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Adaptive Vehicle Trajectory Clustering Based on computer vision 3rd

Adaptive Vehicle Trajectory Clustering Based on Computer Vis...

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3rd international conference on Cognitive Mobility, CogMob 2024

作者： Agg, Áron Dávid Péter, Bence Gábor Horváth, András Doctoral School of Multidisciplinary Engineering Sciences Széchenyi István University Győr Győr Hungary Széchenyi István University Győr Győr Hungary Department of Physics and Chemistry Széchenyi István University Győr Győr Hungary

ISBN: (纸本)9783031817984

Intelligent Transportation Systems is a rapidly evolving and extensively researched field. The development of tools and methods necessitated the appearance of a new concept, Cognitive Mobility, covers greater integration of transport related areas. Our research goes beyond simple image processing and statistical analysis and includes cognitive elements of mobility, for example, gives methods for adaptive analysis of typical vehicle behavior in junctions and helps in decision making for better utilization of infrastructure. In this work, we present an adaptive approach to interpreting traffic scenes. This approach uses the YOLOv7 object detector and DeepSORT tracking algorithm to generate trajectories of moving vehicles in video recordings captured from a stationary viewpoint. The paper examines and compares various techniques for clustering the vehicle trajectories, such as using clustering algorithms to group vehicles based on their entry and exit points within the video scene. Furthermore, we report the results of clustering parameter sensitivity tests performed on the video dataset we created. This control over the granularity of the clustering enables generating street-level or lane-level traffic data from the same recordings. Finally, we demonstrate some practical applicability of the method through illustrative examples, showcasing how the generated traffic data can be used to monitor and analyze traffic patterns, identify congestion points, and inform transportation planning and decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer vision Intelligent Transportation Systems Traffic Monitoring Trajectory Clustering

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Enhancing the Generalization of Synthetic image Detection Models through the Exploration of Features in Deep Detection Models

Enhancing the Generalization of Synthetic Image Detection Mo...

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Iranian Machine vision and image processing (MVIP)

作者： Alireza Hajabdollah Javaheri Hossein Motamednia Ahmad Mahmoudi-Azanveh Cyberspace Research Institute Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran

One of the major challenges of AI is the misuse of images generated by generative models. Advances in this field have reached a point where distinguishing between real and fake images can be impossible for humans and challenging even for machines. Although significant work has been done on detecting fake images, there is an ongoing competition between content generation and detection methods. However, a significant challenge for detection methods is their limitation to content generated by specific models. This study aims to enhance the generalization of fake image detection methods. Experimental results indicate that modifications made to the base model have contributed to improving its generalizability.

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Hyperspectral image Acquisition Methods and processing Techniques Based on Traditional and Deep Learning Methodologies-A Study 3

Hyperspectral Image Acquisition Methods and Processing Techn...

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3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Swarupa, V.Vishnu Sai Devanathan, M. School of ECE REVA University Bangalore India

ISBN: (纸本)9781665456647

Hyperspectral image processing is a very rapidly growing technology these days. It extends the scope of its applications across diversified fields such as Remote sensing, precision agriculture, medicine, computer vision applications and many more. Especially Hyperspectral imagery, benefiting from its very own nature of providing rich spectral information, find many apllications in the field of Remote sensing and Earth observation. With the development of hyperspectral imaging sensor technology, the supply of HSI data has increased greatly, creating a need for proportionate rise in the HIS processing techniques for better interpretation of data. In this context Deep Learning has become a viable option to handle huge datasets like Hyperspectral imagery. Thus many enthusiastic researchers are showing interest in the domain of Hyperspectral image processing. This paper attempts to provide the basic concepts of HSI acquisition and processing at an abstract level with more focus given to Deep Learning based HSI processing tasks. So that the work will come in handy to the beginners in this field to get the preliminary concepts of HSI at a glance. To the best of our knowledge this work is first of its kind to bring to together in this study the the concepts of HSI acquisition and HSI processing. © 2022 IEEE.

关键词： image acquisition

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Research on image processing and Generative Teaching in the Context of AIGC 3

Research on Image Processing and Generative Teaching in the ...

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3rd international conference on Information Science and Education, ICISE-IE 2022

作者： Rao, Jian Qiu, Chuqi Xiong, Mengzhen Art and Design College Wuhan China

ISBN: (纸本)9798350320381

AIGC, meaning AI-generated content, is discussed in this paper, mainly in the form of painting, without discussing the automation of music composition and text-writing poetry, etc. While generative art has aesthetic characteristics, more content needs to be studied in the context of computer information science, with a particular emphasis on computer vision and computer graphics. The article focuses on a comparative analysis of two models, diffusion algorithms and generative adversarial networks, and their application to tools. The practical part of image processing uses a combination of case studies and questionnaires to demonstrate the lack of methodology, teaching experience, and introductory learning materials for non-computer professionals in the emerging field, and to explain "filter mapping"through processing, a visual programming software. The author's reflections on the generated content combine the self-similarity of the "Uncanny Valley effect"and the "Dunning Kruger effect"lineage, comparing the "self-organizing"(machine) simulation personification and the "life form"(human) simulation personification. The process of cognitive assimilation of a "living organism"(human) is used to understand the new human-machine associative relationship. © 2022 IEEE.

关键词： Mapping

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Differential Maximum Euclidean Distance Transform Computation in Component Trees 3rd

Differential Maximum Euclidean Distance Transform Computatio...

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3rd international Joint conference on Discrete Geometry and Mathematical Morphology (DGM)

作者： Silva, Dennis J. Vechiatto Miranda, Paulo Andre Alves, Wonder A. L. Hashimoto, Ronaldo F. Kosinka, Jiri Roerdink, Jos B. T. M. Univ Sao Paulo Inst Math & Stat R Matao 1010 BR-05508090 Sao Paulo SP Brazil Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Nijenborgh 9 NL-9747 AG Groningen Netherlands Nove de Julho Univ Informat & Knowledge Management Grad Program R Vergueiro 235-249 BR-01525000 Sao Paulo SP Brazil

ISBN: (纸本)9783031577925;9783031577932

The distance transform is an important binary image transformation that assigns to each foreground pixel the distance to the closest contour pixel. Among other applications, the maximum distance transform (DT) value can describe the thickness of the connected components of the image. In this paper, we propose using the maximum distance transform value as an attribute of component tree nodes. We present a novel algorithm to compute the maximum DT value of all connected components of a greyscale image in a differential way by joining an incremental method for contour extraction in component trees and the Differential image Foresting Transform (DIFT). We save processing time by reusing the DIFT subtrees rooted at the contour points (DIFT seeds) of a node in its ancestors until those points are not contour points anymore. We experimentally show that we can compute the maximum distance attribute twice as fast as the node-reconstruction approach. Our proposed attribute is increasing and its applicability is exemplified by the design of an extinction value filter. The ability to select thin connected components, like cables, of our filter is compared to filters using other increasing attributes in terms of their parameters and their resulting images.

关键词： Component tree Distance transform image Foresting Transform

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An Integrated Mobile vision System for Enhancing the Interaction of Blind and Low vision Users with Their Surroundings

An Integrated Mobile Vision System for Enhancing the Interac...

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3rd international conference on image processing and vision Engineering, IMPROVE 2023

作者： Chen, Jin Ramnath, Satesh Samaroo, Tyron Maksakuli, Fani Ruci, Arber Sturdivant, E’edresha Zhu, Zhigang Nearabl Inc New YorkNY10023 United States Computer Science Department The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper presents a mobile-based solution that integrates 3D vision and voice interaction to assist people who are blind or have low vision to explore and interact with their surroundings. The key components of the system are the two 3D vision modules: the 3D object detection module integrates a deep-learning based 2D object detector with ARKit-based point cloud generation, and an interest direction recognition module integrates hand/finger recognition and ARKit-based 3D direction estimation. The integrated system consists of a voice interface, a task scheduler, and an instruction generator. The voice interface contains a customized user request mapping module that maps the user’s input voice into one of the four primary system operation modes (exploration, search, navigation, and settings adjustment). The task scheduler coordinates with two web services that host the two vision modules to allocate resources for computation based on the user request and network connectivity strength. Finally, the instruction generator computes the corresponding instructions based on the user request and results from the two vision modules. The system is capable of running in real time on mobile devices. We have shown preliminary experimental results on the performance of the voice to user request mapping module and the two vision modules. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Palmprint recognition

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vision-Based Fall Detection System For The Elderly Using image processing And Deep Learning 3

Vision-Based Fall Detection System For The Elderly Using Ima...

引用

3rd international conference on computer vision and Information Technology, CVIT 2022

作者： Duc, Nguyen Tien Viet, Do Hoang Van Thai, Nguyen Ho Chi Minh City University of Technology and Education Viet Nam Ho Chi Minh City University of Technology and Education Viet Nam

ISBN: (数字)9781510663046

ISBN: (纸本)9781510663039

Population aging is happening in developed and some developing countries, this means the proportion of elderly people in society is increasing dramatically. The elderly often suffers from bone and joint diseases that make their daily live difficult. Therefore, accidental falls are a major cause of loss of autonomy, injuries among the elderly. Healthcare surveillance systems need to be improved to take care of these elderly due to the lack of nurses, this paper represents a solution to this problem. By using a vision-based architecture, the healthcare surveillance system can detect fall accidents in people's daily life activities and then notify the nurse to have in-time assistance. Yolov3-tiny is applied to detect humans in the frame, bounding boxes are generated to visualize the detection, then humans are tracked using the Kalman filter algorithm. AlphaPose is applied to generate key points from detected persons. Each keypoint coordinate will change frame after frame when persons in the frame are moving, then keypoint coordinate states are fed into ST-GCN as input, and the ST-GCN model will predict the probability of the person's activity (fall or not fall). Our model is more effective at detecting and predicting falls, distinguishing it from many other proposed models in the past. Some of our improvement include fall detection in low lighting conditions, multi-person fall detection, and fall detection when the human body is partially occluded. The experimental results show that our proposed vision-based surveillance system achieves high accuracy of 99.08%, precision of 98.84%, and recall of 98.03%. © 2023 SPIE.

关键词： Fall detection

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Self-Supervised Contrastive Learning In Spiking Neural Networks

Self-Supervised Contrastive Learning In Spiking Neural Netwo...

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Iranian Machine vision and image processing (MVIP)

作者： Yeganeh Bahariasl Saeed Reza Kheradpisheh Departement of Computer and Data Sciences Shahid Beheshti University Tehran Iran

Spiking neural networks (SNNs), inspired by the biological neural processing of the brain, are vastly growing due to their higher potential to handle spatiotemporal patterns with lower energy consumption, especially, if implemented on neuromorphic devices. In this study, we propose self-supervised contrastive learning (SSL) for SNNs to learn informative latent representations from a large set of unlabeled data. The proposed SSL pre-trained SNN is then fine-tuned on a small set of labeled samples of a downstream supervised task. To evaluate the proposed method, we trained convolutional SNNs using SSL on MNIST and CIFAR10 datasets with 80% of images as unlabeled samples, then fine-tuned the networks on the remaining 20% images. The proposed SSL-based SNNs could reach 94.23% and 62.24% recognition accuracies on testing sets of MNIST and CIFAR10, respectively.

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Non-invasive Haemoglobin Estimation Using Different Colour and Texture Features of Palm 1

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7th international conference on computer vision and image processing, cvip 2022

作者： Kesarwani, Abhishek Das, Sunanda Dalui, Mamata Kisku, Dakshina Ranjan Department of Computer Science and Engineering National Institute of Technology Durgapur India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Anaemia, caused due to lack of blood haemoglobin levels, is one of the most common diseases which affects billions of people across the world. According to WHO statistics, India is one of the developing countries with highest prevalence of anaemia. Conventional invasive methods are cost-prohibitive and difficult to administer globally which essentially demands non-invasive, accurate, and low-cost approaches for screening of anaemia. The current work targets to combine cutting edge computational approaches with the age-old practice of rough estimation of blood haemoglobin levels by observing pallor in the palm to develop a non-invasive reliable anaemia detection system. The proposed system works with the principle of inducing pallor changes in palm with suitable pressure application and release, measuring the rate of change of colour and performing time-domain analysis thereof to correlate with blood haemoglobin concentration. The entire event of colour changes in palm induced through a customized device, is videophotographed using smartphone camera sensor and is processed and analysed through a set of image processing and analysis techniques. Different handcrafted colour and texture feature extraction techniques are applied on some of the dominant frames considering different colour models on the video samples. The set of features selected throughfeatureselectiontechniques areprovidedas input tomulti-layer perceptron (MLP) networks comprising of different activation functions and optimizers. The proposed system ensures an accurate estimation of blood haemoglobin level with an average RMSE of 0.597 as determined based on palm pallor video samples of 41 participants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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image-Based Physics Rendering for 3D Surface Reconstruction: A Survey 1

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3rd international conference on 3D Imaging Technologies - Multidimensional Signal processing and Deep Learning (3DIT-MSP and DL)

作者： Fang, Da Qin, Zhibao Liang, Shaojun Luo, Renbo Yunnan Normal Univ Yunnan Key Lab Opt Elect Informat Technol Kunming Yunnan Peoples R China

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

Obtaining 3D surface information and physical material information of an object from images is an essential research prospect in computer vision and computer graphics. image-based 3D reconstruction is to extract the 3D depth information of the scene and objects from single or multiple images through specific algorithms to reconstruct the 3D model of objects or locations with robust realism, which has fast reconstruction speed, simple equipment, realistic effect, and minor technical data, which can better realize the virtualization of natural objects.

关键词： image 3D reconstruction Physics rendering

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