Computer Image Processing Research Articles

Imaging and quantification of human and viral circular RNAs.

We present a robust approach for cellular detection, imaging, localization, and quantification of human and viral encoded circular RNAs (circRNA) using amplified fluorescence in situ hybridization (ampFISH). In this procedure, a pair of hairpin probes bind next to each other at contiguous stretches of sequence and then undergo a conformational reorganization which initiates a target-dependent hybridization chain reaction (HCR) resulting in deposition of an amplified fluorescent signal at the site. By harnessing the capabilities of both ampFISH and single-molecule FISH (smFISH), we selectively identified and imaged circular RNAs and their linear counterparts derived from the human genome, SARS-CoV-2 (an RNA virus), and human cytomegalovirus (HCMV, a DNA virus). Computational image processing facilitated accurate quantification of circular RNA molecules in individual cells. The specificity of ampFISH for circular RNA detection was confirmed through an in situ RNase R treatment that selectively degrades linear RNAs without impacting circular RNAs. The effectiveness of circular RNA detection was further validated by using ampFISH probes with mismatches and probe pairs that do not bind to the continuous sequence in their target RNAs but instead bind at segregated sites. An additional specificity test involved probes against the negative strands of the circular RNA sequence, absent in the cell. Importantly, our technique allows simultaneous detection of circular RNAs and their linear counterparts within the same cell with single molecule sensitivity, enabling explorations of circular RNA biogenesis, subcellular localization, and functions.

Identifying topologically associating domains using differential kernels.

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix [Formula: see text], where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in [Formula: see text] within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

Landmark Detection Using Machine Learning

As smartphones and mobile data become more prevalent in modern society, the possibilities for them to interact with the physical world also grow exponentially. Technologies such as Oculus Rift and Google Glass are attempting to bridge the gap between the virtual and the physical, and as enhancements in computer speed and image processing are made, the concept of Augmented Reality (AR) becomes more tangible. However, one difficulty with AR is the sheer complexity of image processing and feature recognition. A successful AR system must be able to distinguish among a large number of landmarks and should be able to adapt to the existence of new landmarks. Because of the adaptability requirement, AR algorithms naturally lend themselves to using machine learning. As such, the focus of this project is to develop, refine and document a machine learning algorithm that can distinguish landmarks from images using a database of known landmarks.

Image Segmentation Techniques: An In-Depth Review and Analysis

In the fields of computer vision and image processing, image segmentation is a very important task. The image is partitioned into segments or regions then the visual data can be understood, analyzed and used easily. In this article, a comprehensive review of image segmentation methods is presented. It covers both the strengths and the advantages of some techniques as well as the weaknesses and limitations of other algorithms. The techniques used in image segmentation can be divided into four groups based on thresholding, region, edge and deep learning techniques. The fundamental principles, popularity of algorithms and evaluation metrics are mentioned. Also, the applications domains and future directions with current challenges are presented.

HDMA-CGAN: Advancing Image Style Transfer with Deep Learning

The integration of artificial intelligence (AI) and deep learning heralds a transformative era in pattern recognition and computer vision, notably in image style transfer. We introduce the hierarchical dynamic multi-attention cycle generative adversarial network (HDMA-CGAN), an innovative deep learning architecture poised to redefine image style transfer capabilities. HDMA-CGAN employs a novel multi-attention mechanism and color optimization strategies, enabling precise style replication with improved fidelity and vibrancy. Our model surpasses existing benchmarks in image quality, validated by leading metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). Although HDMA-CGAN advances the state of the art, it necessitates high computational resources and faces challenges with very high-resolution images. Future work could explore optimizing the model’s efficiency for real-time applications and extending its application to video content. This work enhances the tools available for visual content creation and digital media enhancement, leveraging advanced pattern recognition and AI techniques to significantly impact computer vision and image processing.

An approach-based machine learning and automated thermal images to predict the dark-cutting incidence in cattle management of healthcare supply chain

The healthcare supply chain is a network made up of various systems, processes, and elements that function and interact seamlessly to offer healthcare services and products. Food safety is an essential component of the healthcare supply chain. In the cattle industry, the healthcare supply chain contributes positively to the global economy through providing high-quality products such as milk and meat. Stress in cattle is one of main factor that cause low quality meat called “dark meat”. Numerous studies have been conducted on the development of different non-invasive methods based on Infrared Thermography Technology (IRT) to enhance the meat quality by detecting stress in cattle pre-slaughtering. These studies have the following issues: lack of automating in detecting body temperature of cattle and ignoring detecting stress with prediction dark meat incidence. The present study endeavors a new fully automated system for detecting stress, and dark meat incidence, incorporating the following new approaches: Multiview face detecting, Automatic eye localisation for detecting body temperature automatically employing computer vision and image processing, respectively. Furthermore, machine learning algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT) have been developed specifically for stress detection and the prediction of dark meat. To develop automated system, two forms of data were collected: statistical temperature and infrared thermal images. Infrared thermal images are used to develop Multiview face detection and Automatic eye localisation. Temperature data used to develop the machine learning model. Results reveal that Multiview face detection better than the current methods in term of Precision 0.99, Recall 0.91, F-score 0.95 with high True positive rate 0.90 and zero False-positive rate. Automatic eye localisation has high accuracy, with the following values: sensitivity 0.9780, precision 0.7212, F measure of 0.8024, and misclassification 0.0455. Lastly, results elaborate that the decision tree model can attain a notable level of accuracy in terms of specificity, recall, F-measure, and Area Under the Curve (AUC), all at an optimal rate of 98%.

Compatibilities of Cyprinus carpio with Varied Colors of Robotic Fish

Visual selection plays a fundamental role in various aspects of animal behavior, such as colony formation, maintenance, defense, and courtship. This study investigated the effect of bionic robot fish color on carp behavior based on physiological characteristics that were observed during behavioral experiments. Through computer image processing and analysis of light attenuation, we observed changes in the number and positioning of carp with bionic robotic fish of different colors (white, red, blue, green, and yellow). The results indicated that (1) the attenuation coefficient of visible light in freshwater was red > yellow > green > blue; (2) the order of the average change in the number of carp responding to different colors of robotic fish was white > red > green > yellow > blue, and carp were more sensitive and responsive to white and red robotic fish; and (3) the order of the distances between different colors of robotic fish and carp was white < yellow < blue < green < red, and white and yellow robotic fish were more attractive to carp. Therefore, the use of white or yellow robotic fish for relevant operations can reduce disturbance to fish schools.

A survey on planet leaf disease identification and classification by various machine-learning technique

An overview of methods for identifying plants diseases is given in this article. Each sample is categorized by being divided into various groups. The approach of classification involves identifying healthy and diseased leaves based on morphological traits including texture, color, shape, and pattern, among others. Sorting and categorizing plants can be challenging, especially when doing so across a large area, due to the closeness of their visual qualities. There are several methods based on computer vision and image processing. Selecting the right categorization method can be difficult because the outcomes rely on the data you supply. There are several applications for the categorization of plant leaf diseases in fields like agriculture and biological research. This article gives a summary of several approaches currently in use for identifying and categorizing leaf diseases, as well as their benefits and drawbacks. These approaches include preprocessing methods, feature extraction and selection methods, datasets employed, classifiers, and performance metrics

Identification and classification of Prakriti of human using facial features

<p>Changes in the lifestyle of individual, several of disease has emerged due to imbalance of doshas components. Ayurveda practitioners could identify the imbalance of dosha and relate the root cause of imbalance of doshas. Analysis of dosha varies from practitioner to practitioner and it requires well practiced practitioner to identify dosha. To overcome, the darshana method was adopted to automatically identify predominant dosha using facial features such as face, eyes, nose, and mouth and skin color. Computer vision and Image processing techniques were made attempted in Ayurveda domain, for identification of predominate prakriti, age, and gender of the subject. Eye Aspect Ratio (EAR), Nose Aspect Ratio (NAR), Mouth Aspect Ratio (MAR) and skin color was computed based on Euclidean distance to identify on-live predomaint prakriti of an individual. The values of MAR ≤ 0.5,EAR ≤ 0.1,NAR ≤ 0.8 as identified as vata , 0.5 ≥ MAR ≤ 0.6, 0.1 ≥ EAR ≤ 0.2, 0.8 ≥ NAR ≤ 1 as identified as pitta and MAR ≥ 0.6,EAR ≥ 0.2 and NAR ≥ 1 as identified as kapha dosha. With the features MAR, EAR and NAR classification of predominant prakriti was carried out with an accuracy of 87.5% with support vector classifier.</p>

Tool wear image segmentation algorithm based on K-means clustering

Abstract The existing tool wear region segmentation and detection methods make it difficult to achieve accurate detection of tool wear images under the conditions of noise interference. To further improve the detection efficiency and detection accuracy, combined with the characteristics of tool wear, this paper proposes a tool wear image segmentation algorithm based on K-means clustering to achieve accurate detection of tool wear images. Firstly, the acquired image is pre-processed to reduce the image processing computation and optimize the image, filter out the noise, and reduce the interference of the image. Then K-means clustering is used to cluster the surface features of the image, and then a threshold segmentation algorithm is used to locate the tool wear area and segment it out. The final experimental results show that the segmentation accuracy of this method is high and can be applied to tool wear area detection.

ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

BackgroundPlants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues.ResultsHere we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms.ConclusionTaken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

Select & Enhance: Masked-based image enhancement through tree-search theory and deep reinforcement learning

The enhancement of low-quality images is both a challenging task and an essential endeavor in many fields including computer vision, computational photography, and image processing. In this paper, we propose a novel and fully explainable method for image enhancement that combines spatial selection and histogram equalization. Our approach leverages tree-search theory and deep reinforcement learning to iteratively select areas to be processed. Extensive experimentation on two datasets demonstrates the quality of our method compared to other state-of-the-art models. We also conducted a multi-user experiment which shows that our method can emulate a variety of enhancement styles. These results highlight the effectiveness and versatility of the proposed method in producing high-quality images through an explainable enhancement process.

OBJECT RECOGNITION AND LOCALIZING FOR VISUALLY IMPAIRED PEOPLE

It is estimated that there are already tens of millions of visually impaired persons in the world, accounting for a significant fraction of the population. Their integration into society is a primary and continuous objective. Assuring a healthcare system has required a great deal of work. Many guiding system solutions have been developed to assist people with vision impairments in leading normal lives. These systems are often designed with specific goals in mind. Nonetheless, these fixes can greatly increase the security and mobility of these people. Several frame rates are used to record the visual image. The processing of every acquired image comes next. The processing result will alert the user based on the object that was discovered and its position. Key words: computer vision, image processing, guidance system, and object recognition.

Application of Computer Image Processing Technology in Intelligent Monitoring System

With the continuous progress of science and technology and the rapid development of society, intelligent surveillance systems are gradually emerging in the field of security and other related fields, and have become a key tool for ensuring public safety and improving efficiency. Intelligent monitoring system with computer vision and image processing technology as the core, with the help of advanced hardware equipment and intelligent algorithms, can be efficiently processed and deeply analysed image information in real-time monitoring. This paper provides a strong support to build a safer and more efficient society by studying in depth the application of image processing technology in intelligent surveillance system with the goal of comprehensively promoting the technological innovation of intelligent surveillance system.

Network architecture for single image super‐resolution: A comprehensive review and comparison

AbstractSingle image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers.

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability.

Enhancing Brain Tumour Identification through Convolutional Neural Network

A tumor is carried on by rapid and uncontrolled cell proliferation in the brain. If it is not treated in the early stages, it could prove fatal. Despite multiple significant efforts and encouraging outcomes, accurate segmentation and classification still pose a difficulty. Detection of brain tumors is extremely complicated by the distinctions in tumor position, structure, and proportions. Using computational intelligence and statistical image processing techniques, proposed in our project provide multiple approaches to detect brain cancer and tumors. Also shows an evaluation matrix for a specific system using particular systems and type of dataset. Also explains the morphology of brain tumors, accessible data sets, augmentation techniques, component extraction, and classification of Deep Learning (DL), Transfer Learning (TL), and Machine Learning (ML) models. Key Words: brain tumour, image classification, image sementation, convolutional neural networks.

Application of Computer Image Processing and Recognition Technology

Application of Computer Image Processing and Recognition Technology

Quantitative study of solid-state mechanical mixing by high-pressure torsion based on image processing

Mechanical alloying through the process of severe plastic deformation can significantly enhance the mechanical properties of dissimilar metals. However, uneven mixing of these materials can adversely affect their mechanical performance and may even lead to material failure. To quantitatively assess the degree of mixing between dissimilar materials, a mechanical mixing efficiency index (η) has been introduced. This index is calculated accurately by combining an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with computer image processing techniques. The study focuses on the Al-Zn mechanical mixture, establishing a parametric correlation between the mechanical mixing efficiency index and the microstructural evolution of mechanical mixing. The findings indicate that when the η value falls below 0.27865, the material transitions from the mechanical mixing stage to the chemical fusion stage. This research facilitates the controllable preparation of mechanical alloying, providing theoretical support and practical evidence for the process.