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

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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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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...

引用

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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Research on image Edge Detection Method Based on Wolf King Algorithm 7

Research on Image Edge Detection Method Based on Wolf King A...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Jian, Chang Yimeng, Fan School of Software Liaoning Technical University Huludao125105 China

ISBN: (纸本)9798350350760

Aiming at the problem that the current image edge detection algorithm is not accurate enough to capture the edge contour, an image edge detection algorithm based on Wolf king algorithm was proposed. Firstly, local principal component analysis and generalized Gaussian prior were used to improve the Gaussian filter, so that the image noise was effectively suppressed and the detail information was significantly enhanced. Secondly, multi-direction gradient calculation is proposed to reduce the influence of noise on gradient estimation and provide more information to locate the edge, so as to determine the position and direction of the edge more accurately. Finally, the Wolf king algorithm proposed in this paper is based on the grey Wolf algorithm, which is improved by using the nonlinear convergence factor and adaptive weight strategy. The improved algorithm is used to optimize Otsu to obtain the image threshold, realize the automatic acquisition of the image high and low threshold, and further improve the accuracy of the algorithm. More detailed edge contour information can be extracted from the image processed by the above method. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the images processed by the LPG-PCA image denoising algorithm are increased by 18.8% and 13.6% respectively compared with the traditional Gaussian filtering. In terms of threshold processing, compared with most existing edge detection algorithms, the Wolf king algorithm proposed in this paper can accurately detect the edge contour, and it is more coherent in edge extraction, with better unilateral response effect and better algorithm performance. ©2024 IEEE.

关键词： Wiener filtering

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Hand Recognition Solution as conference Room Intelligent Lighting Control Technique 7

Hand Recognition Solution as Conference Room Intelligent Lig...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Karail, Abdulvahit Bocekci, Veysel Gokhan Kiyak, Ismail Institute of Graduate Studies in Pure and Applied Sciences Marmara University Electrical and Electronics Engineering Istanbul Turkey Marmara University Faculty of Technology Department of Electrical and Electronics Engineering Istanbul Turkey

ISBN: (纸本)9798350342154

In communication technologies, it can be solved in systems that are extremely simple, detailed, among the tools for use in the electronic environment, internet-connected, development and integrated systems. With the rapid development of technology, smart led lamps, smart office, smart conference room lighting, road lighting fixtures and therefore smart lighting systems have been the reason why smart lighting systems can enter daily life rapidly. Sensors which are the components of smart lighting systems, feedback from users, cloud services, smart algorithms become more widespread with artificial intelligence in an easier, faster, and more economical way, causing a change in traditional lighting control and improvements in energy saving, functionality, and user centered lighting. In this study, the mediapipe framework is intended to be used for office conference room. MediaPipe is indeed an open-source framework that enables the processing of computer vision data, including video and audio. The lighting system was built using the artificial intelligence supported mediapipe hand recognition solution, and artificial intelligence and image processing algorithms were used. Each hand gesture corresponds to a different command to control the light. There are 3 main menus are determined for the hand and defined to the system. Main menus can be reached with every hand movement. After entering the main menu, intermediate menus are processed. Lighting application can be realized through control commands obtained by hand movements, digital/analog information conversion and Dali control system. It is concluded that the ease of control brought by this method is more advantageous in terms of its purpose than other methods because it is contactless and does not require any hardware device in the hands of the presenter. © 2023 IEEE.

关键词： image processing

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Algorithm of Palm Vein image Selection for Biometric Authentication systems Based on Multidimensional Markov Chain 25

Algorithm of Palm Vein Image Selection for Biometric Authent...

引用

25th International conference on Digital Signal processing and its Applications, DSPA 2023

作者： Kharina, Natalya Zemtsov, Anton Chernyadyev, Sergei Vyatka State University Department of the Radio Electronic Means Kirov Russia Institute of Mathematics and Information Systems Vyatka State University Kirov Russia

ISBN: (纸本)9798350337235

Biometric palm vein authentication becomes more popular in the industry. This paper proposes an algorithm for palm vein image selection. The main idea of this method is an approximation of biometric template image through discrete Markov process and applying the theory of conditional Markov process to develop the algorithm, which allows to fully utilize the statistic redundancy of the biometric template. Given method can be effectively used after performing a thresholding process on an image, using the dynamic threshold value. CASIA MS Palmprint V1 Database dataset was used to present the examples of algorithm implementation. Morphological dilation and Zhang-Suen algorithms were used to evaluate and compare the effectiveness of the proposed palm vein image thinning algorithm. The results showed that the proposed method makes it possible to compute an image that preserves the vein thickness and closely matches the real vein pattern and is effective when using a template matching algorithm based on perceptual hash functions. © 2023 IEEE.

关键词： Biometrics

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Real-time image Edge Detection System Design and algorithms for Artificial Intelligence FPGAs 2

Real-time Image Edge Detection System Design and Algorithms ...

引用

2nd IEEE International conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Ye, Caizeng Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350334104

Extraction of image edges is the basis of image processing class algorithms, for image edge extraction is of great significance. Nowadays, video image data is greatly increases the amount of image data and the processing difficulty. FPGA technology stands out for its parallel processing method and pipeline structure, which enables high-speed processing of large amounts of image data, and its high processing speed ensures real-time image data processing. The application of FPGA technology in the field of image data processing will be unstoppable. The main objective of this paper is to investigate the design and algorithms of a real-time image edge detection system using artificial intelligence and FPGAs. Median filtering and edge detection algorithms in video acquisition and pre-processing systems are an important part of complex algorithms in the field of target recognition and tracking, and the quality of image pre-processing plays an important role in the high accuracy of subsequent algorithms. This paper presents an in-depth study and analysis of the hardware implementation of the HD video capture and pre-processing system, combining the characteristics of FPGA parallel high-speed and pipeline structure, and completes the system design and verification on the hardware platform, fully proving the feasibility and correctness of the system. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Novel image Captioning Approach Using CNN and MLP 1

引用

5th International conference on Data and Information Sciences, ICDIS 2023

作者： Sharma, Swati Tomar, Vivek Yadav, Neha Aggarwal, Mukul KIET Group of Institutions Ghaziabad India Dehradun India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Computer vision and natural language processing researchers have dedicated significant time and energy to the problem of automatically creating image descriptions. In this work, we propose an artificially intelligent picture captioner built on a hybrid architecture of convolutional neural networks (CNNs) and multilayer perceptron (MLPs). This system will take photographs as input and generate captions based on those images using algorithms from convolutional neural networks (CNN), multilayer perception (MLP), recurrent neural networks (RNN). To generate a natural language caption, CNN first extracts feature from the input image, and then the MLP analyses these features. The proposed model is trained on a dataset of photos with captions by optimizing the parameters of a convolutional neural network (CNN) and a multilayer perceptron (MLP) via a hybrid of supervised learning and reinforcement learning. Our results demonstrate that the suggested model may produce captions that are on par with the best methods currently available in terms of accuracy and variety. The artificially intelligent picture captioner has potential uses in many areas, such as social networking, e-commerce, and image retrieval systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recurrent neural networks

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Enhanced Text-to-image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Downhole image Reconstruction in Oil and Gas Drilling through Low Rank Sparse Models 3

Enhancing Downhole Image Reconstruction in Oil and Gas Drill...

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3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Zhou, Yixi Li, Lin Xue, Chaoyang Ji, Kexin School of Electronic Engineering Xi'an Shiyou University Xi'an710065 China

ISBN: (纸本)9798350331387

para>Effective subsurface monitoring is vital in oil drilling. This study emphasizes the need for an algorithm to streamline video transmission from underground to the wellsite, overcoming bandwidth and interference challenges for high-quality transmission. It employs low-rank and sparse models to enhance image quality. Comparing three image processing algorithms, the Structured Low Dimensional Representation (SLDR) algorithm stands out. The research aims to improve subsurface image accuracy, ensuring underground pipeline system safety and integrity. © 2023 IEEE.

关键词： image reconstruction

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