Measuring the Condition of Parking Lot by Image Processing

Aim To study the parking management in the condition of vehicles' increasing. Methods The methods of pattern recognition and image processing were used to analyze the eigenvalues of parking lot images. Results The automatic identification of every parking place in the parking plot was realized. The automatic measuring of parked vehicle count and parking lot utilization was completed. Conclusion It can complete the real time recognition, and has some practicabilities.

Safer Design and Less Cost Operation for Low-Traffic Long-Road Illumination Using Control System Based on Pattern Recognition Technique

The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.

A New Image Processing Algorithm for Log Cross Section Image

With characteristics of log cross-section image taken into consideration,this paper presents a new image processing algorithm for recognization and measurement of log cross sections,by which the number and area of quasi-circular log cross sections can be calculated automatically,thereby obtaining the total cross-section area and log volume.

Microcomputer System for Automatic Identification of the Cryptococcus neoformans and Its Clinical Application

In this study,microcomputer image processing and pattern recognition technology,and the knowledge of morphology and optical characteristics of Cryptococcus neoformans were used for identification of Cryptococcus neoformans.Four groups of mice were lethally infected with standard strain,Wuhan strain,American B-2643 strain and Var.Shanghainesis of the Cryptococcus neoformans.The samplescollected included mice brain,lung,kidney,liver,small intestine tissue and were observed under a light microscope.More than 600 images of the fungus were input into a microcomputer.A system of computer for automatic identification of the Cryptoccocus neoformans was developed. The technique involved image preprocessing,imagesegmenting,coding of line-length on the edge,curve fitting,extracting of image feature,building of image library and feature data bank etc..And then,768 images of the clinical samples and other fungus samples whose morphological features tend to be confused with Cryptococcus neoformans were input into microcomputer and subjected to automatic identification.The Cryptococcus neoformans was accurately identified within 15 min,and the consistence rate with results of routine culture was 98%.

Development and implementation of an automated system to aid laboratory diagnosis using image processing

The objective of this work is to provide an automatic system to count white blood cells in a blood smear. To do so an experiment was assembled, composed by a standard microscope with two step motors coupled to its knobs in order to move the microscope in x and y directions and a web cam which was mounted in the top of the microscope responsible for to acquire images from the smear. The step motors and the web cam are controlled by a microcomputer PC standard via software developed inDelphi. The motors use the parallel port to communicate with the PC and the camera use the USB port. The main idea is to set an initial point into the smear and the automated system will carry over the smear acquiring images (frames with 640 × 480 pixels) and counting the white blood cells encountered. The double histogram threshold technique is implemented to initially exclude the red cells from the image leaving only the white ones. Preliminaries results are obtained and show that the system is quite fast and has a good capacity of selection, even when different kinds of smear are used.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

RESEARCH ON FACE RECOGNITION BASED ON IMED AND 2DPCA

This letter proposes an effective method for recognizing face images by combining two-Dimen- sional Principal Component Analysis (2DPCA) with IMage Euclidean Distance (IMED) method. The proposed method is comprised of four main stages. The first stage uses the wavelet decomposition to extract low fre- quency subimages from original face images and omits the other three subimages. The second stage concerns the application of IMED to face images. In the third stage, 2DPCA is employed to extract the face features from the processed results in the second stage. Finally, Support Vector Machine (SVM) is applied to classify the extracted face features. Experimental results on the AR face image database show that the proposed method yields better recognition performance in comparison with the 2DPCA method that is not combined with IMED.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

A METHOD ANALYSING PHOTOCCLUSAL IMAGE WITH COMPUTER

Depending on the techniques of pattern recognition and image processing, we established a computer analytic system for photocclusal image. The analysing results made by this system are more accurate and reliable than those by the naked eye and grid for analysing photocclusal image. We analysed photocclusal images for a patient with prematurity of lower first right molar be fore and after occlusal adjustment with the system. The result appeared that occlusal adjustment mainly brought about distributive variation of occlusal stress rather than alteration of absolute value of overall occlusal force.

Continuous Arabic Sign Language Recognition in User Dependent Mode

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.

Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

基于改进YOLOv5的火车连接钩舌识别方法

为了准确识别不同类型钩舌,确保自动复钩机器人能够根据火车连接钩舌状态实时调整机械臂的位姿,提出了一种基于改进YOLOv5的火车连接钩舌识别方法。首先,将YOLOv5主干网络中原有的C3模块替换为梯度流丰富的C2F模块(cross feature module),YOLOv5颈部网络中原有的C3模块替换为基于FasterNet模块构建的轻量化C3_FasterNet模块,并将CoordConv模块嵌入到YOLOv5的主干网络末端。其次,基于现场实测的火车连接钩舌图像进行了识别测试。结果表明:改进的YOLOv5算法在降低模型参数量的同时,可以有效提升对钩舌目标的检测精度,火车钩舌识别精度达到了98.7%,相较于原始算法,模型参数量减少了10.8%。研究结果为复钩机器人在执行钩舌复位和车厢连接操作方面提供了一种有效的解决方案。

DPT‐tracker:Dual pooling transformer for efficient visual tracking

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input images.To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions.MCPT aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction.DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking methods.Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

基于STM32的跟踪小车设计

文章以STM32系列单片机为主控芯片,进行了STM32单片机的内部配置,通过上位机的摄像头对视频数据进行采集,之后利用图像处理技术依据物体的颜色进行匹配,当颜色达到要求则说明有很大的可能性是目标物体,则摄像头就会对其进行锁定识别并自动跟踪。下位机中用串口将数据发送到电脑进行调试,上位机将调试完毕的数据作为控制数据继而对电机的转速和转向进行控制,从而达到对目标识别之后上位机对下位机进行指令发送,实现自动跟踪的效果。经过实验,小车能够根据颜色对目标物体进行识别并跟踪。

图像处理与模式识别技术在系统监控和异常检测中的应用

阐述图像处理与模式识别技术在电力系统监控中的实时异常检测与处理。通过对电力系统监控图像的分析和处理,利用图像处理和模式识别算法,实现对电力系统设备的异常检测和故障诊断。

基于多关联模板匹配的人脸检测

提出一种基于多关联模板匹配的人脸检测算法 .模板由一系列关联的双眼模板和人脸模板组成 ,它们都是通过仿射变换根据伸缩比和姿态 (即旋转角度 )从单一平均脸模板产生出来的 .首先 ,使用双眼模板搜索候选人脸 ,再用人脸模板匹配进一步筛选候选人脸 ,最后 ,通过启发式规则验证是否是人脸 .对于各种类型的图像进行大量实验的结果表明 ,该算法对于正面包括多角度人脸的检测很有效 .

基于图像识别的小麦品种分类研究

基于数字图像分析,利用小麦籽粒的20个形态特征和12个颜色特征对来自中国4个地点7个春小麦品种共28个样本进行分类和识别。对于不同品种和地区的样本,分别利用逐步判别分析,选取显著性较大的特征参量,建立各地区和品种的贝叶斯分类器模型。结果表明,对各地区品种识别的正确回判率和测试集的正确识别率均达到100%。将各样本按品种合并,再对合并后的样本进行品种识别,除了新克旱9号的回判率为98.3%外,其它品种的回判率均为100%。测试集中,龙麦26和青春566正确识别率分别为97.5%和95.0%,其它品种均为100%。品种来源地识别也能达到较高的水平,甘肃、宁夏、新疆和黑龙江的正确识别率分别为88.6%、92.9%、72.9%和95.7%。说明利用籽粒图像对小麦品种进行识别高效可行。

自动指纹识别技术的发展与应用

自动指纹识别技术是利用人类指纹的唯一性 ,通过对指纹图案的采样、特征信息提取并与库存样本相比较的过程来实现身份识别的技术 .与帐号 +密码、IC卡等传统的身份识别手段相比 ,自动指纹识别技术具有不会丢失、不会遗忘、唯一性、不变性、防伪性能好和使用方便等优点 ,已经逐步在门禁、考勤、金融、公共安全和电子商务等领域得到应用 .介绍了自动指纹识别技术的研究现状和指纹特征定义和提取、指纹比对等主要研究内容 ,分析了该技术面临的主要困难 :指纹采集、指纹分类和缺乏相应的系统性能评价体系 ,提出了该技术的发展方向为非接触式复皮层指纹采集、兼容主流采集设备的识别芯片的开发和多种生物识别技术的融合 。

基于傅立叶描述子的步态识别

提出了基于傅立叶描述子的步态识别方法。用背景差方法得到运动人体的轮廓,通过步态周期分析提取步态序列的关键帧。利用傅立叶描述子处理关键帧的轮廓线序列,并进行数据维数压缩,得到匹配模板。用最近邻法进行分类和识别。应用上述方法在Soton步态数据库上进行了实验。结果表明所提的步态识别方法具有较高的识别性能。