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Preliminary Study on the Detection of Subtle Variations in image Sequences for Identifying False Starts in Speedway Racing 1

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19th International conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Krakowian, Jacek Jelen, Lukasz Fdn Support Innovat Res & Dev Grobli 12 Lok 0-211 PL-50421 Wroclaw Poland Wroclaw Univ Sci & Technol Dept Comp Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

Computer Vision algorithms have gained significant popularity and prove to be highly beneficial in detecting motion. These methods have proven to be profitable in motor sports especially. This paper presents a preliminary study on image processing, computer vision, and Long Short-Term Memory (LSTM) networks to detect subtle variations in image sequences for identifying false starts in speedway racing. Traditional methods often rely on manual observation, which lacks the precision to capture delicate movements at the start line. The presented methodology utilizes mentioned methods to enhance image quality, computer vision to detect and track racers' positions, and LSTM networks to analyze temporal sequences for movements indicating a false start. Results indicate that the adopted approach outperforms manual false start detection in accuracy and reliability. This advancement not only ensures fairness in speedway racing by providing a more objective detection of false starts but also demonstrates the potential of these technologies for applications requiring the identification of subtle changes in sequences. The efficiency and precision of our method offer promising implications for sports regulations and beyond, advocating for the broader application of technology in similar contexts.

关键词： motion detection speedway false start Gaussian mixture model frame difference Long Short-Term Memory LSTM

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PAIR : Perception Aided image Restoration for Natural Driving Conditions

PAIR : Perception Aided Image Restoration for Natural Drivin...

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IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Shyam, Pranjay Yoo, HyunJin Faurecia IRYStec Inc Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

We present a two-stage mechanism for generic image restoration in natural driving conditions, where multiple non-linear degradations simultaneously impact perception for humans and driving assistance systems. Our approach overcomes the limitations of utilizing a single neural network that incurs excessive computational overhead and yields sub-optimal recovery. The proposed first stage comprises computationally inexpensive image processing operations applied at a patch level using a lightweight convolutional neural network (CNN) that determines their intensity of operation. This patch size is guided by the receptive field of the CNN, allowing for dynamic restoration of non-linear and non-homogeneous degradation profiles. The second stage leverages a lightweight end-to-end neural network functioning as an inpainting network. It identifies inadequately restored regions and leverages global semantic and structural information to fill the affected areas. This approach enhances the restoration process by considering the entire image and addresses the remainder of localized deficiencies. In addition, we integrate dense perception tasks such as semantic and depth estimation during the optimization cycle to ensure restored images that are perceptually pleasing and conducive for downstream perception tasks. Since datasets covering diverse degradation scenarios for high- and low-level perception tasks are lacking, we utilize a synthetic data augmentation technique to generate non-homogeneous non-linear degradation profiles. Experiments on images captured in adverse weather conditions demonstrate the efficacy of our approach, yielding higher perceptual quality in restored images and improved performance in downstream perception tasks under adverse driving conditions. Importantly, our method offers computational efficiency compared to end-to-end image restoration algorithms, making it suitable for real-time applications.

关键词： Applications Autonomous Driving

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A Multi-Modal image Understanding and Audio Description System for the Visually Impaired People 10

A Multi-Modal Image Understanding and Audio Description Syst...

引用

10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Preetham, Monisha Krishnan, Meena Monta Vista High School CA United States Cisco United States

ISBN: (纸本)9798350384369

A significant challenge in the field of computer vision is to develop algorithms that are capable of producing captions for images. Ensuring image accessibility is crucial for individuals who have visual impairments. Audio descriptions provide detailed explanations of displayed events, such as bodily movements and expressive gestures in the images. Producing top-notch audio reports necessitates a substantial amount of personal effort in generating descriptions. To overcome this barrier to accessibility, this paper developed a novel multi-modal system that examines the graphical elements of an image and produces audio. The proposed technique extracts the features from the images by applying the transfer learning strategy. The captions undergo pre-processing to generate the tokens. The attention mechanism-encoder-decoder model receives as inputs the tokens and the image features that have been extracted. The model undergoes testing to create captions for the images, and its performance is assessed using the Bilingual Evaluation Understudy (BLEU) score. The average score on the test dataset is 73.864. The produced captions are transformed into audio format to cater to individuals with visual impairments. The real-time implementation of this technology can provide significant benefits to visually impaired individuals, enabling them to comprehend and analyze visual information in real time. © 2024 IEEE.

关键词： Multicarrier modulation

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Analysis of Ectopic Pregnancy in First Trimester Fetal Ultrasound images using Machine Learning Technique 2

Analysis of Ectopic Pregnancy in First Trimester Fetal Ultra...

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2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Maheswari, S. Bhuvaneshwaran, A. Rajalakshmi, J. Nivetha, S.B. Kishore, R. Deepanjali, S. Department of Biomedical Engineering Velalar College of Engineering and Technology Erode India

ISBN: (纸本)9798350340235

This study focuses on using medical imaging and machine learning to address Ectopic pregnancies (EPs) during the first trimester. The variability in ultrasound image quality and the presence of noise can hinder accurate image analysis in case of manual review by physicians, thus, this study aims to develop an automated system for fetal ultrasound image analysis. The research employs advanced image processing methods, including Region of Interest (ROI) selection and image enhancement techniques. Three classification algorithms CNN, SVM, and KNN are compared to select the most suitable one based on performance metrics. This suggests that CNN is a promising tool with 97% accuracy for the early detection of EP. The proposed system has the potential to transform the diagnosis and management of EP, potentially improving maternal outcomes. The goal is to enhance the precision and effectiveness diagnosis, potentially transforming early-stage pregnancy diagnostics. © 2023 IEEE.

关键词： Diagnostic accuracy Early diagnosis Ectopic pregnancy image enhancement image processing Machine learning Maternal-fetal health Medical imaging Ultrasound images

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Improved Self-Sparse Fuzzy Clustering Algorithm for image Segmentation

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

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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...

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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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Object detection algorithm based on residual deformable convolution pyramid 4

Object detection algorithm based on residual deformable conv...

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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...

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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...

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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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