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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是101-110 订阅

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Towards Robotic Object recognition and Pick and Place: A Simulation-Based Approach with the KUKA KR5 HW-2 26

Towards Robotic Object Recognition and Pick and Place: A Sim...

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2024 ieee International conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Osorio-Camparán, Roman Fernández-Ávila, Luis E. López-Juárez, Ismael Peña, Mario Ahumada, Cristian Lefranc, Gastón Universidad Nacional Autónoma de México Iimas Unam Ciudad de México Mexico Universidad Nacional Autónoma de México Facultad de Ingeniería Mecatrónica Ciudad de México Mexico Universidad Autónoma de Yucatán Mérida Cinvestav Coah. México Saltillo Mexico Universidad Técnica Federico Santa María Depto.. Electrotecnia e Informática Valparaíso Chile Pontificia Universidad Católica Facultad de Ingeniería Valparaíso Chile

ISBN: (纸本)9798350378115

this paper presents a simulation-based approach to robotic object recognition and rearrangement using the KUKA KR5 HW-2 manipulator. the research focuses on developing and implementing advanced algorithms for object detection, classification, and manipulation within a virtual environment. the KUKA KR5 HW-2, a versatile industrial robot, is modelled in the simulation to perform complex pick and place operations. It emphasizes the integration of computer vision techniques with the robot's kinematic model to enhance its ability to recognize various object geometries and efficiently plan rearrangement tasks. the simulation environment allows for rapid prototyping and testing of different scenarios, enabling the optimization of trajectory planning and grip strategies. Our results demonstrate significant improvements in the manipulator's adaptability to diverse object types and arrangement patterns. this work contributes to the advancement of flexible automation in manufacturing and logistics, offering insights into the potential of simulation-based development for robotic manipulation tasks. © 2024 ieee.

关键词： Robot programming

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InfAnFace: Bridging the Infant-Adult Domain Gap in Facial Landmark Estimation in the Wild 26

InfAnFace: Bridging the Infant-Adult Domain Gap in Facial La...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Wan, Michael Zhu, Shaotong Luan, Lingfei Prateek, Gulati Huang, Xiaofei Schwartz-Mette, Rebecca Hayes, Marie Zimmerman, Emily Ostadabbas, Sarah Roux Inst Portland ME USA Northeastern Univ Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related task of facial detection for infants, and also on a challenging case study of infrared baby monitor images gathered by our lab as part of in-field research into the aforementioned developmental issues(1)

关键词： Facial landmark estimation domain adaptation prodromal risk screening computer vision

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Automated Car Parking Using Advanced Image recognition and License Validation 26

Automated Car Parking Using Advanced Image Recognition and L...

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Adnan Ahmed, Foisal Bhuiyan, Md Sadman Shakif Talha, Md Abu Ahmad, Shameem Islam, Md Mazharul Mannan, Lamia Rahman, Md Abdur Dhaka1229 Bangladesh

ISBN: (纸本)9798350359015

this study presents a novel approach in the form of a fully automated car parking management system, integrating IoT, computer vision, and database technologies to alleviate urban mobility challenges. Employing vehicle presence sensors and computer vision techniques for Bengali number-plate detection, the system enables real-time monitoring and management of parking spaces. Upon vehicle detection, the system cross-checks the license plate against a centralized database to confirm parking eligibility. Additionally, the integration of a mobile app and website interface provides drivers with instant updates on parking slot availability. this dual-layered IoT system not only restricts unauthorized access, minimizing illegal parking but also significantly reduces the time spent searching for parking, enhancing user experience. Consequently, this innovative solution contributes to reduced traffic congestion, fostering seamless urban mobility. By amalgamating automation, user-centric design, and efficient management, this system promises a transformative parking experience for both drivers and facility operators, revolutionizing urban parking paradigms. © 2023 ieee.

关键词： Automation Car Parking Image processing IoT OCR Real-time Database

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Episodic Projection Network for Out-of-Distribution Detection in Few-shot Learning 26

Episodic Projection Network for Out-of-Distribution Detectio...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Fan, Jiawei Ou, Zhonghong Yu, Xie Yang, Junwei Wang, Shigeng Kang, Xiaoyang Zhang, Hongxing Song, Meina Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the increasing demands of safety-critical computer vision applications have attracted extensive research on Out-of-Distribution (OOD) detection in recent years. Nevertheless, a large proportion of real-world tasks are in low-data regime, and the gap between meta-learning paradigm and OOD detection mechanism causes low performance in few-shot settings. In order to bridge the gap, we first propose an simple yet effective Episodic Projection Scheme (EPS). EPS is designed to project feature vectors to task-specific feature space for OOD detection, without sacrificing generalization of few-shot models. We then construct a multi-modal representation space for few-shot OOD detection by employing representations of the labels and their synonyms. At last, we put forward a few-shot OOD detection framework named Episodic Projection Network (EPN), which can integrate many kinds of perturbation based OOD algorithms with ease. To verify effectiveness of the proposed scheme, we implement several OOD algorithms into EPN and conduct experiments on two few-shot classification datasets, i.e., Omniglot and mini-ImageNet. Experimental results demonstrate that accuracy has been increased by 5% by integrating the OOD algorithms into the EPN framework.

关键词： computer vision Semantic segmentation Perturbation methods Neural networks Object detection Feature extraction Classification algorithms

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Report on the 29th ieee conference on computer vision and pattern recognition (cvpr 2016)

Report on the 29th IEEE conference on computer vision and pa...

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作者： Uranishi, Yuki Cybermedia Center Osaka University Osaka Japan

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Machine Learning Based Effective Approach for DNA pattern recognition 26

Machine Learning Based Effective Approach for DNA Pattern Re...

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Md. Rokibul Tabassum, Fariya Afrin, Zakia Rajshahi University of Engineering and Technology Department of Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Due to the length as well as the complexity of DNA sequences, recognizing patterns in DNA is an exhausting task. For this purpose, machine learning techniques have been proven to be successful, although they often need a lot of training data. I'm developing a machine-learning strategy for identifying DNA patterns. My objective is to create a technique that can swiftly spot patterns throughout DNA sequences. this might be used to discover novel genes, create new diagnostic tests, or understand how DNA sequences have evolved. the method combines an effective data processing technique with some machine learning algorithms. the processing of the DNA sequence dataset is utilized to learn the connections between the characteristics that the pattern-matching algorithm extracts from the DNA sequences and the intended output. A simple approach that can rapidly find patterns in DNA sequences is the effective process of the dataset of DNA sequences. A more advanced algorithm that may develop more intricate connections between the input data and the desired result is the deep neural network or machine learning. this approach was selected considering it is more effective and precise than current approaches. Because it doesn't need a lot of training data, it is more effective. Because it can discover more intricate connections between the attributes and the intended result, it is more accurate. © 2023 ieee.

关键词： Bag of words K-mers Naive Bayes Algorithm Random Forest Algorithm Support Vector Machine Algorithm for DNA pattern recognition

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Explanation-based Graph Neural Networks for Graph Classification 26

Explanation-based Graph Neural Networks for Graph Classifica...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Seo, Sangwoo Jung, Seungjun Kim, Changick Korea Adv Inst Sci & Technol KAIST Sch Elect Engn 291 Daehak Ro Daejeon 34141 South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.

关键词： Proteins computer vision Analytical models Machine learning Benchmark testing Graph neural networks Data models

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Characterizing Cell Shape Distributions Using k-Mode Kernel Mixtures 26

Characterizing Cell Shape Distributions Using k-Mode Kernel ...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Deng, Ximu Srivastava, Anuj Sarkar, Rituparna Labruyere, Elisabeth Olivo-Marin, Jean-Christophe Florida State Univ Dept Stat Tallahassee FL 32306 USA Inst Pasteur Dept Cell Biol & Infect Bioimage Anal Unit Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

this paper addresses the problem of characterizing statistical distributions of cellular shape populations using shape samples from microscopy image data. this problem is challenging because of the nonlinearity and high-dimensionality of shape manifolds. the paper develops an efficient, nonparametric approach using ideas from k-modal mixtures and kernel estimators. It uses elastic shape analysis of cell boundaries to estimate statistical modes and clusters given shapes around those modes. (Notably, it uses a combination of modal distributions and ANOVA to determine k automatically.) A population is then characterized as k-modal mixture relative to this estimated clustering and a chosen kernel (e.g., a Gaussian or a flat kernel). One can compare and analyze populations using the Fisher-Rao metric between their estimated distributions. We demonstrate this approach for classifying shapes associated with migrations of entamoeba histolytica under different experimental conditions. this framework remarkably captures salient shape patterns and separates shape data for different experimental settings, even when it is difficult to discern class differences visually.

关键词： Manifolds Measurement computer vision Shape Microscopy Sociology Statistical distributions

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Facial Expression recognition using Recent Convolutional Neural Network Models 26

Facial Expression Recognition using Recent Convolutional Neu...

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Yenilmez, Fulya Yildiz, Canan Ugur, Mukden Turkish-German University Department of Computer Engineering Istanbul34820 Turkey Turkish-German University Department of Robotics and Intelligent Systems Istanbul34820 Turkey

ISBN: (纸本)9798350359015

In recent years, deep learning architectures have demonstrated remarkable achievements on diverse computer vision tasks, including the recognition of facial expressions. the development of precise and robust facial expression recognition (FER) models has the potential to improve a wide range of applications in various fields, such as human-computer interaction, emotion analysis, marketing, robotics, psychology, and health *** this domain, several Convolutional Neural Network (CNN) architectures have been employed to yield promising results. In this paper, we undertake a comparative analysis with the latest iterations of five widely adopted CNN models for the FER task: VGG19, InceptionV3, ResNet152V2, MobileNetV2, and EfficientNetV2B1. We also explore the effect of transfer learning, specifically the difference between pretraining on VGGFace versus ImageNet. Our primary goal is to comprehensively assess these architectures under identical conditions and provide clarity on their performance in comparison to each other and to previous works using various training strategies. Our assessments allow the conclusion that increasing the size and depth of these common "backbone"models leads to only minor improvements. Augmenting these models with additional architectural elements such as attention mechanisms seems much more promising in comparison. Our results further show that pretraining on a domain-specific dataset can lead to significant improvements, demonstrating the importance of a large FER related dataset. © 2023 ieee.

关键词： CNN architectures face expression recognition fine-tuning neural networks transfer learning

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ComDensE Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion 26

ComDensE Combined Dense Embedding of Relation-aware and Comm...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Kim, Minsang Baek, Seungjun Kakao Enterprise Seongnam South Korea Korea Univ Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Real-world knowledge graphs (KG) are mostly incomplete. the problem of recovering missing relations, called KG completion, has recently become an active research area. Knowledge graph (KG) embedding, a low-dimensional representation of entities and relations, is the crucial technique for KG completion. Convolutional neural networks in models such as ConvE, SACN, InteractE, and RGCN achieve recent successes. this paper takes a different architectural view and proposes ComDensE which combines relation-aware and common features using dense neural networks. In the relation-aware feature extraction, we attempt to create relational inductive bias by applying an encoding function specilic to each relation. In the common feature extraction, we apply the common encoding function to all input embeddings. these encoding functions are implemented using dense layers in ComDensE. ComDensE achieves the state-of-the-art performance in the link prediction in terms of MRR, HIT@1 on FB15k-237 and HIT@1 on WN18RR compared to the previous baseline approaches. We conduct an extensive ablation study to examine the effects of the relation-aware layer and the common layer of the ComDensE. Experimental results illustrate that the combined dense architecture as implemented in ComDensE achieves the best performance.

关键词： computer vision Computational modeling Neural networks computer architecture Feature extraction Encoding Natural language processing

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