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

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Automatic Identification of the American Sign Language Using Machine Learning

Automatic Identification of the American Sign Language Using...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Ravi Kishore Kodali Arindam Pattanayak Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Communication is essential, providing a means to express thoughts, emotions, and ideas. Sign language is a fundamental form of communication for those who are deaf, have hearing impairments, or are non-verbal. This research uses machine learning to develop an effective algorithm for identifying the alphabet in American Sign Language (ASL) through natural hand movements. The system combines image processing, machine learning, and artificial intelligence, especially a convolutional neural network (CNN), to translate ASL gestures into accurate and understandable output. After training the model on a dataset that includes sign language images, the results demonstrate the system's ability to identify hand gestures related to the English alphabet accurately.

关键词： Hands Training Deep learning Sign language Translation Accuracy Machine learning algorithms image processing Predictive models Convolutional neural networks

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Applying Nature-Inspired Optimization: Ant and Bee Colony Approaches for Enhanced Edge Detection in image processing

Applying Nature-Inspired Optimization: Ant and Bee Colony Ap...

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Augmented Intelligence and Sustainable systems (ICAISS), International conference on

作者： B. Hariprasanna S. Vinita Sushila Devi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

Finding boundaries within images is the aim of edge detection, a basic job in computer vision, image processing, and pattern recognition. It can be difficult to detect edges in noisy images or under different situations using traditional edge detection techniques like the Sobel and Canny edge detectors. Motivated by the collective behavior of ants and bees in nature, this work investigates the use of Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) algorithms for edge identification. For difficult tasks like edge detection, these swarm intelligence techniques can efficiently search through vast solution spaces and offer the best answers. The ACO algorithm investigates potential edge sites and optimizes them using pheromone updates by simulating the foraging behavior of ants. However, the BCO algorithm uses the exploration and exploitation behavior of the bees to identify specific boundaries in the image. We compare how well the two systems detect edges in pictures with different noise levels and image quality. The findings demonstrate that edge detection performance is much enhanced by both ACO and BCO, with BCO offering quicker convergence and more precise edge localization. The study shows that ACO and BCO have the potential to be useful tools for detecting edges in noisy and complex images.

关键词： Ant colony optimization Computer vision PSNR image edge detection image processing Pattern recognition Noise measurement Optimization Noise level Convergence

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared images 4

A Comparative Study of the Performance of Personnel Detectio...

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4th International conference on Video, Signal and image processing, VSIP 2022

作者： Liu, Futao Li, Zhenling Wan, Yuezhong Cao, Weida Ma, Duanwei Li, Xiaolei Yan, Peng Liaocheng Power Supply Company State Grid Corporation of China China Shandong University School of Control Science and Engineering China Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the mAP@0.5 can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%. © 2022 ACM.

关键词： image enhancement

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DOMAIN ADAPTATION IN image DEHAZING: EXPLORING THE USAGE OF imageS FROM VIRTUAL SCENARIOS 16

DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF ...

引用

16th International conference on Computer Graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information systems, MCCSIS 2022

作者： Sappa, Angel D. Suárez, Patricia L. Velesaca, Henry O. Carpio, Darío Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 P.O. Box 09 Vía Perimetral 01-5863 Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789898704429

This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator;these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. © MCCSIS *** rights reserved.

关键词： Deep learning

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Vision-based real-time molten pool monitoring for Electron Beam Freeform Fabrication

Vision-based real-time molten pool monitoring for Electron B...

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2023 IEEE International conference on Real-Time Computing and Robotics, RCAR 2023

作者： Liao, Zhenyu Liang, Zhiyue Zhang, Haoyu Li, Zixiang Wang, Li Du, Dong Tsinghua University Department of Mechanical Engineering Beijing100084 China Tsinghua University Key Laboratory for Advanced Materials Processing Technology Ministry of Education Beijing100084 China

ISBN: (纸本)9798350327182

In the additive manufacturing process, the molten pool contains abundant information related to the deposition quality and stability. Real-time monitoring and feature extraction of the molten pool is of great significance for closed-loop control of molten pool and guarantee of consistency and stability of the deposition process. In this study, we develop a visual monitoring system of molten pool for electron beam freeform fabrication(EBF3) and propose corresponding digital image processing algorithms to extract molten pool geometric and identify the wire position and metal transfer state of the deposition. The effectiveness of the proposed algorithm is verified by deposition experiments under different beam currents and different metal transfer states, and the transfer function model between the molten pool feature and the energy input is established by step response experiments. The proposed molten pool visual monitoring method and model identification work provide the foundation for the closed-loop control of the molten pool. © 2023 IEEE.

关键词： Deposition

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Evaluation on the Effectiveness of SVM-Based image processing algorithms for the Identification and Classification of Lung Cancer 15th

Evaluation on the Effectiveness of SVM-Based Image Processin...

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15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bhaskar, Nuthanakanti Swaroopa Rani, B. Srinivasula Reddy, A. Asha Shiny, X.S. Gayathri, B. Venkateswara Reddy, R. CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810794

Globally, lung cancer is a prevalent reason for death for individuals. The most common form of lung cancer has a high death rate. If lung cancer is identify and care for in its early on stages, screening can assist identify the disease at an earlier stage. Therefore, a patient's chances of survival are increased by early identification of lung cancer. Lung computed tomography (CT) scans might represent a more reliable method of identifying cancer than MRIs and X-rays. DICOM (Digital Imaging and Communications in Medicine) is used to scan the lungs and obtain images. We improve and smooth photos using a variety of preprocessing approaches. Edge detection and thresholding (ROI) are used in this work to section the lung tumor's region of interest. Lastly, we use Support Vector Machine to compute a number of geometrical aspects of the retrieved ROI and categorize the results into both benign and malignant degrees of severity (SVM). Data is classified using supervised machine learning techniques called SVM classifiers. One of their advantages is their ability to handle small amounts of high dimensional data. Every characteristic is used as an input to evaluate the SVM's efficiency. As such, lung cancer in CT scans is diagnosed using a method that uses methods for processing images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Performance Optimization of Machine Learning-Driven image Recognition algorithms for iOS Mobile Platforms

Performance Optimization of Machine Learning-Driven Image Re...

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International conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ibragim Mamadaev Muslim Mamadaev Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Today., mobile devices play an important role in the daily life of every person., without which many can no longer imagine their lives. One of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task., it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices., such as quantization and compression of models., optimization of initial calculations. In addition to ways to optimize the machine learning model itself., various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages., and therefore., in the results of the work., it is proposed to use not only a combination of the described options., but also an additional method of parallelization of image processing processes.

关键词： Performance evaluation Support vector machines Machine learning algorithms image recognition Recurrent neural networks Optimization methods Generative adversarial networks Mobile handsets Libraries Convolutional neural networks

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FPGA Based image processing: A Comparative Study of Software-Centric and Hardware-Accelerated Approaches

FPGA Based Image Processing: A Comparative Study of Software...

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Smart Electronics and Communication systems (ISENSE), International conference on

作者： P N Parameswaran S Janardan Siddharth R Nair Thejaswitha T K Nikhil M Adersh V R Dept of Electronics and Communication College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work compares two different approaches to image processing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardware-Accelerated image processing (HAIP), Software-Centric image processing (SCIP), and an in-depth analysis of their corresponding performance metrics. image processing algorithms were implemented in the SCIP phase, exclusively using the Zynq processing System (PS). On the other hand, the HAIP phase comprised creating distinct Intellectual Properties (IPs) for image processing tasks and integrating them with the Zynq PS through Direct Memory Access (DMA) and the Advanced eXtensible Interface (AXI) *** all assessed techniques, HAIP considerably outperformed SCIP in terms of processing time, for different image sizes, across both Zynq Zybo and Zedboard. On the other hand, as SCIP is software-centric, it showed minimal resource utilization and lower power consumption than HAIP, which showed higher resource utilization and power consumption. However, both methods produced processed images that were similar, highlighting the stability and dependability of FPGA-based image processing implementations. A comparative analysis between Zynq Zybo and Zedboard reveals insights into how different FPGA architectures impact the performance and resource utilization of image processing implementations. This work offers insights into the trade-offs between hardware-accelerated and software-centric approaches to image processing on FPGA platforms, guiding future design decisions for practical uses.

关键词： Hands Measurement Power demand image processing Intellectual property Logic gates Stability analysis Robustness Resource management Field programmable gate arrays

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Copy-Move image Forgery Detection using Deep Learning Approaches

Copy-Move Image Forgery Detection using Deep Learning Approa...

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Software, systems and Information Technology (SSITCON), International conference on

作者： Zainab Shawkat Ibrahim Taha Mohammed Hasan Department of Computer Science College of Science University of Diyala Iraq

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The advancement of digital image processing software has reached a stage where it is effortless to manufacture forgeries by using numerous manipulating approaches on authentic photos. Occupations such as law, healthcare, and education are susceptible to manipulated imagery. Therefore, it is crucial to ascertain the authenticity of an image. Single type of image manipulation is identified as ‘copy move,’ when significant item or objects are concealed by duplicating and inserting them into the original image. It is crucial to determine whether an image is genuine. The current techniques employed to identify image forgeries relied on conventional feature extraction algorithms, such as key point and block-based algorithms. These conventional approaches yield a subpar outcome. Deep learning approaches have established superior performance in image processing applications. This paper presents a highly effective method for identifying instances of copy-move forgery in images using a hybrid approach that combines deep learning (DL) such as (VGG19 and ResNet50) for feature extraction and Machine Learning (ML) algorithms including (Random Forests (RF) and Support Vector Machine (SVM)) to classify digital images. The proposed models (VGG19-RF), (VGG19-SVM), (ResNet50-RF), and (ResNet50-SVM) have been compared using two standard datasets. The results indicate that ((VGG19-RF) achieves higher recall, accuracy, precision and F1-score of 97% on MICC-F220 dataset, On the other hand (VGG19-RF) and (ResNet50-RF) achieve higher recall, precision, accuracy and F1-score of 97% on the MICC-F2000 dataset.

关键词： Support vector machines Deep learning Radio frequency Accuracy Digital images Feature extraction Software systems Forgery Standards Residual neural networks

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A N-Dimensional Discrete Chaotic Map and Its Performance

A N-Dimensional Discrete Chaotic Map and Its Performance

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Natural Language processing (ICNLP), International conference on

作者： Xin Gao Xiumei Cai Chengmao Wu School of Automation Xi’an University of Post and Telecommunications Xi’an China School of Electronic Engineering Xi’an University of Post and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to solve the problems of small key space and simple chaotic behavior of low-dimensional chaotic systems in discrete domain, an N-dimensional discrete chaotic mapping system is proposed. An N-dimensional discrete chaotic system is obtained by coupling Chebyshev mapping with ICMIC mapping. Taking two-dimensional chaotic mapping as an example, the Lyapunov index, bifurcation graph, correlation and other properties of the discrete chaotic system are analyzed and applied to more classical image encryption algorithms. Experimental simulation results show that the N-dimensional discrete chaotic mapping system has larger key space, better chaotic behavior, and better security performance for image encryption algorithms.

关键词： Performance evaluation Correlation Sensitivity Simulation Information security Bifurcation Natural language processing Encryption Trajectory Indexes

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