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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Detection of Jump Landing Performance of Basketball Player Using image processing and Support Vector Machine

Detection of Jump Landing Performance of Basketball Player U...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jessie R. Balbin Sebastian Paul O. Uy Royce D. Bumagat School of Electrical Electronics and Computer Engineering Mapua University Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This study is dedicated to developing an analytical system aimed at evaluating the jump-landing technique employed by basketball players. This innovative system relies on applying the Support Vector Machine (SVM) image processing algorithm in conjunction with the Kinect sensor to recognize and assess the landing postures of the players. The primary objective of this research is to establish a detection system that can provide precise assessments of landing techniques by comparing them with evaluations made by experienced basketball trainers or coaches. The study mainly concentrates on jump landings in basketball, specifically on maneuvers like layups and rebound jumps. The research outcomes have demonstrated a robust correlation of 95.07 percent between the evaluations generated by the program and those provided by the coach. Rigorous statistical analysis has validated the accuracy of these assessments, which were gathered exclusively from the coaching staff of a single professional basketball team. The resulting system offers valuable insights for enhancing the jump-landing technique of basketball players, making notable contributions to injury prevention and overall performance improvement in the sport.

关键词： Support vector machines Correlation image recognition Statistical analysis Prevention and mitigation Humanoid robots Information technology

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Human Illegal Activity Recognition Based on Deep Learning Techniques

Human Illegal Activity Recognition Based on Deep Learning Te...

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Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： S G Shivaprasad Yadav Sumangala Itagi B V N V Krishna Suresh Hemalatha K.L Ramachandra A C Department of Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Human activity assertion in video is perhaps the most overall applied subjects in the field of picture and video arranging, with different applications in perception (security, sports, and so on), movement ID, video-content-based seeing, man-machine correspondence, and flourishing/handicap care. Homes and public locations such as hospitals, shopping malls, railway stations, airports, and so on all have surveillance cameras placed to safeguard the public's well-being and the safety of their personal items. About seven out of ten of the homicides that have been investigated and solved by professionals over the last several decades have benefited from the use of surveillance film in this capacity. In several instances of criminal investigation, the person of interest was located by comparing their picture to others of the same species that were taken by various cameras in various locations. Person Re-IDentification is the process of discovering matches for a person or suspect across several cameras. This method is also known as Person Matching. The Person Re-Identification system, or PID, is a tool that helps keep track of human entries that are captured on video by a variety of cameras in a variety of settings. As a reaction to an image of the person obtained by a camera, the PRID system retrieves relevant pictures of the same person collected by multiple cameras in various places and at various times. This is done as a result of the input image of the person (called a probe image). By comparing images of the same person captured by different cameras, it is able to carry out the process of automating the search for a person or target that has been followed but been lost in a different camera view.. In this assessment, we propose a novel human development insistence methodology utilizing convolutional neural networks (CNN) and perceive the activity performed on that outline annals. Properly, this examination paper will give the required inspiration to seeing human acti

关键词： Visualization image recognition Manuals Streaming media Cameras Video surveillance Safety

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Monkeypox Detection from Various Types of Poxes: A Deep Learning Approach

Monkeypox Detection from Various Types of Poxes: A Deep Lear...

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international conference for Convergence of Technology (I2CT)

作者： Anik Pramanik Fayazunnesa Chowdhury Salma Sultana Md. Mahbubur Rahman Md. Hasan Imam Bijoy Md. Sadekur Rahman Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Statistics Jahangirnagar University Dhaka Bangladesh

According to World Health Organization (WHO) statistics, monkeypox has been identified as an epidemic in 127 nations so far, and it is spreading quickly over the globe. While the rashes and skin lesions associated with monkeypox usually mimic those of other poxes, including chickenpox and measles. Due to these similarities, it could be challenging for medical professionals to identify monkeypox based just on the appearance of lesions and rashes. Because monkeypox was uncommon before in the current outbreak, healthcare professionals lack knowledge in this area. But the scientific community has demonstrated a rising interest in implementing Artificial Intelligence in Monkeypox prediction and detection from digital skin images as a result of the success of image processing approaches in COVID-19 detection. In this study, we have applied three cutting-edge deep learning models which are InceptionV3, MobileNetV3, and DenseNet201, referred to as transfer learning models, to detect monkeypox on skin images using the publicly available Monkeypox Skin image Dataset 2022 with four classes. According to our research, transfer learning models can detect monkeypox with a top 93.59% accuracy for the InceptionNet-V3 pre-trained model from three implemented algorithms on digitized skin images. For further research, larger training images are required to train those deep learning models to achieve a higher vigorous detection rate.

关键词： Deep learning Training Transfer learning Organizations Medical services Skin Lesions

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LRRU: Long-short Range Recurrent Updating Networks for Depth Completion

LRRU: Long-short Range Recurrent Updating Networks for Depth...

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international conference on computer Vision (ICCV)

作者： Yufei Wang Bo Li Ge Zhang Qi Liu Tao Gao Yuchao Dai Northwestern Polytechnical University and Shaanxi Key Laboratory of Information Acquisition and Processing Chang’an University

Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data. Although such approaches greatly advance this task, their accompanied huge computational complexity hinders their practical applications. To accomplish depth completion more efficiently, we propose a novel lightweight deep network framework, the Long-short Range Recurrent Updating (LRRU) network. Without learning complex feature representations, LRRU first roughly fills the sparse input to obtain an initial dense depth map, and then iteratively updates it through learned spatially-variant kernels. Our iterative update process is content-adaptive and highly flexible, where the kernel weights are learned by jointly considering the guidance RGB images and the depth map to be updated, and large-to-small kernel scopes are dynamically adjusted to capture long-to-short range dependencies. Our initial depth map has coarse but complete scene depth information, which helps relieve the burden of directly regressing the dense depth from sparse ones, while our proposed method can effectively refine it to an accurate depth map with less learnable parameters and inference time. Experimental results demonstrate that our proposed LRRU variants achieve state-of-the-art performance across different parameter regimes. In particular, the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and ranks 1st on the KITTI depth completion benchmark at the time of submission. Project page: https://***/LRRU/.

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Disease Detection in Areca Nut Using Deep Learning

Disease Detection in Areca Nut Using Deep Learning

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ieee international conference on Cloud Computing in Emerging Markets (CCEM)

作者： Kiran Kumar B S Varshini U Manohar N Tian Jipeng Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Computer Science College Zhongyuan University of Technology Zhengzhou Henan Province China

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

The areca nut finds use in multiple industries. It is used in the production of betel quid, in traditional medicine, and in cosmetics and oral care products. However, it is important to consider health risks and adhere to safety and quality standards in its industrial applications, although some of the research has not depicted multiple diseases in shells. The effects of various diseases affecting areca nuts can be attributed. In our research we developed a novel dataset consisting of 1332 samples that was developed for the detection of diseases in areca nuts such as button shedding, fruit rot, fruit crack, and exfoliation. In this research, five convolutional models, namely VGG-16, DenseNet121, MobileNetV3-large, ResNet-50, and ResNet-18, were developed for the detection of areca nut disease. Among them, the proposed model using ResNet-18 achieved an accuracy of 98.12% with an inference time of 0.334 milliseconds on the validation dataset. This research contributes to the sustainability and profitability of this vital agricultural crop.

关键词： Accuracy Profitability Computational modeling Production Safety Sustainable development Standards Diseases image classification Residual neural networks

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Intelligent Visual Tracking Robots Using Cascade PID Controllers

Intelligent Visual Tracking Robots Using Cascade PID Control...

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Signal, Control and Communication (SCC), international conference on

作者： Rihem Farkh Ghislain Oudinet Florian Sananes Sultan Alzahrani Saad Alhuwaimel Thibaut De Leruyelle ISEN Toulon Toulon France Health Sector KACST Digital Health Institute Riyadh Saudia Arabia King Abdulaziz City For Science and Technology Riyadh Saudia Arabia

ISBN: (数字)9798350326390

ISBN: (纸本)9798350326406

A tracking system is useful for a variety of robotic applications, including obstacle detection, autonomous path planning and surveillance. One of the most difficult issues in computer vision is visual object tracking, which involves estimating the position of a target in an image series. Utilizing color information for visual tracking poses a formidable challenge due to variations in illuminant, shadows, shading, specular highlights, cameras, and object geometry. Consequently, color measurements exhibit significant fluctuations throughout an image series. A camera is frequently used for object tracking to acquire information on construction robot motion, which may subsequently be used in coordination controls. The paper presents a mobile robot vision system that allows the robot to recognize and track objects based on their color. A new approach is proposed for stable tracking of mobile robot using combined algorithm based on computer vision techniques and a cascade proportional integral derivative controllers (PID). Within a system designed for controlling the speed of a DC motor, the goal of a PID controller is to maintain speed at a specified reference value while dynamically accepting new set point values. The combination of feedback encoder and PID controllers can be employed to control motor speeds in the presence of noise and load disturbances

关键词： Visualization computer vision Target tracking image color analysis Robot kinematics Surveillance Robot vision systems

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ATOM: Automated Black-Box Testing of Multi-Label image Classification Systems

ATOM: Automated Black-Box Testing of Multi-Label Image Class...

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ieee international conference on Automated Software Engineering (ASE)

作者： Shengyou Hu Huayao Wu Peng Wang Jing Chang Yongjun Tu Xiu Jiang Xintao Niu Changhai Nie Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University China Guangdong OPPO Mobile Telecommunications Corp. Ltd. China

Multi-label image Classification Systems (MICSs) developed based on Deep Neural Networks (DNNs) are extensively used in people's daily life. Currently, although there are a variety of approaches to test DNN-based systems, they typically rely on the internals of DNNs to design test cases, and do not take the core specification of MICS (i.e., correctly recognizing multiple objects in a given image) into account. In this paper, we propose ATOM, an automated and systematic black-box testing framework for testing MICS. Specifically, ATOM exploits the label combination as the testing adequacy criteria, hoping to systematically examine the impact of correlations between a fixed number of labels on the classification ability of MICS. Then, ATOM leverages image search engine and natural language processing to find test images that are not only common to the real-world, but also relevant to target label combinations. Finally, ATOM combines metamorphic testing and label information to realize test oracle identification, based on which the ability of MICS in classifying different label combinations is evaluated. To evaluate the effectiveness of ATOM, we have performed experiments on two popular datasets of MICS, VOC and COCO (each with five state-of-the-art DNN models), and one real-world photo tagging application from our industrial partner. The experimental results reveal that the performance of current DNN-based MICSs remains less satisfactory even in recognizing correlations between only two labels, as ATOM triggers a total number of 6,049 such label combination related errors for all MICSs studied. In particular, ATOM reports 587 error-revealing images for the industrial MICS, in which 92% of them are confirmed by the developers.

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Global Knowledge Calibration for Fast Open-Vocabulary Segmentation

Global Knowledge Calibration for Fast Open-Vocabulary Segmen...

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international conference on computer Vision (ICCV)

作者： Kunyang Han Yong Liu Jun Hao Liew Henghui Ding Jiajun Liu Yitong Wang Yansong Tang Yujiu Yang Jiashi Feng Yao Zhao Yunchao Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Tsinghua Shenzhen International Graduate School Tsinghua University ByteDance Nanyang Technological University

Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS). However, existing OVS techniques confront a fundamental challenge: the trained classifier tends to over-fit on the base classes observed during training, resulting in suboptimal generalization performance to unseen classes. To mitigate this issue, recent studies have proposed the use of an additional frozen pre-trained CLIP for classification. Nonetheless, this approach incurs heavy computational overheads as the CLIP vision encoder must be repeatedly forward-passed for each mask, rendering it impractical for real-world applications. To address this challenge, our objective is to develop a fast OVS model that can perform comparably or better without the extra computational burden of the CLIP image encoder during inference. To this end, we propose a core idea of preserving the generalizable representation when fine-tuning on known classes. Specifically, we introduce a text diversification strategy that generates a set of synonyms for each training category, which prevents the learned representation from collapsing onto specific known category names. Additionally, we employ a text-guided knowledge distillation method to preserve the generalizable knowledge of CLIP. Extensive experiments demonstrate that our proposed model achieves robust generalization performance across various datasets. Furthermore, we perform a preliminary exploration of open-vocabulary video segmentation and present a benchmark that can facilitate future open-vocabulary research in the video domain.

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Sparse to Dense Ground Truth Pre-processing in Hyperspectral Imaging for In-Vivo Brain Tumour Detection

Sparse to Dense Ground Truth Pre-Processing in Hyperspectral...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), ieee international conference on

作者： Guillermo Vazquez Alberto Martín-Pérez Manuel Villa Jaime Sancho Gonzalo Rosa Pedro L. Cebrián Pallab Sutradhar Alejandro Martinez De Ternero Angel Perez-Nuñez Luis Jimenez-Roldan Alfonso Lagares Miguel Chavarrías Eduardo Juarez César Sanz CITSEM. Universidad Politécnica de Madrid Madrid Spain Instituto de Investigación Sanitaria Hospital 12 de Octubre. Hospital Universitario 12 de Octubre Madrid Spain

image segmentation tasks often require fully annotated datasets where the boundaries of the elements to be identified appear accurately marked. However, such detailed ground truth is hard to obtain mainly because it usually involves a time consuming procedure. In biomedical applications, this may imply that the medical specialist in charge of the labelling process can only mark a few sparse samples belonging to the principal elements of interest. In a context of in-vivo brain tumour detection through machine learning techniques and hyperspectral imaging, such sparse ground truth restricts the training of the classifiers to work at pixel level. In addition, the absence of a dense region localising the tumour makes it more difficult to assess the quality of the segmentation with objective metrics. To address these problems, two ground truth pre-processing methodologies are proposed in order to obtain a dense ground truth map of the tumour region from sparse annotations: a BFS (Breadth-First Search)-based method and an adaptation of the SLIC superpixels algorithm. The proposed work is tested by analysing the effect it has on the training of a convolutional neural network with an autoencoder-type architecture. The results are validated comparing the training metrics obtained using a sparse ground truth, the proposed methodology and the state of the art techniques.

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MRI-3D image Fusion Using an Automatic Cone-Centred Breast Ultrasound Scanner

MRI-3D Image Fusion Using an Automatic Cone-Centred Breast U...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), ieee international conference on

作者： Narendra B Mustare U. Sivaji Debi Prasad Das A. Sivasangari Veeraswamy Ammisetty Doppalapudi Harshitha CVR College of Engineering Hyderabad Telangana State India Information technology Institute of Aeronautical Engineering Hyderabad India KIIT School of Management KIIT University Bhubaneswar Department: IT Sathyabama Institute of Science and Technology Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Electronics and communication engineering KKR & KSR Institute of Technology and Sciences India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The limits of conventional approaches are criticized and the importance of prompt detection of breast cancer with sophisticated imaging techniques is emphasized in this study report. The research supports the revolutionary potential of MRI-3D image fusion, which goes beyond the limitations of traditional modalities by fusing 3D MRI with computerized cone-centered breast ultrasonography. It explores multimodal image fusion techniques such as spatial registration and feature-based synthesis and shows how they might improve understanding of breast lesions. It is recognized that incorporating intelligent technology (AI) into combined image operations may automate procedures, increase accuracy, and decrease human error. Even with these developments, problems like false positives continue to arise, which is why neural network integration is being proposed. The study also looks at newer imaging modalities such PET and tomosynthesis, highlighting how they work together to provide a complete picture of breast tumors. The use of these innovative technologies must take into account ethical factors such as patient permission, privacy, and equitable access. The study ends by recommending ongoing investigation, commitment to ethical principles, and patient-centered treatment in order to mold a bright future for diagnostics related to breast health.

关键词： Ethics Privacy Three-dimensional displays Magnetic resonance imaging Neural networks Imaging Breast

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