for improving depth estimation accuracy in stereo vision systems. The method addresses the lens distortion issue by adjusting the surface point's pixel coordinates before the triangulation process. The new pixel coordinates are computed using calibration coefficients, which are calculated through multivariate quadratic regression. Also, the proposed method corrects the relative orientation of the cameras and computes a compensation angle. The proposed method is remarkably robust to varying illumination levels because it adjusts pixel positions independently of image content. In comparison to traditional methods, this method requires fewer steps to implement, as it only requires one image per camera. The proposed method could have significant implications for fields such as autonomous navigation and robotics, where depth estimation is a crucial component. Overall, this paper presents a valuable contribution to the field of computer vision by offering a new, efficient and effective approach to camera calibration for stereo vision systems. The performed experiments demonstrated that the proposed method leads to improved depth estimation in the stereo vision system with an improvement of 34.15% on the MAE and 48.38% on the STD when compared to one of the most commonly used methods.

Method for a deflection-type refractometer measuring the fringe shift by using linear Fresnelzone plates.

This study describes the design and performance of a deflection-type refractometer based on measuring the fringe shift from the Fresnel diffraction pattern to solve some major limitations of conventional differential refractometers, such as measurement range, resolution, zero balancing, and monitoring analysis. The refractometer apparatus comprises a coherent light source, linear Fresnel zone plate, measuring cell, and image capture device mounted on a movable platform. The distance measurement unit is configured to detect fringe deflection due to the difference in refractive index between the sample and the reference. To achieve this, distance measurements with an accuracy of a few nanometers by using the local frequency method and fringe shift measurement method are quite feasible. The uncertainty in this technique is determined by the smallest change in the longitudinal displacement of the image for which the CCD camera can detect a change in pixel position. The refractive index is obtained with a highly extended measurement range of at least ±0.4R I U and precision of the order of 2×10-4 R I U. A numerical comparison between computer simulation of the diffraction patterns that occur when the linear Fresnel zone plate is illuminated by a plane light traveling parallel to the z axis.

Multiservice Compact Pixelated Stacked Antenna With Different Pixel Shapes for IoT Applications

This paper presents a multi-service pixelated stacked antenna for dual-band application at 5.2 GHz and 5.8 GHz bands. An efficient method for designing multilayer or stacked antennas for multistandard Internet of Things (IoT) applications is proposed in this paper. The proposed antenna topology is based on two different pixel shapes and offers essential aspects such as flexibilities in the design of low-profile single band or multi-band antennas for wireless systems. The antenna design consists of two pixelated layers of radiating patch, consisting of different shaped pixels. The V-shaped binary particle swarm optimization (VBPSO) algorithm has been implemented for the optimization of pixel positions. Only by considering predefined regions, the antenna can be designed and optimized for various design goals. In the design procedure, triangular-shaped pixels are used to form the principal radiating patch, while square-shaped pixels are implemented on the stacked parasitic patch. Measurement and simulation results are in good agreement which proves the accuracy of the design and simulation procedure. The antenna operates at 5.16-5.21 GHz and 5.79-5.86 GHz. With a compact design, the proposed antenna achieves 4.7 dBi and 4.4 dBi gain at 5.2 GHz and 5.8 GHz, respectively. The proposed antenna design methodology offers great potentials to be aligned with specific design requirements of wireless low-profile IoT devices.

Deep Learning-Based Enhanced ISAR-RID Imaging Method

Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.

Blast response and damage assessment of reinforced concrete slabs using convolutional neural networks

Concrete structures are essential for shelters, storage, transportation, and defense systems. However, they are vulnerable to terrorist attacks and explosions. The most exposed component of these structures is the reinforced concrete slab, which is also the primary force-transferring member. Therefore, the present study utilizes machine learning techniques to predict the maximum vertical displacement of reinforced concrete slabs subjected to air-blast loading. This can be achieved using 11 input parameters of the slab and TNT blast to predict the maximum displacement. The dataset comprises 146 samples from various experimental and numerical blast studies on reinforced concrete slabs in the open literature. Rather than presenting the data in a tabular format, each individual data sample is transformed into an image using distinct techniques: one uses a self-similarity matrix, and the other utilizes an image generator for the tabular data. Image generation transforms tabular data into images by assigning features to pixel positions. This results in spatial dependency of the input features. Using these images, various convolutional neural networks were adopted (ResNet-18, ResNet-50, ResNet-101, EfficentNet-b0, ShuffleNet, Xception, DarkNet-53, and DenseNet-20) and trained to predict the slab maximum displacement. Most models demonstrated promising results. The performance of the models was predicted based on the root mean squared error, mean absolute error, and coefficient of determination, and the impact of input features on the maximum displacement was examined. Along with this, the initial study of the blast damage assessment on reinforced concrete slabs is explained for future work to be performed based on the proposed method.

Image Encryption Techniques Using Dynamic Approach : An Article Review

In this study, a chaotic method is proposed that generates S-boxes similar to AES S-boxes with the help of a private key belonging to In this study, dynamic encryption techniques are explored as an image cipher method to generate S-boxes similar to AES S-boxes with the help of a private key belonging to the user and enable images to be encrypted or decrypted using S-boxes. This study consists of two stages: the dynamic generation of the S-box method and the encryption-decryption method. S-boxes should have a non-linear structure, and for this reason, K/DSA (Knutt Durstenfeld Shuffle Algorithm), which is one of the pseudo-random techniques, is used to generate S-boxes dynamically. The biggest advantage of this approach is the production of the inverted S-box with the S-box. Compared to the methods in the literature, the need to store the S-box is eliminated. Also, the fabrication of the S-box has a very large key space as it depends on the user's key. The encryption-decryption method allows changing pixel positions with the help of dynamically generated S-boxes, images, videos, etc. Thus, the study shows that a new method of S-boxes for dynamic cipher algorithms can be easily generated and applied to image encryption.

FusionCalib: Automatic extrinsic parameters calibration based on road plane reconstruction for roadside integrated radar camera fusion sensors

Calibrating the relative positional posture of the roadside sensor with respect to the road is crucial for target tracking and sensor fusion. However, most previous studies rely on prior information or require additional manual measurements, which increases calibration error and labor costs. This paper proposes an automatic extrinsic parameters calibration method for roadside integrated radar camera fusion sensors for the first time. In particular, we leverage the radar camera fusion framework to automatically reconstruct the road plane. Specifically, to integrate sensing abilities, a stable bidirectional selection association method is adopted to match radar and camera trajectories. Based on the association results, the Extended Kalman Filter (EKF) is adopted to fuse pixel positions and three-dimensional (3D) velocities of vehicles, and then a series of 3D positions of vehicle bottoms are obtained to reconstruct the road plane. Experimental results show that our proposed method achieves automatic and accurate calibration of extrinsic parameters.

The main color structure setting of Chinese oil painting based on content analysis method

Abstract Based on the content analysis method, this paper first uses the PCA technique to disassemble and transform the base color information to extract multiple feature values. Secondly, the sparse autoencoder is used to adjust the feature parameters by training, and the activation function pair is used to calculate the confidence to complete the effective classification. Finally, the SVM algorithm is introduced to optimize the color information parameters, and the corresponding color label values are calculated by constructing a decision function to accurately set the pixel positions. The results show that the color difference value of Chinese oil painting image keeps between 0.5nbs -1.5nbs, the standard deviation value of saturation is 165.55, and the resolution is 610ppi, which indicates that the content-based analysis method can better coordinate and integrate the relationship between various color structures.

Image encryption algorithm based on reversible information hiding and physical chaos in images

This article proposes a reversible information hiding algorithm based on chaos and histogram translation in ciphertext images to address the shortcomings of traditional algorithms such as separation of encryption and information hiding processes, small embedding capacity, and inflexible operation. This article first uses grayscale images as a carrier for digital images, analyzes and studies existing reversible information hiding algorithms in the ciphertext domain of grayscale images, and improves them to solve the problems of existing reversible information hiding algorithms in the ciphertext domain. Secondly, the sender first encrypts the image and then embeds the information; The receiving end can either decrypt and extract information first, or extract and decrypt information first. Finally, we selected a Lena grayscale image with 513 pixels × 513 pixels for the experiment, dividing the image into 4 blocks (r = 4), and scrambling the pixel positions using DLKL transform for each block, with parameters k = 4, n = 7, q = 59. The simulation results show that the algorithm is easy to implement and can effectively improve the capacity of information embedding.

A 4D discrete Hopfield neural network-based image encryption scheme with multiple diffusion modes

A novel 4D chaotic map is proposed in this paper using the model of a self-feedback Discrete Hopfield Neural Network (DHNN) with nonlinear synaptic ICMIC (4D-IDHNN). Based on an analysis of chaos characteristics, including attractors, Lyapunov exponents, Bifurcation, Sample Entropy and CO algorithms, the 4D-IDHNN system demonstrates reasonable stochasticity and ergodicity, alongside a broad spectrum of hyperchaotic parameters. Based on the 4D-IDHNN system, we design a new Pixel-dependent bit-plane Chaotic random Diffusion (PbCrD) algorithm with high diffusion capability and security. To begin, the 4D-IDHNN system generates pseudo-random sequences to confuse the plaintext image pixel positions with Chaotic Magic Random Scramble (CMRS). Subsequently, the PbCrD algorithm can be employed to diffuse pixel values. With the initial value of 4D-IDHNN generated using the SHA-256 hash of plaintext image, the ability to resist plaintext attacks can be enhanced. The algorithm includes 24 diffusion modes, which is three times greater than the number of DNA rules, thereby enhancing the complexity of decryption. The proposed encryption algorithm has good effectiveness, robustness, and resistance to differential attacks based on simulations and security analyses.

The general method of the tanker car mouth pose measurement

PurposeThe coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.Design/methodology/approachThe collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.FindingsExperimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.Originality/valueA method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.

Reliable and Secure Data Transfer in IoT Networks using Knight-Tour and PHLSB Method

Data security has recently become the most pressing problem for corporate owners and even regular people due to the continual development in the speed of data transmission over the entire Internet, and stricter regulations have been made for data protection. Secure key management is now essential for securing information exchange due to the Internet of Things (IoT) and the rapidly advancing mobile device technologies. Smart home and healthcare IoT apps, for instance, offer automated services to users with little user involvement. Data transmission delays and intrusions can affect existing security solutions that use a single link. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The methods Knight-Tour and polynomial-based hash least significant bit (PHLSB) that take use of flaws in the human visual system (HVS) are combined to form a hybrid approach to image steganography in this research. The Knight's trip path is a chess board layout that follows the route of a horse without stopping at any of the nodes again. In order to create the scrambled image, this travel path pattern is employed to permute the original image's pixel positions. Prior to being masked behind a cover image, this technique makes sure the communication has been encrypted. A strong mathematical tool is a polynomial equation. The cover object's data was hidden using these equations as a secret key. The suggested solution efficiently secures IoT devices by assigning the resource-intensive encryption processing to local organisations. As a result of the proposed framework's simulation findings, network lifetime PDR (Packet Drop Ratio) has improved, throughput has increased, energy consumption has decreased, and latency has decreased.

An Efficient Audio Encryption Scheme Based on Elliptic Curve over Finite Fields

Elliptic curve (EC) based cryptographic systems are more trustworthy than the currently used cryptographic approaches since they require less computational work while providing good security. This paper shows how to use an EC to make a good cryptosystem for encrypting digital audio. As a preliminary step, the system uses an EC of a particular type over a binary extension field to distort the digital audio pixel position. It reduces the inter-correlation between pixels in the original audio, making the system resistant to statistical attacks. In creating confusion in the data, an EC over a binary extension field is used to make a different number of substitution boxes (S-boxes). The suggested design employs a unique curve that relies on efficient EC arithmetic operations in the diffusion module. As a result, it generates high-quality pseudo-random numbers (PRNs) and achieves optimal diffusion in encrypted audio files with less processing work. Audio files of various sizes and kinds can all be encrypted using the provided algorithm. Moreover, the results show that this method effectively protects many kinds of audio recordings and is more resistant to statistical and differential attacks.

Global erosion of terrestrial environmental space by artificial light at night

Artificial light at night (ALAN) disrupts natural light cycles, with biological impacts that span from behaviour of individual organisms to ecosystem functions, and across bacteria, fungi, plants and animals. Global consequences have almost invariably been inferred from the geographic distribution of ALAN. How ALAN is distributed in environmental space, and the extent to which combinations of environmental conditions with natural light cycles have been lost, is also key. Globally (between 60°N and 56°S), we ordinated four bioclimatic variables at 1.61 * 1.21 km resolution to map the position and density of terrestrial pixels within nighttime environmental space. We then used the Black Marble Nighttime Lights product to determine where direct ALAN emissions were present in environmental space in 2012 and how these had expanded in environmental space by 2022. Finally, we used the World Atlas of Artificial Sky Brightness to determine the proportion of environmental space that is unaffected by ALAN across its spatial distribution. We found that by 2012 direct ALAN emissions occurred across 71.9 % of possible nighttime terrestrial environmental conditions, with temperate nighttime environments and highly modified habitats disproportionately impacted. From 2012 to 2022 direct ALAN emissions primarily grew within 34.4 % of environmental space where it was already present, with this growth concentrated in tropical environments. Additionally considering skyglow, just 13.2 % of environmental space now only experiences natural light cycles throughout its distribution. With opportunities to maintain much of environmental space under such cycles fast disappearing, the removal, reduction and amelioration of ALAN from areas of environmental space in which it is already widespread is critical.

Design of a new multi-wing chaotic system and its application in color image encryption

Due to its chaotic carrier function and pseudorandom nature, chaotic systems are frequently applied in the area of secure communication. To render chaotic signals more complicated, a novel multi-wing chaotic system is developed in this study. Initially, a double-wing attractor generated by a novel 3D Lorenz-like chaotic system that comprises two quadratic terms and four linear terms. By inserting a nonlinear feedback controller, the complex system gets transformed into a multi-wing chaotic system. It is noteworthy that the topological shape of the attractor and the number of equilibrium points can both be altered in the particular multi-wing chaotic system. Secondly, the dynamic features of the multi-wing chaotic system are discussed via employing the bifurcation diagram and the Lyapunov exponential spectrum. According to results from simulation, the system displays widely dispersed chaotic characteristics over a large range of parameters. In addition, the complexity analysis findings of chaotic sequences testify that the chaotic sequences have strong complexity. Therefore, the multi-wing chaotic system will have an excellent effect in image encryption. Finally, a scrambling-diffusion encryption scheme is built using the rich chaotic sequences discussed above. The scheme includes two times scrambling of pixel position and once transform of pixel value by the virtue of Galois field (GF). The proposed algorithm has superior efficiency and excellent security according to the results of the feasibility and security analysis. Moreover, the investigation of the multi-wing chaotic system property and its usage in image encryption additionally broadens the applicability of chaos systems and offers some standards for confidential communication.

A hybrid-qudit representation of digital RGB images

Quantum image processing is an emerging topic in the field of quantum information and technology. In this paper, we propose a new quantum image representation of RGB images with deterministic image retrieval, which is an improvement over all the similar existing representations in terms of using minimum resource. We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities. Additionally, we generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image. This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems than qubits. We then describe the image encoding method using higher-order qubit-qutrit gates, and demonstrate the decomposition of these gates in terms of simpler elementary gates. We use the Google Cirq’s quantum simulator to verify the image preparation in both the ideal noise-free scenario and in presence of realistic noise modelling. We show that the complexity of the image encoding process is linear in the number of pixels. Lastly, we discuss the image compression and some basic RGB image processing protocols using our representation.

Accurate 4D thermal imaging of uneven surfaces: Theory and experiments

Conventional IR thermography is basically a 3D imaging model: pixel position (2D) and temperature (1D). However, the surface roughness, which cannot be depicted by the 2D geometry imaging, may introduce significant error into the temperature measurement, especially in close range. Although there are some principle studies on this theme, quantitative tools for arbitrary-shaped surfaces is still lacking. In this work, we have developed an imaging system composed of a binocular camera using structured illumination and an IR camera to reconstruct 4D uneven surface thermal images. This 4D information includes the 3D geometry of the surface and the temperature mapping over it. Based on the highly accurate surface roughness image obtained, a meshed area calculation tool is used to establish a temperature self-correction module for rough/uneven surfaces, which include convex surface, concave surface or their combinations. The effective emissivity of convex as well as flat surface is easy to calculate, because is this case, there is no inter-radiation or reflection. On contrast, the determination of concave surface emissivity is still posed as a challenge. In this work, some theoretic gap was first filled and then the experimental verification was provided. The temperature-correction takes angle of view and emissivity calibration into account. The angle of view is relative to the position of the object point and the emissivity calibration is based on the 3D geometry of the uneven surfaces. An imaginary flat surface is used to provide an effective emissivity for a local roughness. Experimental results shows that without this temperatue-correction module, the deviations between IR thermography and thermocouple are from ∼2.35 °C (at 50 °C) to ∼4.96 °C (at 300 °C) and from ∼2.43 °C (at 50 °C) to ∼4.78 °C (at 300 °C), while after the correction, the deviations are from ∼0.12 °C (at 50 °C) to ∼0.20 °C (at 300 °C) and from ∼0.16 °C (at 50 °C) to ∼0.20 °C (at 300 °C). Utilizing the thermal imaging model for recorrection, the deviations are from ∼0.13 °C (at 50 °C) to ∼0.19 °C (at 300 °C) and from ∼0.16 °C (at 50 °C) to ∼0.19 °C (at 300 °C). This generalized solution paves the way for high-accuracy temperature measurement on complexly rough surface, especially for high temperature, at which the measuring deviation can be very large.

Quantum Image Encryption Algorithm Incorporating Bit-plane Color Representation and Real Ket Model

Image is one of the most important carriers of information that humans transmit on a daily basis. Therefore, the security of images in the transmission process has been a key study subject. A quantum bit-plane representation of the Real Ket model (QBRK) is proposed, which requires 2n+4 and 2n+6 quantum bits to represent gray-scale and color images of 22n−k×2k size, respectively. On the basis of the QBRK model and chaotic system, an image encryption algorithm is proposed according to pixel position encoding for slice dislocation and quantum bit-plane XOR operation. First, we use a modified logistics chaos system to generate two matrices that perform matrix determinant transformations in the bit-plane. Then, we perform an XOR operation on the pixel values based on the parity bit-plane. Finally, the pixel diffusion is completed by permutation with each cut encoding in the QBRK model. According to the simulation outcomes and security analysis, the encryption algorithm is very efficient and well resists state-of-the-art attacks.

An Image Encryption Algorithm Based on Improved Hilbert Curve Scrambling and Dynamic DNA Coding.

As an effective method for image security protection, image encryption is widely used in data hiding and content protection. This paper proposes an image encryption algorithm based on an improved Hilbert curve with DNA coding. Firstly, the discrete wavelet transform (DWT) decomposes the plaintext image by three-level DWT to obtain the high-frequency and low-frequency components. Secondly, different modes of the Hilbert curve are selected to scramble the high-frequency and low-frequency components. Then, the high-frequency and low-frequency components are reconstructed separately using the inverse discrete wavelet transform (IDWT). Then, the bit matrix of the image pixels is scrambled, changing the pixel value while changing the pixel position and weakening the strong correlation between adjacent pixels to a more significant correlation. Finally, combining dynamic DNA coding and ciphertext feedback to diffuse the pixel values improves the encryption effect. The encryption algorithm performs the scrambling and diffusion in alternating transformations of space, frequency, and spatial domains, breaking the limitations of conventional scrambling. The experimental simulation results and security analysis show that the encryption algorithm can effectively resist statistical attacks and differential attacks with good security and robustness.

The extended digital image correlation based on intensity change model

Digital image correlation is used to measure deformation and shape. In some complex scenes, the illumination of the measured surface may vary unevenly before and after deformation. The intensity change model is added to correlation function and a family of speckle image matching algorithms is obtained. In integer pixel matching, linear least square estimation is used to calculate the initial values of the intensity coefficients and integer pixel position; in subpixel matching, cyclic variable method is proposed to make the iteration converge. Compared with the current mainstream algorithm, the proposed method makes the physical meaning of correlation matching clearer, so that the correlation function can be defined according to the actual lighting environment, and correlation matching can be more accurate. The experimental results indicate that the proposed matching algorithm is suitable for applications where ambient light is constantly changing.