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Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs) 6th

Identification of Real or Fake Currency Using Semi-supervise...

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mukherjee, Anupam Ghosh, Anupam Department of Computer Science & Engineering Siliguri Institute of Technology West Bengal Siliguri India Department of Computer Science & Engineering Netaji Subhash Engineering College West Bengal Kolkata India

ISBN: (纸本)9789819780891

Traditional paper money and contemporary electronic money are two significant forms of exchange. However, due to the new and improved methods that counterfeiters are using, it is now becoming an increasingly important issue. this work suggests a machine-assisted method that has been created to distinguish between fake and real currency. Generative adversarial networks (GANs), a cutting-edge machine learning model, are used for this purpose. To develop a model that can be used for supervised prediction, GANs utilize semi-supervised learning. Semi-supervised learning is an exceptional approach in building a model that is fit for supervised prediction. It offers a multitude of benefits, allowing the training of unlabeled data while achieving highly accurate predictions. In this work, detecting counterfeit money using GANs involves a multi-step methodology that leverages the power of deep learning for accurate identification. Preprocess the data by resizing, normalizing, and augmenting the images to ensure uniformity and improve model generalization. the discriminator minimizes the loss when making correct classifications, while the generator minimizes the loss when the discriminator classifies its generated images as genuine. indian banknotes were subjected to this procedure. Modern feature identification and image processing techniques were employed to develop the overall strategy of a valid input. the trials demonstrate that a high-precision system may be created to identify real paper money. During the training process, the generator network and the discriminator networks update their loss functions. Once training is complete, the model can accurately identify real money notes from fake ones with an 80% accuracy rate. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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From classical techniques to convolution-based models: A review of object detection algorithms

From classical techniques to convolution-based models: A rev...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Fnu Neha Deepshikha Bhati Deepak Kumar Shukla Md Amiruzzaman Dept. of Computer Science Kent State University Kent OH USA Rutgers Business School Rutgers University Newark New Jersey USA Dept. of Computer Science West Chester University West Chester PA USA

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. these methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. these features include both semantic and high-level representations essential for accurate object detection. this paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance.

关键词： Deep learning computer vision Visualization Reviews Computational modeling Semantics Object detection Feature extraction Convolutional neural networks Object recognition

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Waste Sorting System Using AI: Development of An Embedded So...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Amal Remili Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. this paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. the data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. the system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

关键词： Waste management Measurement computer vision Computational modeling image processing Real-time systems Plastics Artificial intelligence System validation Sorting

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A computer vision Approach for Autonomous Cars to Drive Safe at Construction Zone

A Computer Vision Approach for Autonomous Cars to Drive Safe...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Abu Shad Ahammed Md Shahi Amran Hossain Roman Obermaisser Chair of Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. the autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. this paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. the model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.

关键词： YOLO computer vision Computational modeling Roads Urban areas Robustness Smart transportation Automobiles Autonomous vehicles Automotive engineering

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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International image processing, Applications and Systems conference (IPAS)

作者： Norah A. AlSubaie Ghayda A. AlMalki Ghada N. AlMutairi Sarah A. AlRumaih Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

this research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. the system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. the YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. these results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.

关键词： Measurement Location awareness computer vision Urban planning Machine learning Inspection Road safety Maintenance Mobile applications Defect detection

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Wide Line Segments Detection in Grey-Level images via Guided Scale Space Radon Transform

Wide Line Segments Detection in Grey-Level Images via Guided...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Aicha Baya Goumeidane Djemel Ziou Nafaa Nacereddine Nawal Yala Research Center in Industrial Technologies CRTI Algiers Algeria Département D’Informatique Université de Sherbrooke Québec Canada

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. the paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. the method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. the method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising.

关键词： Radiography image segmentation image analysis Error analysis Welding Noise Transforms Robustness Iron Pattern recognition

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Bridging the Machine-Human Gap in Blurred-image Classification via Entropy Maximisation

Bridging the Machine-Human Gap in Blurred-Image Classificati...

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International image processing, Applications and Systems conference (IPAS)

作者： Emilio Sansano-Sansano Marina Martínez-García Javier Portilla INIT Universitat Jaume I Castellón de la Plana Spain IMAC Universitat Jaume I Castellón de la Plana Spain Instituto de Óptica CSIC Madrid Spain

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image.

关键词： Measurement image quality Visualization computer vision Artificial neural networks Entropy Kernel Optimization Information theory image classification

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Cricket Shot Analysis using Conditional Directed Spatio-Temporal Graph networks

Cricket Shot Analysis using Conditional Directed Spatio-Temp...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Tanu Aneja Brejesh Lall Harsha Malaviya

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent Use of Conditional Spatio-temporal Directed Graph Convolutional Networks(Cond ST-DGCN) [8] to represent human pose estimation has significantly helped in capturing varying non-local dependencies between limbs for different actions. this can be immensely helpful in Sports analytics where player pose plays key role in shot evaluation and can help in corrective action. In this article, we propose CondDGCN [8] based framework to explore use of spatial-temporal relation of batsman shot sequences (labelled and annotated 2D cricket dataset [1]) for Cricket shot action recognition by conditioning the graph network on batsman 2D poses. We achieve 97% accuracy for shot recognition and further explore visualization of conditional graph connections to establish importance of particular limbs for shots. the proposed framework uses fine-tuned 2D Pose estimator OpenPose [11] (fine-tuned for cricket dataset [1]) which in turn helps in easy adaptation of our solution to internet cricket videos for shot analytics.

关键词： Limbs computer vision Graph convolutional networks Visual analytics image processing Pose estimation Directed graphs computer architecture Sports Videos

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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments

Occlusion-aware Data Augmentation and Reconstruction for Imp...

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International image processing, Applications and Systems conference (IPAS)

作者： Vincent Pham Sonit Singh School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

the demand for adaptive learning in education is an important application for computer vision (CV)-based detection models which extract learners’ faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners’ faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection.

关键词： computer vision Visualization Accuracy Computational modeling Pipelines Data augmentation Robustness Data models Faces image reconstruction

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

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International image processing, Applications and Systems conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

the integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

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