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

共 15616 条记录，以下是221-230 订阅

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Research on the Application of Improved Genetic Algorithm in the Design of Physical Education Teaching systems 2

Research on the Application of Improved Genetic Algorithm in...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Zhang, Xue Liaoning Fushun113122 China

ISBN: (纸本)9798350360240

In colleges and universities in China, physical education is a crucial public fundamental subject. Traditional instruction often takes place during the designated class period in the training program. The instructor imparts to the pupils the principles of physical education so that they may become proficient in sports and ultimately raise their physical and athletic abilities. To lessen the issue of the basic genetic algorithm for intelligent grouping systems' delayed convergence and partial convergence, an enhanced genetic algorithm is suggested. By consistently rejecting comparable candidates, the algorithm may quickly expand the search field and guarantee the stability and diversity of the group. As a result, this work suggests a novel approach to intelligent grouping that is based on an enhanced genetic algorithm. Compared to the conventional algorithm, the novel approach is more effective and straightforward in resolving the issue of the algorithm's early convergence. Numerous tests have demonstrated how effectively the suggested algorithm satisfies every need for physical education. Test papers with a moderate level of difficulty and a sensible structure can be automatically generated by the genetic algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Pneumonia Classification using deep learning: A comparative study 8

Pneumonia Classification using deep learning: A comparative ...

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8th IEEE International conference on image and Signal processing and their Applications, ISPA 2024

作者： Tiar, Mohamed Lakhdar Terki, Nadjiba Dominguez-Jimenez, Juan Jose University of Mohamed Khider Biskra IL3CUB Laboratory Algeria University of Mohamed Khider Biskra Electrical Engineering Department Algeria University of Cadiz Escuela Superior de Ingenieria Cadiz Dept. Computer Languages and Systems Spain

ISBN: (纸本)9798350309249

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders. © 2024 IEEE.

关键词： Classification (of information)

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Smart and Quick Parking Spot Detection 15

Smart and Quick Parking Spot Detection

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Veeramalla, Santhosh Kumar Gali, Rama Lakshmi Ranjeeth, M. Tara, Saikumar Hindumathi, V. Bhargava Kumar, L. Bvrit Hyderabad College of Engineering for Women Department of Ece Hyderabad India

ISBN: (纸本)9798350370249

The rapid expansion of urban areas has intensified the challenge of finding parking spaces for drivers. Intelligent parking systems emerge as a crucial solution by providing real-time detection of available spaces. While various methods exist, from basic magnetic sensors to advanced computer vision algorithms, their effectiveness can be hindered by environmental factors. Recognizing the importance of automated systems, this paper focuses on implementing an image processing approach for parking space detection. By strategically placing cameras in parking lots, the system identifies vacant spaces and transforms images to determine availability. Integrating these techniques into mobile applications could further aid drivers in locating parking spots, improving their overall experience. Ultimately, accurate and efficient parking space identification is vital for reducing congestion, enhancing air quality, and improving urban environments. © 2024 IEEE.

关键词： CCTV Computer vision image processing Parking availability Parking guidance Smart parking Sustainable cities

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An Efficient Implementation of the Threshold-based VNG Demosaicing with Reduced Calculations

An Efficient Implementation of the Threshold-based VNG Demos...

引用

2024 Smart systems Integration conference and Exhibition

作者： Alnaser, Yousef Ravichandran, Anbumani Langer, Jan Fraunhofer ENAS Chemnitz Germany TU Chemnitz Chemnitz Germany

ISBN: (纸本)9798350388787;9798350388770

Color Filter Arrays (CFA) are essential components of digital cameras and image sensors to capture the color information needed to produce full-color images from only a single image sensor per pixel. Many methods and algorithms have been proposed to recover the missing color information of CFAs. In this work, we use a simplified version of the Theshold-based Variable Number of Gradients algorithm proposed by Chang et al. to estimate the full-color information from Bayer images. We also show that the slight modification to algorithm does not effect images quality while making it more compatible with hardware. We propose an efficient implementation of the algorithm that reduces the number of calculations per pixel at the cost of increased memory resources. Our implementation targets an image processing pipeline in an FPGA platform which is short on LUTs and FF resources but has DSPs and BRAMs to spare. We buffer the absolute differences and average color components to be shared and re-used between neighboring pixels, on two levels: within the same row, and between different rows. The latter strategy reduces the number of absolute differences calculated every cycle from 32 to 4 and average color components from 32 to 6. However, the memory requirements are increased from storing 4 image rows to 18 image rows. We implement the solutions on an FPGA using high-level synthesis (HLS) and optimize it to further reduce resources.

关键词： Demosaicing Variable Number of Gradients Bayer image FPGA

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Retrofitting a Legacy Cutlery Washing Machine Using Computer vision 16th

Retrofitting a Legacy Cutlery Washing Machine Using Computer...

引用

16th International conference on Computational Collective Intelligence (ICCCI)

作者： Fwa, Hua Leong Singapore Management Univ 81 Victoria St Singapore 188065 Singapore

ISBN: (纸本)9783031702587;9783031702594

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.

关键词： Industry 4.0 Computer vision Deep Learning image processing

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Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution 37

Multi-Feature Aggregation in Diffusion Models for Enhanced F...

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37th SIBGRAPI conference on Graphics, Patterns and images (SIBGRAPI)

作者： dos Santos, Marcelo Laroca, Rayson Ribeiro, Rafael O. Nieve, Joao C. Menotti, David Univ Fed Parana Curitiba Parana Brazil Pontificia Univ Catolica Parana Curitiba Parana Brazil Brazilian Fed Police Brasilia DF Brazil Univ Beira Interior Covilha Portugal

ISBN: (纸本)9798350376043;9798350376036

Super-resolution algorithms often struggle with images from surveillance environments due to adverse conditions such as unknown degradation, variations in pose, irregular illumination, and occlusions. However, acquiring multiple images, even of low quality, is possible with surveillance cameras. In this work, we develop an algorithm based on diffusion models that utilize a low-resolution image combined with features extracted from multiple low-quality images to generate a super-resolved image while minimizing distortions in the individual's identity. Unlike other algorithms, our approach recovers facial features without explicitly providing attribute information or without the need to calculate a gradient of a function during the reconstruction process. To the best of our knowledge, this is the first time multi-features combined with low-resolution images are used as conditioners to generate more reliable super-resolution images using stochastic differential equations. The FFHQ dataset was employed for training, resulting in state-of-the-art performance in facial recognition and verification metrics when evaluated on the CelebA and Quis-Campi datasets. Our code is publicly available at https://***/marcelowds/fasr.

关键词： Stochastic systems

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image Recognition and Position Technology Based on Super-pixel Fuzzy C-Means Clustering in Industrial Assembly System 3

Image Recognition and Position Technology Based on Super-pix...

引用

3rd International conference on image processing and Intelligent Control, IPIC 2023

作者： Yuan, Hailiang Sun, Weitao Wang, Hailing Tianjin Vocational College of Mechanics and Electricity Tianjin China Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781510668140

Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems. © 2023 SPIE.

关键词： image recognition

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APPLICATION OF UNSUPERviSED LEARNING AND image processing INTO CLASSIFICATION OF DESIGNS TO BE FABRICATED WITH ADDITIVE OR TRADITIONAL MANUFACTURING 24

APPLICATION OF UNSUPERVISED LEARNING AND IMAGE PROCESSING IN...

引用

24th International conference on Engineering Design, ICED 2023

作者： Ördek, Baris Borgianni, Yuri Free University of Bozen-Bolzano Italy

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Camera Movement Cancellation in video Using Phase Congruency and an FFT-Based Technique 11th

Camera Movement Cancellation in Video Using Phase Congruency...

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11st World conference on Information systems and Technologies (WorldCIST)

作者： Gharahbagh, Abdorreza Alavi Hajihashemi, Vahid Machado, J. J. M. Tavares, Joao Manuel R. S. Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal Univ Porto Dept Engn Mecan Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal

ISBN: (纸本)9783031456503;9783031456510

One of the interesting fields in video processing is motion detection and human action detection (HAR) in video. In some applications where both objects in the scene and the camera may be moving, camera movement cancellation is very important to increase accuracy in extracting motion features. HAR systems usually use image matching/registration algorithms to remove the camera movement. In these methods, the source (fixed) image frame is compared with moved image frame, and the best match is determined geometrically. In video processing, due to the existence of a set of frames, one can correct errors using previous data, but at the same time, it is needed a fast frame registration algorithm. According to the above explanations, this article proposes a method to detect and minimize camera movement in video using phase information. In addition to having the acceptable speed and the ability to be implemented online, the proposed method, by combining texture and phase congruency (PC), can significantly increase the accuracy of detecting the objects in the scene. The proposed method was implemented on a HAR dataset, which includes camera movement, and its ability to compensate for camera motion and pre-serve object motion was verified. Finally, the speed and accuracy of the proposed method were compared with a number of the latest image registration methods, and its efficiency in terms of camera movement cancellation and execution time is discussed.

关键词： Camera Movement Cancellation Phase Congruency (PC) video image Registration Fast Fourier transform (FFT)

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Automatic Number Plate Recognition System Using Deep Learning algorithms and image processing for Surveillance 9

Automatic Number Plate Recognition System Using Deep Learnin...

引用

9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Jeba Malar, A. Christy Deepika, S. Jayasree, S. Keerthana, G. Sri Krishna College of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350365092

The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by their shape, bulk, and color in different countries. Number plates must adhere to rules in India, where a light hue serves as the background and a black color serves as the foreground. This paper recommended a new technique to locate the number plate on a vehicle by using histogram equalization and then identifying the alphabets and digits on the plates using character segmentation using Sobel and Canny edge detection image processing technique. © 2024 IEEE.

关键词： Deep learning

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