Chain Code Research Articles

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

A Magnetic Flux Leakage Detector for Ferromagnetic Pipeline Welds with a Magnetization Direction Perpendicular to the Direction of Travel.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Accounting for modern slavery risk transparency in Nigerian businesses: Institutional context, disclosure and the way forward

This paper examines the state of engagement of Nigerian companies with modern slavery risk and how current accounting and reporting might be better enabled to contribute towards ending modern slavery practices. Crane's (2013) theoretical framework is used to guide understanding of the contexts behind modern slavery risk disclosure in Nigeria, including industry, socioeconomic, regulatory, cultural and geographic factors. Cross-sectional content and thematic analysis are undertaken, as published in the annual reports, sustainability reports and websites of top companies listed on the Nigerian Stock Exchange. In line with Crane (2013), supply chain codes of conduct and collaboration are identified as a possible first-stage intervention moderator between industry context and ending modern slavery in Nigeria. However, a second moderator, affordable micro-credit, is notably absent from disclosures, and a third moderator, private or civil regulators, as a substitute for government, is considered ineffective. Management capability mediators as circuit breakers, reducing accounting opacity, require further research. This is the first paper to present evidence about modern slavery supply chain risk disclosures in Nigeria and to develop possible contextual, moderating and mediating variables aimed at policies and actions to end modern slavery practices in Nigeria.

An Efficient Privacy and Anonymity Setup on Hyperledger Fabric for Blockchain-Enabled Internet of Things (IoT) Devices

The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, we introduce our proposed work for managing IoT devices using Hyperledger Fabric. We integrated our system on the blockchain with a closed-circuit television (CCTV) security camera fixed at a rental property. The CCTV security camera redirects its feed whenever a new renter walks in. We have introduced the web token for authentication from the renter to the owner. Our contributions include an efficient framework architecture using key invalidation scenarios and token authentication, a novel chain code algorithm, and stealth addresses with modified ring signatures. We performed different analyses to show the system’s throughput and latency through stress testing. We have shown the significant advantages of the proposed architectures by comparing similar existing schemes. Our proposed scheme enhances the security of blockchain-enabled IoT devices and mitigates the single point of failure issue in the existing scheme, providing a robust and reliable solution. Our future work includes scaling it up to cater to the needs of the healthcare system.

Feature Extraction Algorithm based Metaheuristic Optimization for Handwritten Character Recognition

Interest in feature extraction for Handwritten Character Recognition (HCR) has been growing due to numerous algorithms aimed at improving classification accuracy. This study introduces a metaheuristic approach utilizing the Honey Badger Algorithm (HBA) for feature extraction in HCR. The Freeman Chain Code (FCC) is employed for data representation. One challenge with using FCC to represent characters is that extraction results vary depending on the starting points, affecting the chain code's route length. To address this issue, a metaheuristic approach using HBA is proposed to identify the shortest route length and minimize computational time for HCR. The performance metrics of the HB-FCC extraction algorithm are route length and computation time. Experiments on the algorithm use chain code representations from the Center of Excellence for Document Analysis and Recognition (CEDAR) dataset, containing 126 uppercase letter characters. According to the results, the proposed HB-FCC method achieves a route length of 1880.28 and requires only 1.07 seconds to process the entre set of character images.

A three-dimensional extension of the slope chain code: analyzing the tortuosity of the flagellar beat of human sperm

In the realm of 3D image processing, accurately representing the geometric nuances of line curves is crucial. Building upon the foundation set by the slope chain code, which adeptly represents intricate two-dimensional curves using an array capturing the exterior angles at each vertex, this study introduces an innovative 3D encoding method tailored for polygonal curves. This 3D encoding employs parallel slope and torsion chains, ensuring invariance to common transformations like translations, rotations, and uniform scaling, while also demonstrating robustness against mirror imaging and variable starting points. A hallmark feature of this method is its ability to compute tortuosity, a descriptor of curve complexity or winding nature. By applying this technique to biomedical engineering, we delved into the flagellar beat patterns of human sperm. These insights underscore the versatility of our 3D encoding across diverse computer vision applications.

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity (τ3D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au _{3D}$$\\end{document}) as a shape descriptor was investigated by characterizing brain structures. The results of the τ3D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au _{3D}$$\\end{document} computation on the central sulcus and the main lobes revealed significant differences between Alzheimer’s disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a p<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<0.05$$\\end{document} for the left central sulcus and the four brain lobes.

Research on Monocular Depth Sensing Method Based on Liquid Zoom Imaging

Monocular stereo vision has excellent application prospects in the field of microrobots. On the basis of the geometric model of bifocal imaging, this paper proposes a monocular depth perception method by liquid zoom imaging. Firstly, the configuration of a monocular liquid vision system for depth measurement is presented, and the working mechanism of the system is analyzed through theoretical derivation. Then, to eliminate the influence of optical axis drift induced by the liquid gravity factor on the measurement results, the target image area is used as the calculation feature instead of the image vector length. A target area calculation method based on chain code classification and strip segmentation is proposed. Furthermore, in response to the fluctuation problem of liquid lens focal power caused by factors such as temperature and object distance, a dynamic focal length model of the liquid zoom imaging system is constructed after precise calibration of the focal power function. Finally, a testing experiment is designed to validate the proposed method. The experimental results show that the average error of depth perception methods is 4.30%, and its measurement time is only on the millisecond scale. Meanwhile, the proposed method has good generalization performance.

Proving a conjecture on prime double square tiles

In 2013, while studying a relevant class of polyominoes that tile the plane by translation, i.e., double square polyominoes, Blondin Massé et al. found that their boundary words, encoded by the Freeman chain coding on a four letters alphabet, have specific interesting properties that involve notions of combinatorics on words such as palindromicity, periodicity and symmetry. Furthermore, they defined a notion of reducibility on double squares using homologous morphisms, so leading to a set of irreducible tile elements called prime double squares. The authors, by inspecting the boundary words of the smallest prime double squares, conjectured the strong property that no runs of two (or more) consecutive equal letters are present there. In this paper, we prove such a conjecture using combinatorics on words’ tools, and setting the path to the definition of a fast generation algorithm and to the possibility of enumerating the elements of this class w.r.t. standard parameters, as perimeter and area.

Application of Digital RMB Smart Contracts in Grid Smart Payment Settlement

Abstract The use of smart contract technology for contract execution and real-time payment can ensure the timely availability of funds, thus ensuring the security and authenticity of data in power grid transactions. In this paper, we design a digital RMB smart contract model based on blockchain technology and use the hexadecimal model to create, deploy, and execute smart contract functions. And through the DTSC algorithm, smart contracts can be applied to the grid smart payment settlement. At the same time, a privacy protection algorithm for transaction data is proposed, and finally, the grid smart payment settlement system is designed based on smart contracts and privacy protection algorithms. Simulation test results show that the cost required for the digital RMB smart contract proposed in this paper is lower than the existing schemes in terms of invocation and deployment costs. The average chain code invocation delay in smart contract technology decreases when the transaction sending rate increases from 250TPS to 300TPS. Moreover, the on-chain operation time of this system is only 2.69 seconds, which meets the demand of practical power grid smart payment settlement applications. This paper sets the foundation for the efficient operation of the grid smart payment and settlement system and provides a guarantee for payment and settlement security.

Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction

The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points.

Privacy preservation of inventory management using adaptive key generation with Hyperledger blockchain technology

ABSTRACT The blockchain is an emerging and essential technology in offering various services to several applications including the financial industry and information technology. Consequently, recent information methodologies cannot satisfy the constraints. The data management issue may rise complexities including tracking operations with information security and unsatisfactory traceability data quality due to the high increase in inventory management data. To address the challenge, this paper aims to focus on developing new hyperledger blockchain-enabled secure inventory management with an adaptive key management process. Here, the inventory management data processed by different distributors is encrypted using the adaptive key generation by Fitness-derived Predator Presence Probability-based Squirrel Search Algorithm (FPPP-SSA) that ensures data control. The optimal key management in encryption solves the multi-objective function based on the “degree of modification information preservation rate and hiding ratio (Euclidean distance).” Once the data are enciphered, they are saved in the hyperledger blockchain, where the smart contract is the chain code application. While restoring or decrypting data, the authorized distributor should use the same adaptive key for maintaining secured inventory management. The performance is evaluated under diverse test cases, and the results are estimated under certain performance metrics.

Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space

This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space. The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network (CNN)-based models, i.e., U-Net (University of Freiburg, Germany) and DeepLabV3+ (Google, USA) models. Chain code technology, polyline approximation algorithm, and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces, including trace length, dip angle, density, and intensity. The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise, especially the DeepLabV3+ model. Furthermore, the quantization results further prove the reliability of extracting the fracture trace. As a result, a case study with two types of traces (i.e., on flat and uneven surfaces) demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.

Method for maize plants counting and crop evaluation based on multispectral images analysis

The processing of multispectral images acquired with embedded cameras in unmanned aerial vehicles (drones) has brought new opportunities for precision agriculture. In this study a method for evaluating the number of corn plants (Zea mays L) in a crop area is presented. Plant density is one of the most important yield factors, yet its precise measurement after the emergence of plants is impractical in large and medium-scale production, since significant amount of labor is required. For validation, a dataset of spectral images was gathered from flights over an agricultural area, and digital image processing techniques were applied, taking into account the concept of intelligent processing. Therefore, pattern recognition and models to aid decision-making through machine learning were also used. After image acquisition, the processing of orthomosaics in the spectral channels, i.e., red (R), green (G), and blue (B), was performed, making it possible to register and organize all the images. Likewise, techniques for geometric transformation, brightness, and contrast adjustments were evaluated globally, whereas local adjustments were evaluated based on the use of adaptive equalization techniques, which were explored based in the choice of the HSV color space. For the post-processing step, segmentation based on the best observed color threshold technique, in conjunction with Gaussian filtering and morphological operations, were considered. To enable pattern recognition, techniques that use distance maps were evaluated, considering the use of Euclidean distance. Thus, the locations of canopy patterns in maize plants were studied using a template matching algorithm and Chamfer pattern mask. For feature extraction, chain code and circular pattern map techniques were considered. The analyses made it possible to establish vectors of features based on patterns related to the number of maize plants occurrences. Finally, three calibration steps were considered, one related to the plant height versus the canopy opening radius, other related to the number of maize plants for each position in the crop area versus the radii identified by the developed model, and the third related to the cross-correlation between the plant counting by human vision and the new method. In addition, the classification step was established using a set of classifiers based on support vector machine (SVM). Results have shown an accurate and timely counting methodology for maize plants, which can guide cultivation to ensure high yield. The results showed that as a new method it can effectively count the number of maize plants with an average accuracy rate equal to 88.47%. Besides, both selected SVM classifiers have presented accuracy higher than 84% and precision higher than 83%. Furthermore, the cross-correlation between the plant counting by human vision and the new method has presented a linear correlation coefficient equal to 0.98. Thus, the developed method proved to be adequate for counting the maize plants in the post-emergence stage.

Wheat leaf diseases classification and severity analysis using HT-CNN and Hex-D-VCC-based boundary tracing mechanism.

Wheat is one among the significant crops for humans. Significant fungal illnesses of wheat are brought on by multiple pathogens. Wheat output could be enhanced by the early identification of wheat leaf disease. Thus, a novel hyperparameter tanh-based convolutional neural network (HT-CNN)-based wheat leaf disease prediction is proposed with its severity level. Here, initially, the red, green, and blue (RGB) images are converted into a hue saturation value (HSV) image. Next, the small probability space filtering is applied to the V component. Afterward, the contrast of the V component has been enhanced. The obtained HSV image is converted into the RGB image. Then, by employing weighted Canberra distance-based K-means (WCD-K means), the affected and normal regions are segmented. Next, the image is binarized. Afterward, for tracing a boundary around disease-affected region, the hex directional vertex chain code (Hex-D-VCC) is applied over the binarized image, and then the features are extracted. By employing baker's map-based Harris hawks optimization (BM-HHO), the optimal features are selected. For classifying disease, the selected features are further given into the HT-CNN, and the severity level is calculated to minimize the yield loss. As per the experimental result, the proposed model shows higher accuracy and efficacy when analogized to the other methods.

Intelligent Vehicle Inspection Tool Design Based on Freeman Chain Code for Automatic Annotation of 3D Models

Autonomous vehicle are more and more widely used in daily life, and the requirements for their safety performance are higher and higher. As a tool for testing auto parts, intelligent inspection tools are crucial to the guarantee of automobile quality. However, traditional fixture design relies on manual drawing, which is inefficient and prone to errors. To solve this problem, this research uses Freeman chain code to determine the annotation object, uses case clustering method to annotate, and uses error back propagation algorithm to realize case knowledge classification learning, and designs intelligent vehicle inspection tool design technology based on Freeman chain code 3D automatic annotation method. The experimental results show that the geometric feature matching results are correct, and the difference in feature comparison results is significant, with a high accuracy rate. Meanwhile, the geometric similarity annotation method has a high accuracy rate, taking only 3 minutes to complete the annotation, which is 7 minutes longer than traditional manual annotation. The error backpropagation algorithm can accurately achieve feature classification, and the design time of size chain inspection tool deformation design is reduced by 214 minutes compared to manual reverse deformation design, significantly improving design efficiency. In summary, the proposed design method for automotive inspection tools can achieve automatic model annotation, improve design efficiency, and reduce design time.

Data Security in Healthcare Industrial Internet of Things With Blockchain

The Industrial Internet of Things has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-Healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integration of E-healthcare and IIoT provides an analytical platform to handle a large amount of data with a low cost of cloud-enabling scalable storage. Throughout the transformation, patients’ personal information is at risk while exchanging records over centralized server-based systems. Thus, the rate of node connectivity, failure of parallel data sharing, and deliverance-related issues increase. In this paper, we present solutions in three folds. First, the paper proposes a novel and secure architecture for E-healthcare data security using a blockchain-distributed ledger technology named BHIIoT. Second, the transformation in the lifecycle of medical wireless sensor networks for data management and optimization with a distributed layered hierarchy is developed, which enhances the network resources and increases trust in the blockchain-enabled peer-to-peer environment. Third, the proposed BHIIoT uses the NuCypher threshold re-encryption mechanism for data encryption and protects shared resources in the form of blocks preserved in a blockchain immutable storage. For instance, chain codes are deployed to automate authentication, logging, index information deliverance, and trace transactions to resist illegal activities in the E-Healthcare distributed-application. The customized lightweight blockchain multi-proof-of-work and multi-proof-of-stake are designed with a digital signature for improving the consumption of resources and reducing the load of storage while the transaction process of E-healthcare IIoT is scheduled.

Design of a Novel Skin Lesion Predictor Model Using Hybrid Particle Swarm Optimization and Convolutional Neural Networks

The prediction of skin cancer at the earlier stage is extremely essential for melanoma. There is a need for intellectual computer analysis for skin lesions. The segmentation of lesion boundaries is vital to accurately identify the lesions from the dermoscopic images where the diagnosis is complex for various skin lesion types. Thus, some pre-processing steps are required to attain higher sensitive lesion boundary segmentation and classification. Initially, pre-processing is done with median filter to offer reputation of boundary preservation and does not in-cooperate newer pixel values to the processed image. Next is the process of image segmentation for Region of Interest (ROI) and non-Region of Interest ([Formula: see text] using the Jaccard Distance segmentation process. The novelty of the work relies on the inclusion of compression techniques to make the access easier without any loss. The extracted regions are encoded with Freeman Chain Coding and compressed with Lempel–Ziv–Welch (LZW) and Zero Tree Wavelet (EZW) for ROI and non-Region of Interest regions. Finally, image classification is done with Hybrid Particle Swarm Optimization and Convolutional Neural Networks ([Formula: see text]PSO-CNN). The simulation is done with TensorFlow &amp; Python environment and the proposed model outperforms the existing standard approaches. Some metrics like objective function, confusion matrix, accuracy, precision, [Formula: see text]-measure, and recall are evaluated. The model attains 77.5% accuracy, 86.36% precision, 77.5% recall and 77.92% [Formula: see text]-measure for proposed [Formula: see text]PSO-CNN which is higher than the standard CNN model.

Binocular measurement method for a continuous casting slab based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting.

Due to the low measurement accuracy of the continuous casting slab model caused by difficulty in detecting ideal corners, a binocular measurement method based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting is proposed. First, the one-dimensional probabilistic Hough transform based on inclination angle voting and the Freeman chain code is used to detect the line segments of the exterior outline. Next, sub-pixel points are extracted in each region of interest (ROI) by using Zernike moments, and sifted in the overlapping area of adjacent ROIs. Then the orthogonal total least squares (TLS) method is applied to fitting sub-pixel edges. Finally, after the key points are matched, three-dimensional localization and measurement are completed according to the binocular vision measurement principle. The experimental results show that the minimum relative error and average relative error of length reach 0.3401% and 0.3945%, respectively, satisfying the measurement requirement. Compared with scale-invariant feature transform (SIFT) and the oriented FAST and rotated BRIEF (ORB), the measurement error of the proposed algorithm is reduced by 80.01% and 74.63%, respectively. Compared with another edge fitting method based on k-means clustering and least squares fitting, its measurement error is reduced by 34.11%, and the time consumption is shortened by 39.07%, verifying its excellent performance in accuracy and efficiency.

Bayesian sampling with BeAtlas, a grid of synthetic Be star spectra I. Recovering the fundamental parameters of α Eri and β CMi

ABSTRACT Classical B emission (Be) stars are fast rotating, near-main-sequence B-type stars. The rotation and the presence of circumstellar discs profoundly modify the observables of active Be stars. Our goal is to infer stellar and disc parameters, as well as distance and interstellar extinction, using the currently most favoured physical models for these objects. We present BeAtlas, a grid of $61\, 600$ non-local thermodynamic equilibrium radiative transfer models for Be stars, calculated with the hdust code. The grid was coupled with a Monte Carlo Markov chain (MCMC) code to sample the posterior distribution. We test our method on two well-studied Be stars, α Eri and β CMi, using photometric, polarimetric, and spectroscopic data as input to the code. We recover literature determinations for most of the parameters of the targets, in particular the mass and age of α Eri, the disc parameters of β CMi, and their distances and inclinations. The main discrepancy is that we estimate lower rotational rates than previous works. We confirm previously detected signs of disc truncation in β CMi and note that its inner disc seems to have a flatter density slope than its outer disc. The correlations between the parameters are complex, further indicating that exploring the entire parameter space simultaneously is a more robust approach, statistically. The combination of BeAtlas and Bayesian-MCMC techniques proves successful, and a powerful new tool for the field: The fundamental parameters of any Be star can now be estimated in a matter of hours or days.