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Non-imaging Real-time Detection and Tracking of Fast-moving Objects Using a Single-pixel Detector 4

Non-imaging Real-time Detection and Tracking of Fast-moving ...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Zhou, Fengming Ke, Shulin Ma, Zhiqiang Nanjing Thunder and Lightning Information Technology Co. Ltd Chengdu China

ISBN: (纸本)9798350373233

Detection and tracking of fast-moving objects have widespread utility in many fields. However, fulfilling this demand for fast and efficient detecting and tracking using image-based techniques is problematic, owing to the complex calculations and limited data processing capabilities. To tackle this problem, we propose an image-free method to achieve real-time detection and tracking of fast-moving objects. It employs the Hadamard pattern to illuminate the fast-moving object by a spatial light modulator, in which the resulting light signal is collected by a single-pixel detector. the single-pixel measurement values are directly used to reconstruct the position information without image reconstruction. Furthermore, a new sampling method is used to optimize the pattern projection way for achieving an ultra-low sampling rate. Compared with the state-of-the-art methods, our approach is not only capable of handling real-time detection and tracking, but also it has a small amount of calculation and high efficiency. We experimentally demonstrate that the proposed method, using a 22kHz digital micro-mirror device, can implement a 105fps frame rate at a 1.28% sampling rate when tracks. Our method breaks through the traditional tracking ways, which can implement the object real-time tracking without image reconstruction. © 2023 IEEE.

关键词： image reconstruction

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Real-time web monitoring of shoulder rehabilitation exercises using artificial vision techniques 4

Real-time web monitoring of shoulder rehabilitation exercise...

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4th international conference on Information Systems and Software Technologies (ICI2ST)

作者： Quizhpe-Cordova, Lenin Figueroa-Diaz, Roberth Barba-Guaman, Rodrigo Univ Nacl Loja Posgrad Ingn Software Loja Ecuador Univ Nacl Loja Carrera Computat Loja Ecuador Univ Tecn Particular Loja Dept Computat & Elect Loja Ecuador

ISBN: (纸本)9798350373226;9798350373219

the implementation of rehabilitation exercises to support recovery from shoulder pain through software development is a promising solution in the area of health. It begins with the identification of user requirements necessary to continue with the design and construction of the software, later technological tools such as the Python programming language, the Django framework used for the creation and administration of the web system are selected. computer vision libraries such as OpenCV and MediaPipe were integrated into the software architecture. the registration and authentication of users was carried out thanks to the established models of Django, it was customized according to the identified needs of the system. Regarding the abduction exercise, it was carried out in four steps, which were validated by calculating the angle of elevation of the arm through image processing with the implemented tool, these angles are registered in a database and later presented in graphs for the analysis and follow-up of the physiotherapist in charge of the rehabilitation.

关键词： Telerehabilitation software development MediaPipe computer vision monitoring software.

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Motor imagery Classification Combining Riemannian Geometry and Artificial Neural Networks 5th

Motor Imagery Classification Combining Riemannian Geometry a...

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5th international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Cecotti, Hubert Tiwale, Girish Calif State Univ Dept Comp Sci Fresno CA 93740 USA

ISBN: (纸本)9783031235986;9783031235993

Brain-computer interfaces (BCIs) based on non-invasive electroencephalography provide a means of communication for people with severe disabilities. BCI based on the detection of motor imagery can be used for both communication and rehabilitation purposes [8]. For transferring BCIs outside of the lab to clinical settings, it is necessary to have a high accuracy. the current state of the art techniques includes the use of distance based on the Riemannian geometry. In this paper, we propose a new pattern recognition system for the multiclass classification of brain evoked responses corresponding to motor imagery. the method is based on the combination of features based on Riemannian geometry obtained from 15 frequency bands from 8 24Hz to cover the mu and beta bands, and a feedforward neural network for the classification. We compare the performance of the multi-layer perceptron (MLP) and the extreme learning machine (ELM) classifiers. the system has been assessed on two publicly available datasets. the kappa value for 4-class is 0.53. the average binary classification across the six pairwise tasks is 80.83%. the results support the conclusion that multi-band classification can be successfully achieved using artificial neural networks and MLPs provide substantially better performance than ELMs approaches.

关键词： Brain-computer interface Motor imagery Classification

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Refining U-Net Architecture through Genetic Algorithms for Improved Skin Lesion image Segmentation 4th

Refining U-Net Architecture Through Genetic Algorithms for I...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： El Abassi, Fouzia Darouichi, Aziz Ouaarab, Aziz Cadi Ayyad Univ Comp & Syst Engn Lab L2IS FSTG Marrakech 40000 Morocco

ISBN: (纸本)9783031821554;9783031821561

Recently, various approaches using manually designed convolutional neural networks (CNNs) and U-shaped (encoder-decoder) models have yielded encouraging results in the automatic segmentation of medical images. Nevertheless, the manual construction of these models is laborious, requires a high level of expertise, is time-consuming, error-prone and can result in the loss of crucial details. To improve segmentation performance, these models often incorporate numerous parameters, which can lead to problems such as overfitting, vanishing and exploding gradient and increased computational complexity. In this study, we implement a genetic algorithm (GA) specifically designed for a U-Net architecture to segment images of skin lesions, using two datasets ISIC-SMALL1 and ISIC-SMALL2 constructed from ISIC-2017 dataset. the main objective is to optimize internal block topologies within the U-Net, in order to identify a high-performance deep neural network with a minimum of parameters. Compared to the U-Net baseline, our proposed methodology demonstrates favorable outcomes in terms of both the number of trainable parameters and Intersection over Union (IoU). For the ISIC-SMALL1 dataset, the leading model surpasses U-Net with a reduced parameter count (0.352001M) and an elevated IoU score (0.826052 vs. 0.736352). Similarly, the second-best model outperforms U-Net with fewer parameters (0.258781M vs. 31.043586M) and a higher IoU score (0.821956 vs. 0.736352). In the ISIC-SMALL2 dataset, both top models outshine U-Net in IoU (0.753122 and 0.722079 vs. 0.725363, respectively) while maintaining lower parameter counts.

关键词： Medical image Segmentation Convolutional Neural Networks (CNNs) U-Net architecture Genetic Algorithms (GA) Skin Lesion

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Identification of astronomical temporal sources in ultra wide angle high frame images 4

Identification of astronomical temporal sources in ultra wid...

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4th international conference on Optics and image processing, ICOIP 2024

作者： Gong, Xu The 15th Institude of China Electronics Technology Group Corporation Beijing China

ISBN: (纸本)9781510682481

Transient sources are important research objects in the field of time-domain astronomy, where nearby celestial bodies or moving objects such as asteroid fragments are suspected transient sources in observations. In the current stage of observation and analysis, the primary method for studying optical transient sources involves capturing multiple consecutive frames of the same sky using a computer and creating a template image using specific algorithms. this template image reflects the state of the sky over a period of time and can be subtracted from any detected image captured at a given moment to produce a residual image. Subsequently, all suspicious target images in the residual image are automatically extracted and presented to the personnel for manual identification, distinguishing between transient source phenomena and false positives. However, this method introduces human factors, leading to low operational efficiency, slow identification speed, and ultimately, the accuracy of identification is affected by the experience of the *** address these issues, with the rise of neural networks in recent years, convolutional neural networks (CNNs) can be trained to replace manual identification of transient sources in astronomical images. By feeding continuous astronomical images captured over time into the CNN, the network automatically identifies whether transient sources are captured in the images. Additionally, the CNN can eliminate false positives caused by errors in methods such as image alignment and subtraction. Ultimately, achieving accuracy comparable to human recognition. this project undertakes the following tasks:1. Preparation of training datasets, including two categories: positive samples of actual transient sources and negative samples of false positives caused by errors in data processing. Specific classifiers are trained to distinguish these false positives.2. the main task of this project is to build a classifier based on convolutional neura

关键词： Electromagnetic transients

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D-ALPR: Drone-Based Automatic License Plate recognition Within Restricted Parking Environment 4th

D-ALPR: Drone-Based Automatic License Plate Recognition With...

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4th international conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Maisuria, Hardik Mepani, Kalp Shah, Manil Verma, Jaiprakash Narmawala, Zunnun Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9789819769940

Given its usefulness, License Plate recognition using a drone is relatively less researched. Placing cameras everywhere is not possible. We can get images and video footage of unreachable places utilizing a drone with a camera. Publicly available datasets do not reasonably include samples with various challenging environmental conditions. So, we created a new dataset by flying the drone over open parking and basements of the premises of Nirma University and taking video footage in different conditions. After processing and labeling, the dataset is fed to the ResNet convolutional neural network to compare the accuracy of the existing approach with publicly available datasets and our dataset. We obtained an accuracy of 50–60% with our dataset, while the accuracy of the existing approaches is between 80–90% with publicly available datasets. It shows that there is a vast scope for improvement if conditions are challenging. the dataset created will be of immense value to the research community intending to work on this problem. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drones

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework 4

Preprocessing and feature extraction of MRI Liver Tumour ima...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Bhagya, A. Perumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350349856

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. the multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. the Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. the new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Sign Language recognition Using Convolutional Neural Network 4th

Sign Language Recognition Using Convolutional Neural Network

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Potluri, Tejaswi Vallurupalli Nageswara Rao Vignana Jyothi Inst En Vignana Jyothi NagarPragathi Nagar Hyderabad 500090 Telangana India

ISBN: (纸本)9783031837920;9783031837937

Sign language detection is essential for improving accessibility, communication, and inclusion for individuals who are deaf or hard of hearing. this technology enables smooth communication across various environments, including schools, workplaces, healthcare settings, and everyday interactions. By accurately interpreting sign language gestures, it bridges communication barriers, providing equal access to information and services. In education, sign language detection supports language learning and academic success for deaf students, while in healthcare, it ensures that medical information is conveyed accurately, enhancing care quality. In the workplace, it fosters inclusivity by enabling communication between deaf employees and their colleagues, promoting equal opportunities and professional growth. Additionally, during emergencies, it can save lives by enabling swift communication with emergency responders. this study presents a robust sign language detection system based on convolutional neural networks (CNNs), trained on a dataset of 2,515 images representing 36 distinct ASL gestures. the system employs a comprehensive preprocessing pipeline, with images standardized, resized, and split for training and validation, resulting in an accuracy rate of 85.87% across ASL letters and numerals. Real-time testing, facilitated by OpenCV's video capture, demonstrates the model's effectiveness in live gesture recognition with minimal delay. Despite minor errors in misclassification and occlusion, the system shows strong potential to support inclusive communication across settings and to further advance sign language recognition technology.

关键词： Convolutional neural networks image processing Feature extraction

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Hand Gesture-Based AI System for Accessing Windows Applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

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4th international conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. the hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. this system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. the system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. the trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. the system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. thus, the proposed model improves the interaction between the human and the computer. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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Indicator and sign recognition in real electricity scenario 4

Indicator and sign recognition in real electricity scenario

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2024 4th international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Wang, Baigen Dong, Lanfang Ma, Tao Huang, Kexuan Anqing Power Supply Company State Grid Anhui Electric Power Company Anqing Anhui China School of Computer Science and Technology University of Science and Technology of China Anhui Hefei China Institute of Advanced Technology University of Science and Technology of China Anhui Hefei China

A multitude of instruments and equipment are contained within the electricity scenario, and their operation leads to fluctuations in a wide range of parameters. these changes are typically represented through indicators and signs. this study introduces an efficient and robust method for recognizing these indicators and signs, utilizing a variety of image processing techniques, including the computation of image mean and variance, as well as template matching. Given the plethora of indicators and signs present on substation equipment, it is crucial to discern the illumination status of indicators at different locations and relay this information to the system based on the indicator's significance. Considering the indoor setting, which is relatively uncomplicated, this study employs the variance and mean of the indicator area image for judgment. As for identifiers, which often vary in shape and color, this study uses template matching for identification, subsequently reporting the operational status of the equipment marked by this area. Experimental results demonstrate that compared to deep learning methods, traditional image processing algorithms yield superior outcomes while significantly reducing computational costs and time. © Published under licence by IOP Publishing Ltd.

关键词： Template matching

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