Review of the deep learning for food image processing

As deep learning techniques are increasingly applied with greater depth and sophistication in the food industry,the realm of food image processing has progressively emerged as a central focus of research interest.This work provides an overview of key practices in food image processing techniques,detailing common processing tasks including classification,recognition,detection,segmentation,and image retrieval,as well as outlining metrics for evaluating task performance and thoroughly examining existing food image datasets,along with specialized food-related datasets.In terms of methodology,this work offers insight into the evolution of food image processing,tracing its development from traditional methods extracting low and intermediate-level features to advanced deep learning techniques for high-level feature extraction,along with some synergistic fusion of these approaches.It is believed that these methods will play a significant role in practical application scenarios such as self-checkout systems,dietary health management,intelligent food service,disease etiology tracing,chronic disease management,and food safety monitoring.However,due to the complex content and various types of distortions in food images,further improvements in related methods are needed to meet the requirements of practical applications in the future.It is believed that this study can help researchers to further understand the research in the field of food imaging and provide some contribution to the advancement of research in this field.

Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

The mechanical properties and failure mechanism of lightweight aggregate concrete(LWAC)is a hot topic in the engineering field,and the relationship between its microstructure and macroscopic mechanical properties is also a frontier research topic in the academic field.In this study,the image processing technology is used to establish a micro-structure model of lightweight aggregate concrete.Through the information extraction and processing of the section image of actual light aggregate concrete specimens,the mesostructural model of light aggregate concrete with real aggregate characteristics is established.The numerical simulation of uniaxial tensile test,uniaxial compression test and three-point bending test of lightweight aggregate concrete are carried out using a new finite element method-the base force element method respectively.Firstly,the image processing technology is used to produce beam specimens,uniaxial compression specimens and uniaxial tensile specimens of light aggregate concrete,which can better simulate the aggregate shape and random distribution of real light aggregate concrete.Secondly,the three-point bending test is numerically simulated.Thirdly,the uniaxial compression specimen generated by image processing technology is numerically simulated.Fourth,the uniaxial tensile specimen generated by image processing technology is numerically simulated.The mechanical behavior and damage mode of the specimen during loading were analyzed.The results of numerical simulation are compared and analyzed with those of relevant experiments.The feasibility and correctness of the micromodel established in this study for analyzing the micromechanics of lightweight aggregate concrete materials are verified.Image processing technology has a broad application prospect in the field of concrete mesoscopic damage analysis.

A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images.Automated analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer variability.This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework.The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)algorithms.Traditional FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation output.Our method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate noise.The results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image segmentation.Furthermore,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)Algorithm.Comparative analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging.

Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

Parallel Image Processing: Taking Grayscale Conversion Using OpenMP as an Example

In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.

Single-Phase Velocity Determination Based in Video and Sub-Images Processing:An Optical Flow Method Implemented with Support of a Programmed MatLab Structured Script

Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.

Correlations between mineral composition and mechanical properties of granite using digital image processing and discrete element method

This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing(DIP) and discrete element method(DEM). The results showed that the X-ray diffraction(XRD)-based DIP method effectively analyzed the mineral composition contents and spatial distributions of granite. During the particle flow code(PFC2D) model calibration phase, the numerical simulation exhibited that the uniaxial compressive strength(UCS) value, elastic modulus(E), and failure pattern of the granite specimen in the UCS test were comparable to the experiment. By establishing 351 sets of numerical models and exploring the impacts of mineral composition on the mechanical properties of granite, it indicated that there was no negative correlation between quartz and feldspar for UCS, tensile strength(σ_(t)), and E. In contrast, mica had a significant negative correlation for UCS, σ_(t), and E. The presence of quartz increased the brittleness of granite, whereas the presence of mica and feldspar increased its ductility in UCS and direct tensile strength(DTS) tests. Varying contents of major mineral compositions in granite showed minor influence on the number of cracks in both UCS and DTS tests.

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

基于MATLAB的中药炮制色质客观化辨识技术研究

中药炮制过程中中药色质与成分含量、药材质量之间存在一定的关系,提出一种基于MATLAB图像处理的中药炮制色质的客观化辨识技术与鉴别方法。首先,利用MATLAB对大量选定的中药图片进行图像分割,提取药材的RGB、HSX、Lab等特征值,并通过GUI界面显示,最终实现中药材炮制程度和药材分类和质量鉴别的目的。研究显示,利用图像处理技术能够快速进行中药特征提取与显示,对不同炮制方式的姜类有很好的区别。结果表明,利用图像处理技术与色度值能够有效提高中药材炮制色质辨识的效率和直观性,为中医药现代化发展提供技术支持。

316L不锈钢电解抛光的MATLAB图像定量分析

基于机器视觉的不锈钢表面无损检测方法正受到工业界关注并得到研究人员的探索与开发。本研究使用MATLAB对电解抛光的316L不锈钢表面金相图进行图像二值化处理,保留区域点蚀和过抛腐蚀区的黑色像素点和黑色区域,除去浅色划痕和不明显的凹凸区域。图像腐蚀处理选取半径为3个像素点的圆形结构元素为参数,去除图像噪声,增强图像黑色像素区域边界和对比度。选取图像的黑色像素占比和点蚀区域数量作为特征变量,并通过对特征变量进行测量和计数来评估抛光效果。在此基础上,借助MATLABGUI编程平台开发了一个基于图像定量分析过程的抛光效果评估程序,应用该程序可快速获得316L不锈钢电解抛光材料点蚀定量数据,使金相图片分析数值化,弥补主观判断的不足。该方法得到的结果与基于探针式粗糙度测量的优化结果一致。

基于MATLAB的数字信号处理综合课程实验

数字信号处理是一门理论与实践结合性很强的课程,结合遥感科学与技术专业的特点以及实际应用,设计了一个基于MATLAB的数字信号处理综合课程实验。该实验以GRACE卫星Level 2数据为研究对象,通过信号频谱分析、滤波器设计和处理等操作,从而实现高精度快速减小数据条纹噪声。该综合实验覆盖了数字信号处理课程的主要关键知识点,有利于学生形成较完整的数字信号处理知识体系,培养了学生将抽象的理论知识应用于实际遥感处理的能力。

Application of PCA Numalgorithm in Remote Sensing Image Processing

A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.

基于MATLAB GUI的食品取向度检测系统的建立和验证

具有取向结构的食品在自然界中普遍存在,而食品取向度对食品本身的质构特性和消费者的喜爱度有重大影响。为了定量表征食品取向度,本研究基于MATLAB GUI构建了一种食品取向度检测系统。借助已建立的激光传输成像装置,使用相机捕获样品上的激光散斑,运用MATLAB GUI进行食品取向度检测系统的编程设计,将激光散斑图像经过一系列的计算机视觉操作处理成椭圆拟合图,并计算样品取向度。然后以不同取向程度的面团为例对该系统进行了验证。实验结果表明,检测系统对激光散斑图像识别准确率为96.33%,实际运用效果良好。测得的不同面团取向度排序和显微结构图结果一致。此外,面团的取向度和面筋蛋白横纵长度比之间存在高强度的正相关关系(R=0.99,P<0.05)。建立的食品取向度检测系统具有良好的准确性,能够有效应用于食品取向度的检测。

Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

Matlab对牛顿环半径实验的多种数据处理方法分析及可视化

在“大学物理实验”中,数据的处理尤为关键。本文以光的等厚干涉测量牛顿环曲率半径为例:首先采用Matlab对不同的实验数据处理方法进行模拟,比较逐差、最小二乘和加权平均三种数据处理方法和误差分析,并计算了不确定度,发现最小二乘法展现了最优的拟合结果,具有最小的误差;其次使用MATLAB成功生成了牛顿环的可视化图像,展示了不同光波长和牛顿环半径下的实验结果。比较数据处理方法优劣和实验可视化有助于直观理解和展示实验现象,为实验结果的分析提供了有效的辅助工具。

基于FastAPI的MATLAB函数远程加载与实现

针对利用MATLAB官方提供的Web组件将在线虚拟仿真实验平台打包部署到Web端后,客户端出现浏览器端加载应用程序缓慢的问题,文章不使用MATLAB官方的Web组件,在使用超文本标记语言(HyperText Markup Language,HTML)和Vue搭建前端界面的同时,使用异步Web框架FastAPI搭建后端应用程序接口(Application Program Interface,API)并进行服务部署。开展数字信号处理中的图像处理实验,结果表明文章提出的方法可有效地解决Web应用加载速度缓慢等问题。

Matplotlib和Matlab在大学物理实验数据分析中的对比应用

在物理实验领域,对实验数据的精致且严格处理构成了实验研究的基石。尤其在对数据处理的精确性和作图质量有着极高要求的复杂物理实验中,借助专业的数据处理软件以执行数据解析、图像展现以及曲线拟合等任务显得尤为关键。首先,以弗兰克赫兹实验为案例,实现了运用Matplotlib与Matlab两款软件对实验数据进行直观且高效的可视化处理,发现在本实验中,Matlab软件做出的图像更加精细且代码更简单;其次,以铁磁材料的基本磁化曲线及其磁滞回线为例,进一步揭示了这两款软件在图像拟合方面的卓越性能,发现Matlab的拟合能力更强,可供选择的拟合方式更多;但Matlab更适用于简单绘制,Matplotlib适用于精细地控制。将Matplotlib与Matlab融入大学物理实验教学,不仅能够激发学生的创新意识,而且有助于培育其科学研究素养,更为他们未来投身科研工作奠定坚实的基础。

A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.

基于MATLAB的红外辐射特性与扫描成像仿真实验教学设计

光电成像原理与实验是应用型本科院校光电信息专业开设的必修实验课程,根据该课程的教学效果反馈,学生普遍认为红外辐射理论复杂且抽象,实验预习效果差,实验原理理解不透彻,实验总结报告抄袭严重等问题,为了解决上述问题和提高课程的教学质量,提升学生的实践创新能力,该教学设计结合MATLAB语言构建虚拟仿真实验平台,革新传统的实验项目,设计内容主要包括红外辐射特性中普朗克定律、计算普朗克常数、维恩位移定律和红外扫描成像的仿真实验,该教学设计将复杂的理论知识形象化,激发学生的学习兴趣和实践动手能力。

基于MATLAB的数字信号处理课程实验教学平台设计

随着计算机应用技术的不断发展,各种仿真工具被越来越多地使用在课程的理论和实践教学过程中,数字信号处理课程由于其理论性强、数学公式多、概念抽象导致其传统理论课堂教学效果不佳,学生畏难情绪严重,基于MATLAB界面设计工具可以将数字信号处理课程中的公式和概念,以更直观形象的方式通过图形和动态演示的过程展示出来,本课程实验教学平台基于MATLAB的GUI界面设计按照章节将实验内容组织在一起,实现主界面和子界面间的互相切换。本设计可以帮助教师提高教学效果,帮助学生更好地掌握课程知识,提升学习的兴趣和动力。