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检索条件"任意字段=International Workshop on Intelligent Computer in Pattern Analysis/Synthesis"

共 294 条记录，以下是11-20 订阅

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The problem of convergence of classifiers construction procedure in the schemes of logical and algorithmic classification trees 5

The problem of convergence of classifiers construction proce...

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5th international workshop on computer Modeling and intelligent Systems, CMIS 2022

作者： Povkhan, Igor Mulesa, Oksana Melnyk, Olena Bilak, Yuriy Polishchuk, Volodymyr Uzhhorod National University Zankoveckoy str. 89B Uzhhorod88000 Ukraine

The paper considers the problem of convergence in the procedure of classifier schemes synthesis by methods of logical and algorithmic classification trees. It suggests the upper evaluation of complexity for the scheme of algorithms tree in the problem of approximation of real data array by a set of generalized features with a fixed criterion of termination of the branching procedure at the stage of the classification tree construction. This approach provides the required accuracy of the model, evaluates its complexity, reduces the number of branches, and achieves the required performance features. The paper represents the evaluation of convergence for the procedure of recognition schemes construction is logical and algorithmic classification trees on conditions of weak and robust separation of primary initial sampling classes. © 2022 Copyright for this paper by its authors.

关键词： pattern recognition

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Space Environmental Health: Non-Invasive Holographic Imaging Flow Cytometry for Astronauts Biodosimetry

Space Environmental Health: Non-Invasive Holographic Imaging...

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2024 IEEE international workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Mottareale, Rocco Pirone, Daniele Miccio, Lisa Bianco, Vittorio Memmolo, Pasquale Durante, Marco Pugliese, Mariagabriella Ferraro, Pietro University of Naples Federico II Department of Physics 'E. Pancini' Naples Italy Institute of Applied Sciences and Intelligent Systems 'E. Caianiello' CNR-ISASI Pozzuoli Naples Italy GSI Helmholtzzentrum Für Institute of Condensed Matter Physics Technische Universität Biophysics Department Darmstadt Germany

ISBN: (纸本)9798350385014

Ionizing radiation exposure defines one of the main risks in the context of space environmental health for astronauts of the international Space Station (ISS) involved in long duration missions. The Galactic Cosmic Radiation (GCR) constitutes a primary source of radiation made of energetic protons and heavy ions, protons and electrons trapped in the Earth's magnetic field and protons from infrequent solar particle events (SPEs). A secondary source of radiation arises from neutrons and light charged particles, created by interactions of high-charge (Z) and energy (E}) nuclei (HZE particles) with spacecraft shielding and with the human body itself. The existence of such a heterogeneous exposure scenario causes astronauts to undergo effective doses accumulations at a rate of 0.2 to 0.5 mSv or more each day of a mission. This causes high risks of circulatory diseases, central nervous system effects and cancer progression. In this context, biodosimetry initiatives have been implemented to extrapolate organ dose equivalents or risk assessments via direct quantification of cytogenetic aberrations in the peripheral blood lymphocytes of crew members. These datasets offer the distinct advantage of incorporating individual radiosensitivity alongside the effects of microgravity and other stressors. In this framework, the Tomographic Phase Microscopy in Flow-Cytometry (TPM-FC) is proposed as a non-invasive holographic imaging flow cytometry to evaluate the effect of ionizing radiations on blood cells. The technique conjugates the high-throughput analysis typically achievable in flow-cytometry and the label-free tomographic imaging based on digital holographic recording in microscopy configuration. The reconstruction of the interference fringe pattern allows to decode the 3D spatial distribution of the single cell's refractive indexes (RIs). The method proposed here belongs to the Quantitative Phase Imaging (QPI) techniques that enable to detect morphological alterations into irr

关键词： Refractive index

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Adaptive Instance Distillation for Object Detection in Autonomous Driving 26

Adaptive Instance Distillation for Object Detection in Auton...

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26th international Conference on pattern Recognition / 8th international workshop on Image Mining - Theory and Applications (IMTA)

作者： Lan, Qizhen Tian, Qing Bowling Green State Univ Dept Comp Sci Bowling Green OH 43403 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In recent years, knowledge distillation (KD) has been widely used to derive efficient models. Through imitating a large teacher model, a lightweight student model can achieve comparable performance with more efficiency. However, most existing knowledge distillation methods are focused on classification tasks. Only a limited number of studies have applied knowledge distillation to object detection, especially in time-sensitive autonomous driving scenarios. In this paper, we propose Adaptive Instance Distillation (AID) to selectively impart teacher's knowledge to the student to improve the performance of knowledge distillation. Unlike previous KD methods that treat all instances equally, our AID can attentively adjust the distillation weights of instances based on the teacher model's prediction loss. We verified the effectiveness of our AID method through experiments on the KITTI and the COCO traffic datasets. The results show that our method improves the performance of state-of-the-art attention-guided and non-local distillation methods and achieves better distillation results on both single-stage and two-stage detectors. Compared to the baseline, our AID led to an average of 2.7% and 2.1% mAP increases for single-stage and two-stage detectors, respectively. Furthermore, our AID is also shown to be useful for self-distillation to improve the teacher model's performance.

关键词： Adaptation models Detectors Object detection Predictive models pattern recognition Task analysis Autonomous vehicles

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6th international workshop on computer Modeling and intelligent Systems, CMIS 2023

作者： Shtovba, Serhiy Petrychko, Mykola Shtovba, Olena Vasyl’ Stus Donetsk National University 600-richchia str. 21 Vinnytsia21021 Ukraine Vinnytsia National Technical University Khmelnytske Shose 95 Vinnytsia21021 Ukraine

Estimating a level of similarity of two objects is a common problem in pattern recognition, clustering, and classification. Among these problems can be reviewer recommendation, similar text documents analysis, human pose detection in video, species distribution clustering, recommendation in internet-shops etc. In case of categorical attributes an object is described as a distribution of membership degrees over categories. Similarity metrics of such distributions are usually defined as a superposition of objects’ similarities for each category. Most often it is a sum of similarities in separate categories. In addition to that each category is considered independently and in isolation from the others. Some practical problems have categories that are akin. Therefore, it is expedient to consider objects’ similarity not only directly, as a similarity between equivalent categories, but it is also necessary to consider an indirect similarity, cross-similarity through akin categories. It is such similarity metric of two categorical distributions that accounts for the kinship of different categories is proposed in this paper. The metric has two components. The first component is defined as Czekanowski metric. It defines a direct similarity of categorical distributions as a sum of intersection of distributions’ membership degrees of two objects. After the intersection the remaining residuals are accounted for in the second component of the metric. The second metric’s component is defined as element-wise product of two matrices: matrix of residuals composition from memberships of two categorical distributions and matrix of categories’ paired kinship. It is assumed that kinship indices for each pair of categories are known. As a result, with a large number of categories the overall noisy contribution from weakly akin categories is prominent. Therefore, it is proposed to filter the noise and account only for contribution from strongly akin categories. © 2023 Copyright for this p

关键词： pattern recognition

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Swimmer Position Estimation and Stroke analysis Based on Head Recognition

Swimmer Position Estimation and Stroke Analysis Based on Hea...

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international workshop on Advanced Imaging Technology (IWAIT)

作者： Ishikawa, Mayuko Shishido, Hidehiko Kameda, Yoshinari Univ Tsukuba Grad Sch Syst & Informat Engn Dept Intelligent Interact Technol Tsukuba Ibaraki Japan Univ Tsukuba Ctr Computat Sci Tsukuba Ibaraki Japan

ISBN: (数字)9781510653320

ISBN: (纸本)9781510653320;9781510653313

In the field of competitive swimming, performance investigation on official games is important and useful for performance development. We have been working on swimmer position estimation from a wide video view of official games. The challenges are water splash and complicated reflection of light that may hide swimmers from the camera. To overcome these problems, we utilize YOLOv3 and prepare a dedicated dataset of swimmer's heads in real games. We have trained the YOLOv3 and the trained YOLOv3 can detect heads by 48.1 %mAP. In addition to the position estimation, we also propose a new method to investigate the status of the strokes along the time by detecting two head classes: over the water and under the water. We also prepare another dedicated dataset for this two-class training. With the trained YOLOv3, we successfully visualize the status change of a swimmer over a whole game.

关键词： head detection YOLO Athlete 50-meter pool computer vision performance analysis

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DroBoost: An intelligent Score and Model Boosting Method for Drone Detection 21st

DroBoost: An Intelligent Score and Model Boosting Method for...

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21st international Conference on Image analysis and Processing (ICIAP)

作者： Eryuksel, Ogulcan Ozfuttu, Kamil Anil Akyon, Fatih Cagatay Sahin, Kadir Buyukborekci, Efe Cavusoglu, Devrim Altinuc, Sinan OBSS Technol OBSS AI 1606 Cad 4-1-307 Cyberpk Cyberplaza C Blok 3 Kat Ankara Turkey

ISBN: (纸本)9783031133244;9783031133237

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).

关键词： Drone detection Deep learning Object tracking Object detection Synthetic data

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Functional Electromagnetic Signatures from Complex Volume Systems and Topological Material Properties

Functional Electromagnetic Signatures from Complex Volume Sy...

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2025 international Conference on Mobile and Miniaturized Terahertz Systems, ICMMTS 2025

作者： Erni, Daniel Degen, Marvin Jerbic, Kevin Svejda, Jan Taro Rennings, Andreas Jandieri, Vakhtang Faculty of Engineering Duisburg47048 Germany Free University of Tbilisi Department of Electrical and Computer Engineering Tbilisi0159 Georgia

ISBN: (纸本)9798350365832

This scientific paper is an outcome of the MARIE project (for more information, see https://***) [M03]. The contribution reflects our ongoing and planned research in the electromagnetic analysis and functionalization of complex surface systems and volume scattering structures at mm-wave up to THz frequencies. The research relies significantly upon the development of an ultra-fast, full-wave, electromagnetic (EM) simulator based on the Recursive Aggregated Centered T-Matrix Algorithm (RACTMA). Building on this foundation, we are now focusing on the functionalization of EM responses and signatures to enable the analysis and synthesis of reconfigurable volumetric structures through ultra-fast inverse problem solutions. An inverse solution is given in the framework of plant stem monitoring. This effort involves accelerating RACTMA via an n-layer T-matrix algorithm and leveraging massive parallelization in CPU/GPU implementations. The enhanced RACTMA frame-work will incorporate novel topological material representations, such as multiferroic and ferro-electric properties, to enable new EM responses and unlock unexpected device functionalities. A target application includes outdoor relaying using microfluidic reconfigurable scattering posts as realizations of reconfigurable intelligent volumes (RIV). © 2025 IEEE.

关键词： Surface properties

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1st international Virtual Conference on Visual pattern Extraction and Recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st international Virtual Conference on Visual pattern Extraction and Recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st international Virtual Conference on Visual pattern Extraction and Recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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CMIS 2021 - Proceedings of the 4th international workshop on computer Modeling and intelligent Systems

CMIS 2021 - Proceedings of the 4th International Workshop on...

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4th international workshop on computer Modeling and intelligent Systems, CMIS 2021

The proceedings contain 44 papers. The topics discussed include: long-term forecasting method in the supply chain based on an artificial neural network with multi-agent metaheuristic training;adaptive learning of evolving hyper basis function neural network;intelligent big data system based on scientific machine learning of cyber-physical systems of medical and biological processes;homogeneity hypothesis in discriminant analysis;implementation of probabilistic data structures in the processes of neuroevolutionary synthesis;data mining methods for evaluation and forecasting the mobile internet traffic in roaming;method for visual video defects detection using machine learning;and on classification hidden concepts language in specialized texts based on methods of the intellectual data processing.

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Functional Electromagnetic Signatures from Complex Volume Systems and Topological Material Properties

Functional Electromagnetic Signatures from Complex Volume Sy...

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international workshop on Mobile Terahertz Systems (IWMTS)

作者： Daniel Erni Marvin Degen Kevin Jerbic Jan Taro Svejda Andreas Rennings Vakhtang Jandieri General and Theoretical Electrical Engineering (ATE) Faculty of Engineering University of Duisburg-Essen and Center of Nanointegration Duisburg-Essen (CENIDE) Duisburg Germany Department of Electrical and Computer Engineering Free University of Tbilisi Tbilisi Georgia

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

关键词： Inverse problems Electromagnetic scattering Buildings Beams Focusing Electromagnetic analysis Surface treatment Monitoring Microfluidics Material properties

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