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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

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Design of AXI Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

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12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATivE EVALUATION OF image AND POINT CLOUD BASED METHODS

SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATI...

引用

IEEE International conference on image processing (ICIP)

作者： Katrolia, Jigyasa Singh Kraemer, Lars Rambach, Jason Mirbach, Bruno Stricker, Didier DFKI Kaiserslautern Germany Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665441155

The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.

关键词： scene segmentation depth image point cloud

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Design of an Improved Dish Recognition Algorithm Based on image Retrieval

Design of an Improved Dish Recognition Algorithm Based on Im...

引用

Machine Vision, image processing and Imaging Technology (MVIPIT), International conference on

作者： Yi Yu Jie Liu Archival Sebial Angie Ceniza Canillo Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification methods. This breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly red

关键词： Industries Deep learning Training image recognition Costs Accuracy Face recognition image retrieval Feature extraction Classification algorithms

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Enhancing Structural Health Monitoring: AI-Driven image processing for Automated Crack Identification in Concrete Surfaces

Enhancing Structural Health Monitoring: AI-Driven Image Proc...

引用

Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： S. Jency G. Ramkumar Department of ECE Saveetha School of Engineering SIMATS Chennai Technology Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

One of the first signs of structural deterioration is cracks in the concrete surface, which is important for maintenance because prolonged exposure will seriously harm the environment. The highly regarded method for inspecting cracks is manual inspection. During the handwritten inspection, the irregularities' conditions are noted and a hand-drawn illustrate of the crack is created. One of the main problems on large construction sites is the degradation of structures as a result of cracks, especially in cases where inspection by hand is not feasible. Thus, an alternative called automatic image-based crack detection is suggested. The primary goal of this research work is to use artificial intelligence-based image processing techniques to identify and categorize cracks. Here, this study proposes five modules. The first module collects the dataset of crack images; the second module applies a pre-processing method to filter the data; the third module divides the data into training and testing portions; and the fourth module implements a model that uses both deep learning, such as the Mobile-net algorithm, and machine learning, such as the Random Forest algorithm, for testing and training the data. The last part is the prediction module, which forecasts whether a crack image will be positive or negative. Lastly, this study compares the accuracy score, recall, precision, loss graph, F1 score value, and algorithms for machine learning along with deep learning. The system performed better, as demonstrated by the experimental results.

关键词： Deep learning Training Machine learning algorithms Accuracy image processing Roads Inspection

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Machine Learning Based Skin Disease Detection Using Semi-Automatic image Segmentation

Machine Learning Based Skin Disease Detection Using Semi-Aut...

引用

Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： J. Sathya A. Kalaivani Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The human skin is a remarkable structure, susceptible to numerous known and unknown diseases. Thus, diagnosing skin conditions is one of the most uncertain and complex areas in medical science, making clinical image analysis for skin diseases a formidable task. Moreover, diagnosing skin conditions manually by medical professionals is not only time-consuming but also subjective. This paper presents an approach for diagnosing and categorizing skin diseases utilizing machine learning classification techniques. This approach is based on a popular Semi-automatic image segmentation algorithm known as Interactive Superpixel Segmentation. It combines manual user input with automated algorithms to partition images into meaningful regions. Morphological operations are used in this system to remove hair in image processing. Median filtering technique is employed for noise removal. Computer-based methods will be employed to analyze, process, and classify image data, taking into account different attributes of the photos under examination. To start with, skin photographs go through filtering to eliminate unwanted noise, followed by processing to enhance the overall quality of the image. Gray-Level Co-occurrence Matrix (GLCM) is implemented to extract the features from the skin disease images. Following feature extraction, the image can be classified using the K-Nearest Neighbors (KNN) algorithm, ultimately generating an evaluative report as the output. This paper summarizes the basic workflow for interactive superpixel segmentation in machine learning. Medical image dataset is taken from different websites to predict the skin diseases. This application is more efficient and producing more accuracy results comparing to others.

关键词： image segmentation Machine learning algorithms Machine learning Nearest neighbor methods Feature extraction Skin Classification algorithms

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image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology

Image Recognition and Analysis of Computer Interlock Interfa...

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Xuanmo Chen Qiang Li College of Computer Science and Technology Lanzhoujiaotong University Lanzhou China Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Province Lanzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (Optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces.

关键词： Visualization Technological innovation image recognition Machine learning algorithms Optical character recognition Software algorithms Clustering algorithms

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Single image Super-Resolution Based on Enhanced Residual Neural Network 14

Single Image Super-Resolution Based on Enhanced Residual Neu...

引用

14th International conference on Signal processing systems, ICSPS 2022

作者： Zhao, Jie Chen, Zhenxue Liu, Chengyun Yang, Yue Ye, Mengting Zhang, Yujiao The School of Control Science and Engineering Shandong University Jinan China Information Tech Co Ltd China Mobile Comm Corp Hangzhou China Shenzhen Research Institute Shandong University Shenzhen China

ISBN: (纸本)9798350336313

Vision is an important way for humans to perceive the world around them. It is an urgent need to improve image quality by using image super resolution. Residual network plays an important role in image super resolution because of its powerful feature extraction ability. Only stacking residual network is not enough to extract a large amount of effective information, and at the same time, more training memory is needed. Therefore, this paper designs a single feature extraction module with better performance to enhance the feature extraction ability of the network. In this paper, the Residual Network is mainly improved, and a Single image Super-Resolution Based on Enhanced Residual Neural Network (ERNN) is proposed based on improved residual module. Compared with other classical lightweight algorithms, the lightweight network with the new residual module as the basic unit has higher reconstruction ability, and can achieve better image super-resolution reconstruction with a small number of parameters. Moreover, a large number of experiments have verified that the proposed ERNN can achieve the trade-off between the number of parameters and performance. © 2022 IEEE.

关键词： Feature extraction

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Research on image brightness enhancement algorithm based on deep learning 24

Research on image brightness enhancement algorithm based on ...

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Siqi He Jianping Xing Yu Bai Shuzhen Huang Kaiping Sun Peng Sun Shandong University Jinan Shandong Province China Shandong Vocational and Technical University of Engineering Jinan Shandong Province China

ISBN: (纸本)9798400713880

In view of traditional image improvement technology's limitations in high real-time application environments, a new low-light video image improvement algorithm is proposed in this article. In consideration of traditional improvement algorithms' faults, and under the precondition of studying low-light improvement techniques in image signal processing (ISP) systems, deep learning frameworks are introduced to break through complex structure and long processing pipeline faults in traditional ISP systems. In this article, a lightweight image brightness improvement algorithm with a basis in convolutional neural network (CNN) is proposed. In this algorithm, feature extraction of RGB images is performed through CNN to generate a brightness coefficient matrix, simplifying RGB to RGB direct mapping and reducing model numbers, model parameters, and computational complexity. This technology pays attention to generating coefficients of brightness, can effectively extract changing parts of brightness, maintain detail information, and conduct efficient and natural brightness improvement, and can apply to high real-time application environments such as autonomous driving.

关键词： Convolutional Neural Network

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Prediction of Most Confident Labelled Output for Fashion image using Deep Learning Model with Tensorflow

Prediction of Most Confident Labelled Output for Fashion Ima...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International conference on

作者： Saranya M S P. Geetha Dept. of DCSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of science and Technology Chennai India Dept. of DCSE CEG Anna University Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Fashion image classification is an emerging field of research. Examples of numerous e-commerce applications that rely on fashion image classification include recommendation systems, retrieval systems, and apparel recognition tasks. The extensive variety of clothing attributes, including designs, textures, and colours, and the high degree of clothing categorization present formidable challenges to the classification of clothing images. In this paper, a new algorithm called Most Confident Labelled Output (MCLO) algorithm has been proposed. Later, widely used optimization techniques in CNN models like SGD, Adam, AdaGrad and RMSProp are added for classifying the fashion images. Among the CNN optimizers, AdaGrad model achieved better accuracy as well as loss gets decreased. The proposed work was carried on benchmarking Fashion MNIST dataset The MCLO algorithm has been coded using tensorflow with graphics processing unit (GPU). The outcomes demonstrate that the proposed method outperforms state-of-the-art models, as assessed through the Fashion MNIST dataset benchmarks.

关键词： Computational modeling Clothing Graphics processing units Predictive models Benchmark testing Prediction algorithms Classification algorithms

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ALOS-2 disaster mapping processing and its implementation in an operational system 26

ALOS-2 disaster mapping processing and its implementation in...

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conference on Sensors, systems, and Next- Generation Satellites XXVI

作者： Ohki, Masato Takakura, Yuki Kawakita, Shiro Koyama, Christian Tadono, Takeo Japan Aerosp Explorat Agcy Earth Observat Res Ctr Ibaraki 3058505 Japan Satellite Applicat & Operat Ctr Chiyoda Ku Surugadai 4-6 Tokyo 1018008 Japan

ISBN: (纸本)9781510655324

Increasing human and economic losses from urban disasters demand synthetic aperture radar (SAR), which has all-weather, day-and-night observation capability. The Japan Aerospace Exploration Agency (JAXA) has been operating the Advanced Land Observing Satellite-2 (ALOS-2), carrying PALSAR-2, an L-band SAR, for monitoring disasters and environmental changes. We developed the first fully automated algorithms for detecting flood extents and earthquake-induced building damage from ALOS-2 data. The algorithms rapidly process ALOS-2 and ancillary data (flood simulation, hazard map, and other geographical information) and provide damage information less than an hour after the input data are entered. The validation results showed that the accuracy of the estimated flood extent was 60 to 94%, depending on the observation conditions, especially the incidence angle of ALOS-2 observation. The accuracy of the damaged building detection was 72% for buildings with a footprint larger than 200 m(2) in the area. We implemented these algorithms in an operational disaster response system. We launched a one-stop operation that automatically processes ALOS-2 data after emergency observation and provides the damage maps rapidly via email for disaster response workers.

关键词： PALSAR-2 space application image processing disaster management disaster monitoring flood building collapse

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