Transformation Technique Research Articles

Towards robust image matching in low-luminance environments: Self-supervised keypoint detection and descriptor-free cross-fusion matching

Image keypoint detection and feature matching are fundamental steps in computer vision tasks. However, variations in environment, time, and viewpoint pose a challenge to the stability of image keypoint detection and matching. Most traditional and deep learning-based methods cannot accurately and efficiently extract highly repeatable keypoints and robust match pairs in low-luminance environments. Therefore, we propose a two-step ‘detection + matching’ framework, which consists of deep neural networks in each step. Firstly, we design a self-supervised robust keypoint detection network, which utilizes multi-scale, multi-angle, and multi-luminance transformation techniques to create pseudo-labeled datasets to improve the model’s keypoint detection repeatability and luminance invariance. Secondly, we propose a descriptor-free cross-fusion matching network, which uses the cross-fusion attention mechanism to establish connections between keypoint-centered image patches and converts the feature-matching task into an image patch assignment task to improve the accuracy and efficiency of matching. Thirdly, the proposed framework is used to replace traditional SIFT in SfM. Experimental results on testing datasets show that the self-supervised robust keypoint detection network achieves higher keypoint repeatability in low luminance environments compared to SIFT, ORB, LIFT, and Superpoint. The descriptor-free cross-fusion matching network’s mean matching accuracy and efficiency are higher than the mainstream Superglue algorithm. Also, SfM achieves better performance regarding the number of sparse point clouds and accuracy.

Application of Developmental Regulators for Enhancing Plant Regeneration and Genetic Transformation.

Establishing plant regeneration systems and efficient genetic transformation techniques plays a crucial role in plant functional genomics research and the development of new crop varieties. The inefficient methods of transformation and regeneration of recalcitrant species and the genetic dependence of the transformation process remain major obstacles. With the advancement of plant meristematic tissues and somatic embryogenesis research, several key regulatory genes, collectively known as developmental regulators, have been identified. In the field of plant genetic transformation, the application of developmental regulators has recently garnered significant interest. These regulators play important roles in plant growth and development, and when applied in plant genetic transformation, they can effectively enhance the induction and regeneration capabilities of plant meristematic tissues, thus providing important opportunities for improving genetic transformation efficiency. This review focuses on the introduction of several commonly used developmental regulators. By gaining an in-depth understanding of and applying these developmental regulators, it is possible to further enhance the efficiency and success rate of plant genetic transformation, providing strong support for plant breeding and genetic engineering research.

A novel and efficient Apple latent spherical virus-based gene silencing method for functional genomic studies in Chenopodium quinoa

Quinoa (Chenopodium quinoa Willd.), with its resilience in harsh environments and excellent nutritional value, has become crucial for global food security. Despite recent progress in genomic research, the inability to perform functional studies in quinoa due to the absence of transformation techniques remains a significant obstacle. In this work, we present the development of a novel Apple Latent Spherical Virus (ALSV)-mediated virus-induced gene silencing (VIGS) protocol that will allow to perform functional genomics studies in quinoa in a fast and simple way. The method was fine-tuned using ALSV plasmids that carry partial gene sequences of phytoene desaturase (PDS) from Nicotiana benthamiana and quinoa. The developed technique involves an initial inoculation in Nicotiana plants through agroinfiltration with Agrobacterium tumefaciens cultures carrying the different viral constructs. Viral extracts were prepared using local or systemic leaves, which were then used as inoculum to infect quinoa leaves through mechanical damage. The method was successfully tested in two contrasting quinoa varieties, although some differences were observed in infection phenotype and viral susceptibility. The effect of insertion sequence size in the viral vectors was also analyzed, resulting in differences in bleaching or chlorosis phenotype and impact on plant growth. The presence of the virus in inoculated plants was confirmed, and the reduction in PDS gene expression in silenced plants was verified. Because quinoa lacks stable transformation protocols, limiting heterologous expression assays, our ALSV-based VIGS protocol is very attractive for loss-of-function gene studies.

Establishment of genome-editing system and assembly of a near-complete genome in broomcorn millet.

The ancient crop broomcorn millet (Panicum miliaceum L.) is an indispensable orphan crop in semi-arid regions due to its short life cycle and excellent abiotic stress tolerance. These advantages make it an important alternative crop to increase food security and achieve the goal of zero hunger, particularly in light of the uncertainty of global climate change. However, functional genomic and biotechnological research in broomcorn millet has been hampered due to a lack of genetic tools such as transformation and genome-editing techniques. Here, we successfully performed genome editing of broomcorn millet. We identified an elite variety, Hongmi, that produces embryogenic callus and has high shoot regeneration ability in in vitro culture. We established an Agrobacterium tumefaciens-mediated genetic transformation protocol and a clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated genome-editing system for Hongmi. Using these techniques, we produced herbicide-resistant transgenic plants and edited phytoene desaturase (PmPDS), which is involved in chlorophyll biosynthesis. To facilitate the rapid adoption of Hongmi as a model line for broomcorn millet research, we assembled a near-complete genome sequence of Hongmi and comprehensively annotated its genome. Together, our results open the door to improving broomcorn millet using biotechnology.

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications. This study addresses the importance of estimating the friction angle and presents the development of four soft computing models: YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. First of all, the Yeo-Johnson (YJ) transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances. This technique is expected to improve the accuracy of friction angle prediction models. The friction angle prediction models then utilized multi-layer perceptron neural networks (MLPnet) and metaheuristic optimization algorithms to further enhance performance, including flower pollination algorithm (FPA), coral reefs optimization (CRO), ant colony optimization continuous (ACOC), and cuckoo search algorithm (CSA). The prediction models without the YJ technique, i.e. FPA-MLPnet, CRO-MLPnet, ACOC-MLPnet, and CSA-MLPnet, were then compared to those with the YJ technique, i.e. YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. Among these, the YJ-CRO-MLPnet model demonstrated superior reliability, achieving an accuracy of up to 83% in predicting the friction angle of clay in practical engineering scenarios. This improvement is significant, as it represents an increase from 1.3% to approximately 20% compared to the models that did not utilize the YJ transformation technique.

인공지능기반 사전학습언어모델 적용방안에 관한 연구

Pre-trained Language Model(PLM) refers to a natural language processing(NLP) model that has been pre-trained using large amounts of text data. The PLM has the limitation of not being able to understand domain-specific terminology due to a lack of training data for terminology. Therefore, the need for a domain-specific language model modified through BERT- or GPT-based pre-trained learning has recently been emphasized. In this study, we analyze BERT's pre-training method and BERT-based transformation techniques (ALBERT, RoBERTa, ELECTRA) and propose a PLM that can be used in biomedical, financial, and legal domains. The biomedical-specific pre-trained learning model is designed to learn domain-specific language characteristics such as technical terminology, medical sentence structure, and medical entity name recognition in the biomedical field. It is mainly adjusted to be applied to biomedical tasks through transfer learning based on BERT's pre-training method and architecture. For this purpose, it is pre-trained with pre-trained biomedical text data, and this pre-training transfers domain-specific knowledge to the model through learning representations for biomedical-related texts. The finance-specific pre-trained learning model is a model that can understand and process financial terminology, financial market trends, and sentence structures and vocabulary related to financial products and services. It can be used to generate news articles about financial market trends and to extract key information by concisely summarizing long texts such as financial reports and corporate press releases. Additionally, finance-specific pre-trained models help financial analysts generate investment recommendations based on a company's financial condition, performance, and prospects. The legal-specific pre-trained model is a language model suitable for legal documents and is used for legal document classification, legal document summarization, and legal document similarity evaluation. The legal-specific pre-learning model was created by pre-training the BERT model on special texts in the legal field, and through this, it learns characteristics specialized for legal documents. The performance of the legal-specific pre-training model can be improved to solve legal-related tasks through scratch pre-training and additional pre-training using legal corpora.

A Comparison of Hilbert and Fast Fourier Transform Techniques for Reciprocating Motors

Human existence has been significantly impacted by the growth of information and technology, particularly in relation to the requirement for effective mobility. But as a car ages, symptoms that affect its performance and safety start to show. These symptoms are a serious worry. Interference with car parts, such coils, is one issue that can have a major effect on driving safety and performance. Vibration analysis techniques have been successful in addressing this problem by detecting damage to different car parts. With the intention of comprehending piston motor vibration characteristics at different rotational speeds. The piston motor was used to collect vibration data at three distinct speeds: 900 rpm, 1500 rpm, and 3500 rpm. Both the Hilbert approach, which offers profound insight into frequency energy, and an FFT analyzer, which produces clear frequency spectrum information, were used in the analysis. The findings demonstrated that vibration characteristics changed in response to variations in reciprocating motor speed. On the other hand, the average frequency energy tends to remain constant across a range of speeds, suggesting that the features of reciprocating motors may be complicated and not very affected by variations in speed. According to this study, different perspectives on the vibration characteristics of piston motors operating at different speeds can be gained by utilizing vibration analysis techniques like FFT and Hilbert. Improved comprehension of this phenomenon could have a significant impact on maintenance initiatives and performance analysis of vehicles. To fully understand the complexity of vibration characteristics in reciprocating motors at various speed conditions, more research will be needed

Business Transformation and the Performance of Manufacturing Frms in Nigeria

The business atmosphere is increasingly becoming competitive due to the reality of global dynamics. Thus, firms especially, the manufacturing sub-sector need to adopt transformation strategies to enable them compete favorably. This study titled Business transformation and Performance of the Manufacturing Firms in Nigeria is written to examine the effect of business transformation on performance of manufacturing sub-sector in North-Central Nigeria. The population of the study was 1404 spread across the selected manufacturing firms in Nigeria. However, due to the fact that the population is large the researcher utilized the Godden sample size statistical technique to reach 302 respondents, however, only 284 respondents completed and returned their questionnaire which shows a retrieval rate of 94%. More so, the study applied research survey design, and the statistical tools applied comprised descriptive and parametric statistics. More so, the hypothesis was tested using simple linear regression analysis. The findings revealed that there is a significant positive relationship between Technological Resource application and First Pass Yield (r =.939, p-value <0.05. In view of the finding, the study concluded that there is a significant positive relationship between Business transformation and Performance of manufacturing firms in Nigeria. Anchored on the finding the study recommends that a Business Transformation techniques should be improved and periodic review be carried out in order to sustain and improve performance.

Mathematical modeling of entropy generation in MHD mixed convective Cu–Ag-Al2O3/H2O tri-hybrid nanofluid over an exponential permeable shrinking surface with radiation and slip impacts: Multiple solutions with stability analysis

The present investigation aims to determine the existence of a dual solution in magnetohydrodynamic (MHD) mixed convective radiative flow of Cu-Ag- A l 2 O 3 / H 2 O tri-hybrid nanofluid in a permeable Darcy–Forchheimer porous medium over an exponentially shrinking surface by considering velocity, thermal slips, and suction effects at the surface. This study focuses on assessing entropy production in novel coolant applications. To streamline the analysis, the complex nonlinear partial differential equations (PDEs) are simplified by converting them into a set of ordinary differential equations (ODEs) through the utilization of the similarity transformation technique. These ODEs are then solved utilizing the bvp4c numerical MATLAB function. Graphical and tabular analyses are conducted to investigate the impact of emerging variables on entropy generation, as well as velocity, temperature, skin friction coefficient, and Nusselt number. Due to the contraction of the surface, dual solutions are found, but dual solutions cannot be found beyond the critical values. The critical values of S C are 1.58928, 1.48700, and 1.66058, but ξ C are − 1.239499 , − 1.416999 , and − 1.130030 for Cu / H 2 O nanofluid, Cu − Ag / H 2 O hybrid nanofluid, and Cu − Ag − A l 2 O 3 / H 2 O tri-hybrid nanofluid, respectively. A positive minimum eigenvalue γ 1 signifies the existence of the upper stable solution branch, while a negative minimal eigenvalue indicates the presence of the lower unstable solution branch. The tri-hybrid nanofluid exhibits superior thermal properties compared to nanofluid and hybrid fluids. Adding nanoparticles to traditional fluids is perceived as improving their ability to transmit heat. The analysis has demonstrated that the thermal radiation and temperature slip parameters significantly influence the heat transfer rate. Thermal radiation is found to speed up the creation of entropy. Additionally, in the case of a stable solution, an increase in the Forchheimer number results in a deceleration of the liquid flow, an effect which is further amplified by the presence of the magnetic field and velocity slip parameter.

Unveiling the Untapped Potential of Bertagnini's Salts in Microwave-Assisted Synthesis of Quinazolinones.

Microwave-assisted organic synthesis (MAOS) has emerged as a transformative technique in organic chemistry, significantly enhancing the speed, efficiency, and selectivity of chemical reactions. In our research, we have employed microwave irradiation to expedite the synthesis of quinazolinones, using water as an eco-friendly solvent and thereby adhering to the principles of green chemistry. Notably, the purification of the product was achieved without the need for column chromatography, thus streamlining the process. A key innovation in our approach is using aldehyde bisulfite adducts (Bertagnini's salts) as solid surrogates of aldehydes. Bertagnini's salts offer several advantages over free aldehydes, including enhanced stability, easier purification, and improved reactivity. Green metrics and Eco-Scale score calculations confirmed the sustainability of this approach, indicating a reduction in waste generation and enhanced sustainability outcomes. This methodology facilitates the synthesis of a diverse array of compounds, offering substantial contributions to the field, with potential for widespread applications in pharmaceutical research and beyond.

MULTIMEDIA FRAMING OF BRECHTIAN CHIMERISM

Brecht's epic theater in its artistic diversity includes works that, having received multimedia interpretation, not only do not lose their artistic and aesthetic significance but also enhance it. This refers to B. Brecht's use of photo-epigrams from "Kriegsfibel", which became emblems of war. "Kriegsfibel" is the title of the second part of the great oratorio "Deutsches Miserere", which emerged as a result of Bertolt Brecht's creative collaboration with the composer Paul Dessau. Twenty-nine photo-epigrams formed the basis of the concert, which Brecht called "Deutsches Miserere" – "the great repentance of the German people". In this article, we examine the basic principles of the construction of "Deutsches Miserere", focusing on the functional features of the alienation effect in the work through multimedia and intermedia means. The focus lies on the interconnection and combination of different types of art: fiction, photography, cinematography, and music aimed at creating an original author's language. The article claims that the unifying principle that connects them is chimerism, which contributes to the effect of alienation and serves to provocatively and paradoxically comprehend and embody a certain theme or idea. In the triad of "principle – technique – medium", the leading artistic principle is the effect of alienation, one of the techniques of artistic transformation of reality is chimerism, and the medium is multimedia. The introduction of multimedia amplifies the alienation effect, and this allows us to consider the interaction of chimerism with intermedial means of expression, namely, it makes intramediality the sphere of existence of chimerism.

Hydrothermal Treatment: A Critical Research on Improving Milling Efficiency Using the Parboiling Process for Pearl Millet

Hydrothermal treatment, particularly through the parboiling process, has emerged as a critical method for enhancing milling efficiency in pearl millet. This study provides a comprehensive analysis of the various aspects related to parboiling as a means to improve the quality and yield of milled pearl millet grains. The study begins by elucidating the significance of milling efficiency in pearl millet processing, considering its nutritional value and widespread consumption in regions such as sub-Saharan Africa and Asia. It then delves into the principles underlying the parboiling process, including soaking, steaming, and drying stages, and their impact on the physicochemical properties of millet grains. Key factors influencing milling efficiency, such as soaking temperature, duration, and steaming conditions, are thoroughly examined based on recent research findings and empirical evidence. The study highlights the critical role of gelatinization in starch transformation, protein denaturation, and fiber preservation during the parboiling process, contributing to improved milling outcomes. Furthermore, the review explores the effects of parboiling on reducing grain breakage, enhancing milling yield, and minimizing nutrient losses, thereby optimizing the overall milling efficiency of pearl millet. It discusses the implications of parboiling on nutritional quality, food safety, and economic viability, underscoring its potential to address challenges in millet processing and promote sustainable agricultural practices. The study concludes by outlining future research directions and practical considerations for optimizing the parboiling process in pearl millet production. It emphasizes the importance of interdisciplinary collaboration, knowledge exchange, and technology transfer to realize the full benefits of hydrothermal treatment in improving milling efficiency and enhancing the value chain of pearl millet. In summary, this critical review provides valuable insights into the role of parboiling as a transformative technique for enhancing milling efficiency in pearl millet processing, offering implications for research, policy, and practice in the domains of food security, nutrition, and agricultural development.

Diabetic retinopathy prediction based on vision transformer and modified capsule network

Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating the evolution of this disease depends on its early detection as well as identifying the exact levels of this pathology, which is done manually by ophthalmologists. This manual process is very consuming in terms of the time and experience of an expert ophthalmologist, which makes developing an automated method to aid in the diagnosis of diabetic retinopathy an essential and urgent need. In this paper, we aim to propose a new hybrid deep learning method based on a fine-tuning vision transformer and a modified capsule network for automatic diabetic retinopathy severity level prediction. The proposed approach consists of a new range of computer vision operations, including the power law transformation technique and the contrast-limiting adaptive histogram equalization technique in the preprocessing step. While the classification step builds up on a fine-tuning vision transformer, a modified capsule network, and a classification model combined with a classification model, The effectiveness of our approach was evaluated using four datasets, including the APTOS, Messidor-2, DDR, and EyePACS datasets, for the task of severity levels of diabetic retinopathy. We have attained excellent test accuracy scores on the four datasets, respectively: 88.18%, 87.78%, 80.36%, and 78.64%. Comparing our results with the state-of-the-art, we reached a better performance.

Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

Lexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a QA system to assign a high score to the most relevant answers. Although considerable research efforts exist for LAT prediction in diverse domains, it remains a challenging biomedical problem. LAT prediction for the biomedical field is a multi-label classification problem, as one biomedical question might have more than one expected answer type. Achieving high performance on this task is challenging as biomedical questions have limited lexical features. One biomedical question must be assigned multiple labels given these limited lexical features. In this paper, we develop a novel feature set (lexical, noun concepts, verb concepts, protein–protein interactions, and biomedical entities) from these lexical features. Using ensemble learning with bagging, we use the label power set transformation technique to classify multi-label. We evaluate the integrity of our proposed methodology on the publicly available multi-label biomedical questions dataset (MLBioMedLAT) and compare it with twelve state-of-the-art multi-label classification algorithms. Our proposed method attains a micro-F1 score of 77%, outperforming the baseline model by 25.5%.

IMAGE STEGANOGRAPHY USING MID POINT TRANSFORMATION TECHNIQUE

The project titled "Image Steganography using Mid-Point Transformation Technique" aims to explore and implement a novel approach to concealing information within digital images while preserving their visual integrity. Steganography is an age-old technique for covert communication, and this project leverages the mid-point transformation method to embed data seamlessly into images. The mid-point transformation technique involves the subtle alteration of pixel values based on the midpoint of neighboring pixels. This process ensures that the changes made to the image are imperceptible to the human eye, allowing for effective data hiding without compromising the overall visual quality. The project will focus on the development of an algorithm to encode and decode hidden information within images using the mid-point transformation technique. Implementation will be carried out using a programming language suitable for image processing, and the project aims to provide a user-friendly interface for ease of use. Key objectives include understanding the theoretical foundations of steganography, implementing the mid-point transformation algorithm, evaluating the effectiveness of the technique in terms of data capacity and visual impact, and comparing the results with existing steganographic methods. Key Words: Image Steganography, Mid Point Transformation, Steganography Techniques, Digital Image Security, Data Hiding, Information Security.

Almost sure and mean square convergence of iterative learning control system with state feedback under random packet dropouts

This paper investigates the almost sure and mean square convergence of a state feedback-based iterative learning control scheme for a class of discrete-time multi-input–multi-output linear systems with a direct feedback term in the presence of random data dropouts. Firstly, the realisability of the control system is considered. It is shown that the realisability of the control system is only determined by the direct feedback matrix, which has nothing to do with the input–output coupling matrix, and the direct feedback matrix to be full-row rank is enough to guarantee the system realisable. Secondly, the convergence of output tracking errors is analysed in both the almost sure and mean square senses, respectively. By the contraction mapping method combined with matrix decomposition and transformation techniques, it is strictly proved that the output sequence can converge to any given desired trajectory in the sense of almost sure and mean square, respectively, if and only if the general spectral radius condition is satisfied. Finally, an example is given to show the correctness of the theoretical analysis.

A rotating magneto-photothermoelastic effect with moisture diffusivity of nonlocal semiconductor medium

This study focuses on investigating the deformation of a one-dimensional elastic nonlocal semiconductor medium. The aim is to understand how the magnetic field and moisture diffusivity affect this deformation. The research aims to analyze the connection between plasma and moisture diffusivity processes in a rotational field, as well as thermo-elastic waves. The study examines the transport process of photo-thermoelasticity while considering the influence of moisture diffusivity. To derive the governing equations of the photo-thermo-elastic medium, Laplace’s transformation technique is used. These equations encompass the carrier density, elastic waves, moisture transport, heat conduction, and constitutive relationships. The fundamental physical parameters in the Laplace domain, such as mechanical stresses, thermal conditions, and plasma boundary conditions, are calculated. Numerical techniques are employed to invert the Laplace transform and obtain complete time-domain solutions for the various physical domains under investigation. The analysis takes into account reference moisture, nonlocality, magnetic field and rotation field. The effects of applied forces on displacement, moisture concentration, carrier density, stress resulting from forces, and temperature distribution are considered through graphical analysis.

Comparative transcriptome and functional analyses provide insights into the key factors regulating shoot regeneration in highbush blueberry.

Establishing an efficient plant regeneration system is a crucial prerequisite for genetic engineering technology in plants. However, the regeneration rate exhibits considerable variability among genotypes, and the key factors underlying shoot regeneration capacity remain largely elusive. Blueberry leaf explants cultured on a medium rich in cytokinins exhibit direct shoot organogenesis without prominent callus formation, which holds promise for expediting genetic transformation while minimizing somatic mutations during culture. The objective of this study is to unravel the molecular and genetic determinants that govern cultivar-specific shoot regeneration potential in highbush blueberry (Vaccinium corymbosum L.). We conducted comparative transcriptome analysis using two highbush blueberry genotypes: 'Blue Muffin' ('BM') displaying a high regeneration rate (>80%) and 'O'Neal' ('ON') exhibiting a low regeneration rate (<10%). The findings revealed differential expression of numerous auxin-related genes; notably, 'BM' exhibited higher expression of auxin signaling genes compared to 'ON'. Among blueberry orthologs of transcription factors involved in meristem formation in Arabidopsis, expression of VcENHANCER OF SHOOT REGENERATION (VcESR), VcWUSCHEL (VcWUS), and VcCUP-SHAPED COTYLEDON 2.1 were significantly higher in 'BM' relative to 'ON'. Exogenous application of auxin promoted regeneration, as well as VcESR and VcWUS expression, whereas inhibition of auxin biosynthesis yielded the opposite effects. Overexpression of VcESR in 'BM' promoted shoot regeneration under phytohormone-free conditions by activating the expression of cytokinin- and auxin-related genes. These findings provide new insights into the molecular mechanisms underlying blueberry regeneration and have practical implications for enhancing plant regeneration and transformation techniques.

Image Morpher

The "Image Morphing with the Beier-Neely Method" research paper extensively explores image transformation techniques, with a special focus on the Beier- Neely approach. Image morphing, the art of seamlessly transforming one image into another, has garnered significant attention for its applications in animation, filmmaking, and scientific visualization. The research paper delves into the historical context and importance of image morphing, particularly emphasizing the role of the Beier-Neely method in addressing the limitations of earlier techniques. Classical image morphing methods, characterized by pixel-level interpolation, suffer from visual artifacts, manual correspondence definition, and sluggishness. These shortcomings set the stage for the introduction of the feature-based Beier-Neely method, which revolutionized image morphing by allowing control points on features, facilitating smooth transitions and natural transformations. This research papers objectives are clearly articulated, with the primary aim of implementing the Beier- Neely method to produce high-quality image morphs. The research paper proposes a system architecture that underscores the use of feature-based control points, Delaunay triangulation, affine transformations, and the significance of visual feedback. Additionally, it provides a detailed analysis of the mathematical foundations and algorithmic intricacies of the Beier-Neely method, elucidating the theoretical framework driving its effectiveness. Overall, the research paper provides a comprehensive exploration of image morphing techniques, highlighting the pivotal role played by the Beier-Neely method in advancing the field.. Key Words: Image Morphing, Beier-Neely Method, Seamless Transitions, Feature-based Control Points, System Architecture, Mathematical Foundations

Detection of maize stem diameter by using RGB-D cameras' depth information under selected field condition.

Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error (MAPE) of 3.01%, a Mean Absolute Error (MAE) of 0.75mm, a Root Mean Square Error (RMSE) of 1.07mm, and a coefficient of determination (R²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.