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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381-390 订阅

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Improved Self-Sparse Fuzzy Clustering Algorithm for image Segmentation

Improved Self-Sparse Fuzzy Clustering Algorithm for Image Se...

引用

2023 International conference on Communication, Circuits, and systems, IC3S 2023

作者： Nayak, Tunirani Bhoi, Nilamani Vssut Department of Electronics and Telecommunication Engg. Burla758018 India

ISBN: (纸本)9798350325904

The effectiveness of the FCM image segmentation algorithm can be increased by introducing local spatial information as FCM is very sensitive to noise. Also, during transmission, the image details are lost because noise is added. Thus, an Alpha-trimmed mean filter is utilized as a pre-processing step to maintain the image features and immunity. Then SSFCA algorithm is used to obtain a fuzzy membership with sparsity. It is used to reduce the non-homogeneous interference and got a good segmentation result in comparison to other self-optimized FCM algorithms. Again, to overcome the background noise the resultant image is applied to the morphological operators as a postprocessing step. Experiments show that this method can create good segmentations. © 2023 IEEE.

关键词： image segmentation

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Intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

引用

5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

引用

17th International conference on Interfaces and Human Computer Interaction 2023, IHCI 2023, the 17th International conference on Computer Graphics, visualization, Computer vision and image processing 2023, CGVCviP 2023 and 16th International conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). The second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. These databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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Object detection algorithm based on residual deformable convolution pyramid 4

Object detection algorithm based on residual deformable conv...

引用

4th International conference on Computer vision and Pattern Analysis, ICCPA 2024

作者： Huang, Bin Wang, Sanqian Li, Linjie Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang*** China School of Software Nanchang Hangkong University Nanchang*** China

ISBN: (纸本)9781510682528

Considering the challenges associated with robots in optoelectronic imaging applications, typically require real-time and accurate recognition and localization of targets, especially in complex environments. Due to the potential challenges of noise, blurring, or dense targets in the images obtained by optoelectronic imaging systems, traditional object detection algorithms may face issues such as semantic information loss and difficulty in detecting small targets. By introducing residual deformable convolution structures, feature representation can be enhanced in optoelectronic imaging systems, and the shape of the convolution kernel can be dynamically adjusted to adapt to the non rigid deformation of the target, thereby better capturing the detailed features of small targets. Experimental results exhibit that RAFPN enhances the mAP of object detection algorithms RetinaNet, Faster R-CNN, and GFL by 1.2%, 0.9%, and 1.1% respectively on the MS COCO dataset, effectively enhancing the performance of object detection algorithms in optoelectronic imaging. © 2024 SPIE.

关键词： Optoelectronic devices

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image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology 6

Image Recognition and Analysis of Computer Interlock Interfa...

引用

6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2024

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

ISBN: (纸本)9798350316520

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. © 2024 IEEE.

关键词： Clustering algorithms

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Precision Pistachios: Enhancing Agricultural Quality Control with Deep Learning 2

Precision Pistachios: Enhancing Agricultural Quality Control...

引用

2nd International conference on Intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Kaur, Arpanpreet Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Originating from the pistachio tree (Pistacia vera) and prized for their nutritional content and adaptability in the kitchen, pistachios have great financial worth in the agricultural field. Ensuring the quality and safety of pistachio nuts is absolutely vital and calls for effective industrial post-harvest methods. Modern technology, especially image processing and computer vision algorithms, are augmenting conventional approaches of pistachio categorization and separation. This work intends to build an enhanced classification model for pistachio identification using deep learning, most especially the VGG16 Convolutional Neural Network (CNN). After gathering a dataset of 2,148 high-resolution photos of pistachios, the approach uses resizing and data augmentation methods including rotation, flipping, and zooming to improve the generalization and prevent overfitting of the model. Pretrained on imageNet, the VGG16 model is fine-tuned to fit the particular job of pistachio recognition by means of feature extraction capacity. For feature extraction, the model architecture comprises convolutional layers;for classification, it features fully connected layers. With performance criteria including accuracy, precision, recall, and F1-score watched to guarantee efficient learning, optimization methods including gradient descent and backpropagation are utilized during training. On a test set, the model shows an extraordinary accuracy of 0.98, so highlighting the potential of deep learning models-especially VGG16-in automating and improving quality control systems in agricultural uses. With possible uses in related agricultural research, this highperformance classification model not only meets the demand for effective separation of pistachio species but also considerably increases their economic value.

关键词： Pistachio detection Convolutional Neural Networks (CNNs) VGG16 image processing Computer vision Deep learning Classification model Data augmentation Quality control Agricultural Feature extraction post-harvest processes Economic species

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MicroDeblur: image Motion Deblurring on Microcontroller-based vision systems 22

MicroDeblur: Image Motion Deblurring on Microcontroller-base...

引用

22nd ACM/IEEE International conference on Information processing in Sensor Networks (IPSN)

作者： Lee, Seulki UNIST Ulsan Natl Inst Sci & Technol Ulsan South Korea

ISBN: (纸本)9798400701184

This paper introduces MicroDeblur, an on-device image motion deblur solution for resource-constrained microcontroller-based vision systems. Although motion blurs caused by the movement or shake of the device (camera) are pervasive in embedded, IoT, and mobile devices, it has been considered a hard nut to crack for many microcontrollers with extremely-limited resources (e.g., hundreds of KB of RAM). To tackle this problem, we combine the DNN (deep neural network) motion deblur method with the classical motion deblur approach and take the best of both worlds, i.e., 1) powerful pattern recognition ability of DNNs and 2) simplicity and stability of matrix-based classical algorithms. To deblur an image, MicroDeblur takes three steps: 1) blur kernel estimation, 2) blur image transformation, and 3) iterative clear image restoration. We propose 1) depth-independent convolution that efficiently estimates the blur kernel (pattern) and 2) Toeplitz-based motion blur modeling that enhances the time and space complexity of the deblurring process by O(n) and O(n(3)), respectively, compared to the existing methods. To the best of our knowledge, MicroDeblur is the first self-sufficient blind deconvolution solution for a stand-alone microcontroller that does not rely on extra hardware or external systems. We implement MicroDeblur on an ARM Cortex-M4F, achieving a competitive quality of deblurred images using 187x and 429x smaller memory and energy, respectively, compared to high-end GPU-based solutions.

关键词： image deblurring blind deconvolution on-device deep neural networks microcontroller

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Various Approaches in Deep Learning for Medical Modality image Segmentation 1

Various Approaches in Deep Learning for Medical Modality Ima...

引用

1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Lavanya, K. vijay, Devi P

ISBN: (纸本)9798350348057

In computer vision and image processing the surveillance of video, virtual reality, robotic perception, and image compression are just a few of the uses for image segmentation. There are a plethora of segmentation algorithms out there right now. These new approaches to picture segmentation have been spurred on by the extensive victory of Deep Learning (DL). Semantic and instance segmentation literature is thoroughly reviewed, with particular attention paid to groundbreaking work in CNN models, architecture of encoder-decoder, recurrent networks, attention models, and GAN settings. This work is a comprehensive review of the most recent research in these fields. Using a wide range of datasets, we assess the performance of different deep learning-based segmentation representation, examine their strengths and drawbacks, and identify promising future avenues © 2023 IEEE.

关键词： Semantics

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Fuzzy Net for image processing Applications: image Segmentation 6th

Fuzzy Net for Image Processing Applications: Image Segmentat...

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6th International conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Alameen, Huda A. Kareem, Noor Riyadh Habeeb, Sundus Q. Department of Computer Science Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq Faculty of Medicine Jabir Ibn Hayyan Medical University Najaf Iraq

ISBN: (纸本)9789811995118

Since the invention of computers, image processing methods have been utilized in a variety of applications, where their significance has grown. An important topic and the main emphasis of image processing methods are image segmentation, which is also a classic topic in the area. In order to segment images, a number of general-purpose algorithms and methods have been created. Since the image segmentation problem does not have a generic solution, these strategies frequently need to be paired with domain expertise to successfully solve an image segmentation problem for a problem domain. In this paper, we present a comparative study of basic two types of fuzzy convergence with a midpoint as image segmentation techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Real-Time Media Synthesis from Speech: A New Era in Passenger Entertainment 3rd

Real-Time Media Synthesis from Speech: A New Era in Passen...

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3rd International conference on Cognitive Mobility, CogMob 2024

作者： Csippán, György Kovari, Bálint Bécsi, Tamás Leginusz, Laszló Asura Technologies Ltd. Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9783031817984

This research explores the development and implementation of a software application that utilizes live human speech to generate dynamic visual media, aiming to revolutionize smart passenger entertainment. In order to bridge the gap between spoken and visual content creation, this innovative approach introduces a novel method for media production. The software employs advanced voice recognition and synthesis algorithms to convert spoken words into visually engaging and animated representations. Key aspects and algorithms necessary for accurate analysis and interpretation are examined. Deep learning models are used to extract linguistic information from speech input, enabling real-time processing and interpretation. The extracted text is then transformed into moving media content using a modified image synthesis model, resulting in visually dynamic outputs from spoken input. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application programs

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