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international conference on Intelligent Systems Design and applications (ISDA)

作者： Nik Nur Aisyah Nik Ghazali Nazri A. Zamani Siti Norul Huda Sheikh Abdullah Jinjuli Jameson Faculty of Defence Science and Technology Universiti Pertahanan National Malaysia Kuala Lumpur Malaysia Digital Forensics Department CyberSecurity Malaysia Selangor Darul Ehsan Malaysia Pattern Recognition Research Group Center for Artificial Intelligence Technology Faculty of Technology and Information Science Universiti Kebangsaan Malaysia Selangor Darul Ehsan Malaysia

Generating a quality high resolution image has become an essential for variety purposes especially in forensic field. Compressed and at low resolution video frames of common security surveillance videos are found to be very low in clarity and degraded with many noises, distortions, blurs, bad illumination and video compression artifact. this could interfere during image interpretation and analysis process. this paper proposed a combination of super resolution methods for image processing. Using super resolution methods, high resolution image is obtained from a set of low resolution images, after it had undergone two main processes; image registration process based on Keren algorithm and image reconstruction process based on Projection onto Convex Set (POCS) on frequency domain. the validation process of output is done by calculating the Peak Signal to Noise Ratio (PSNR) value to show the comparison of image quality. the experimental results have shown that our proposed combinatorial method based super resolution and nearest neighbor methods outperformed other state-of-the-art methods.

关键词： Decision support systems Intelligent systems PSNR

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Improved Multi-Face Detection with ResNet for Real-World applications

Improved Multi-Face Detection with ResNet for Real-World App...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Munish Kumar Monali Gulhane Sandeep Kumar Harshita Sharma Rashmi Verma Deepika Verma Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Nagpur India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India Department of Computer Science and Engineering Om Sterling Global University Hisar Haryana India

this paper presents an improved approach to multi-face detection using ResNet50 in Python for real-world applications. the study focuses on enhancing the accuracy and efficiency of detecting multiple faces in a single frame, a challenge often encountered in real-world scenarios such as surveillance and social media platforms. the proposed method leverages the power of ResNet, a deep residual learning framework known for its superior performance in image recognition tasks. the paper discusses the implementation details of the ResNet model in Python and how it has been optimized for multi-face detection. Experimental results demonstrate that the improved ResNet model outperforms traditional face detection methods regarding accuracy and speed, making it a promising solution for real-world applications. the paper concludes with potential future work and improvements that could further enhance the model's performance.

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Sparse representation super-resolution method for enhancement analysis in video forensics

Sparse representation super-resolution method for enhancemen...

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international conference on Intelligent Systems Design and applications (ISDA)

作者： Nazri A. Zamani A.D.M. Zahamdin Siti Norul Hilda Sheikh Abdullah Md. Jan Nordin Digital Forensics Department CyberSecurity Malaysia Selangor Selangor Malaysia CyberSecurity Malaysia Seri Kembangan Selangor MY Pattern Recognition Research Group University Kebangsaan Malaysia Bangi Selangor Malaysia

the enhancement analysis in video forensics is used to enhance the clarity of video frames of a video exhibit. the enhanced version of these video frames is important as to assist law enforcement agency for investigation or to be tended as evidence in court. the most significant problem observed in the analysis is the enhancement of objects under probe in video. In many cases, the probes appeared to be in low-resolution and degraded with noise, lens blur and compression artifacts. the enhancement of these low quality probes via conventional method of denoising and resizing has proven to further degrade the quality of the prober the objective of this paper is to propose an enhancement analysis algorithm based on super-resolution. Hence, we present an solution which is a single-frame solution for super-resolution. For that purpose, our proposed method incorporates sparse coding with Non-Negative Matrix Factorization in order to improve hallucination of probes in video. Sparse coding is employed in learning a localized part-based subspace which synthesizes higher resolution with respect to overcomplete patch dictionaries. We test our proposed method and compare with state-of-the-art methods namely resampling and super-resolution method, by enhancing probes in exhibit videos. We measure the image quality using peak-signal-to-noise-ratio. the result shows that our proposed method outperforms state-of the-art methods after enhancing probes in exhibit videos.

关键词： Forensics Image resolution Probes Signal resolution Computer aided software engineering Object recognition Image coding

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Deep Neural Network-Based Fingerprint Reformation for Minimizing Displacement

Deep Neural Network-Based Fingerprint Reformation for Minimi...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Munish Kumar Sandeep Kumar Monali Gulhane Rajender Kumar Beniwal Nitin Shilpha Choudhary Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of CSE Symbiosis Institute of Technology (SIT) Nagpur Symbiosis International (Deemed University) (SIU) Pune India Electrical and Electronics Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Computer Engg. Govt. Polytechnic Hisar Haryana Department of Computer Science and Engineering(AI & ML) Neil Gogte Institute of Technology Hyderabad India

In the realm of biometric authentication and forensic analysis, the accurate matching of fingerprints is paramount. Fingerprint displacement, caused by factors such as skin elasticity and pressure during touch, has been a significant challenge in achieving precise fingerprint recognition both during the enrollment and the authentication process. this research presents a technique to address this issue by leveraging Support Vector Machines (SVM) for fingerprint transformation and displacement minimization. the proposed methodology involves the extraction of distinctive fingerprint features and the application of SVM-based algorithms to realign and correct fingerprint distortions resulting from displacement. We analyze the effectiveness of SVM in reducing displacement-induced errors and improving matching accuracy. Experimental results demonstrate the potential of SVM-based fingerprint transformation techniques to significantly enhance the robustness and reliability of fingerprint recognition systems. this research contributes to the ongoing efforts in biometrics and forensic science by providing a practical solution to the problem of fingerprint displacement, with potential applications in identity verification, criminal investigations, and security systems. the findings of this study offer a promising avenue for further research and development in the field of biometric authentication, ultimately leading to more accurate and secure identification methods.

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FreeStyler: A Free-Form Stylization Method via Multimodal Vector Quantization 12th

FreeStyler: A Free-Form Stylization Method via Multimodal V...

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12th international conference on Computational Visual Media, CVM 2024

作者： Liu, WuQin Lin, MinXuan Huang, HaiBin Ma, ChongYang Dong, WeiMing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Kuaishou Technology Beijing100085 China

ISBN: (纸本)9789819720910

Image stylization refers to the process of transforming an input image into a new one, while retaining its original content but in different styles. However, most existing works only support single-modal guidance, which is not ideal for real-world applications. To tackle this limitation, we propose FreeStyler, a flexible framework for image stylization that is capable of handling various input scenarios. Our approach goes beyond the traditional approach of relying on content and style images to generate a stylized image and supports situations where these references are absent. Specifically, in such cases, FreeStyler allows performing the stylization through text or audio information. the core of FreeStyler is a vector quantized style transfer framework that encodes content and style information into a shared discrete latent feature space, followed by a stylization transformer for style fusion and an image decoder for stylized image reconstruction. To enable free-form stylization, we introduce a novel pseudo-paired token predictor that can estimate tokens from varying input forms without the need for additional text or audio data. Specifically, we leverage Contrastive Language-Image Pre-training (CLIP) as prior knowledge to align discrete representations across different modalities and train the framework using an image and pseudo caption pair provided by Bootstrapping Language-Image Pre-training (BLIP). through qualitative and quantitative experiments, our method has demonstrated superior performance compared to state-of-the-art stylization methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vector quantization

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At-Bit Lithology Estimation Based on Surface Drilling Parameters by Online Machine Learning and Multi-source Data Fusion: methods and applications 12th

At-Bit Lithology Estimation Based on Surface Drilling Parame...

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12th international Field Exploration and Development conference, IFEDC 2022

作者： Tao, Xin-Gang Zhang, Hong-Bao Yang, Shun-Hui Chen, Dong Bai, Kan-Kan Zhou, Fei Beijing China SINOPEC Research Institute of Petroleum Engineering Co. Ltd Beijing China

ISBN: (纸本)9789819919635

this paper proposed a lithology recognition method while drilling based on machine learning and multi-source fusion mechanism, which aims to effectively identify the lithology at bit in real time, and provide decision-making reference for drilling process monitoring, downhole tools adjustment and drilling parameters optimization. the nonlinear relationship between lithology and surface drilling parameters is mapped by extreme gradient boosting (XGBoost), and an online machine learning mechanism is designed to calibrate the classification model in real time according to the cutting descriptions obtained from the drilled section. the model is established by the 6 adjacent wells, in which the stratigraphic sedimentation and dynamic calibration are taken as constraints, so that the model can adjust itself adaptively and auto-learn while drilling. Field tests show that incremental learning while drilling and spatial geological prior information can significantly improve accuracy and robustness, and the average accuracy of lithology detection by multi-source information fusion method is over 90%. this scheme adopts the methods of machine learning, calibration and spatial prior knowledge constraints to establish a comprehensive lithology identification model at bit, which is helpful to develop industrial data scientific solutions and provide a basis for optimization while drilling. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Lithology

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conference Calendar [conference Calendar]

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IEEE Computational Intelligence Magazine 2023年第1期18卷 104-104页

作者： Marley Vellasco Leandro Minku Pontifícia Universidade Católica do Rio de Janeiro BRAZIL University of Birmingham U.K.

Δ 12th international conference on pattern recognition applications and methods (icpram 2023)

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Sampling rate impact on energy consumption of biomedical signal processing systems

Sampling rate impact on energy consumption of biomedical sig...

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international Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Andreas Tobola Franz J. Streit Chris Espig Oliver Korpok Christian Sauter Nadine Lang Björn Schmitz Christian Hofmann Matthias Struck Christian Weigand Heike Leutheuser Björn M. Eskofier Georg Fischer Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Digital Sports Group Pattern Recognition Lab University Erlangen-Nürnberg (FAU) Germany Institute for Electronics Engineering University Erlangen-Nürnberg (FAU) Germany

Long battery runtime is one of the most wanted properties of wearable sensor systems. the sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. they all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. the Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.

关键词： Finite impulse response filters Signal processing algorithms Computational complexity Biomedical signal processing Electrocardiography Mathematical model Approximation methods

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Deep Learning applications in Histopathological Images 12th

Deep Learning Applications in Histopathological Images

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12th international conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Pereira, Luis Felipe Rocha de Paiva, Anselmo Cardoso Araújo, Alexandre de Carvalho Junior, Geraldo Braz de Almeida, Joao Dallyson Sousa Silva, Aristófanes Corrêa Universidade Federal do Maranhão UFMA Av. dos Portugueses 1966 - Vila Bacanga MA São Luís65080-805 Brazil Núcleo de Computação Aplicada NCA Av. dos Portugueses 1966 - Vila Bacanga MA São Luís65080-805 Brazil Departamento de Informática Deinf Campus Dom Delgado UFMA Av. dos Portugueses 1966 - Vila Bacanga MA São Luís65080-805 Brazil

ISBN: (纸本)9783031606649

Breast cancer is a neoplasm that mainly affects women above the age of 45. However, an increase in the incidence of this disease among young women has been observed. Although it is considered a cancer with a good prognosis when diagnosed early, early detection remains a challenge. In Brazil, the mortality rate due to breast cancer remains high, which is directly related to the late diagnosis of the disease. To contribute to the reduction of this rate, the development of effective early detection techniques is essential. these techniques can assist in diagnosing the disease at its initial stages, enabling quicker treatment and thereby increasing the chances of a cure. Computer-aided detection and diagnosis systems have been developed and improved in the field of computing. these systems base their accuracy and reasoning on data obtained through a combination of computer vision techniques, such as pattern recognition and machine learning. When applied, these techniques assist doctors and specialists in data analysis to provide diagnostic support and treatment planning. this significantly enhances a patient’s chances of recovery. More recently, within the machine learning field, Deep Learning has become a prevalent focus of research due to its ability to automatically extract relevant features for the target task. In this work, the methodology proposed employs Convolutional Neural Networks for machine learning. While the results obtained are not superior to those in the literature, they are close and generally require fewer computational resources for training the selected networks after the selection process. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Diseases

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