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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91-100 订阅

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Robust Stereo Matching with an Unfixed and Adaptive Disparity Search Range 26

Robust Stereo Matching with an Unfixed and Adaptive Disparit...

引用

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

作者： Liu, Jiazhi Liu, Feng Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Stereo matching is an essential basis for various applications in computer vision, but currently, most stereo matching methods have poor generalization performance and require a fixed disparity search range. In this work, we propose to adopt the operation "sub-pixel three-around-maximum", which supports an unfixed disparity search range, to replace the currently popular operation soft argmax. We also propose to directly supervise the feature extractor by three loss functions. Just depending on the feature extractor, we can obtain an accurate semi-dense disparity map before cost aggregation, which can help to remove adaptively the redundancy part of the predefined disparity search range. An adaptive disparity search range for each stereo pair can save much time and memory. the proposed architecture achieves state-of-the-art cross-domain generalization performance in the datasets KITTI2012&2015, and experimental results demonstrate that our method supports unfixed and adaptive disparity search range.

关键词： computer vision Costs Redundancy Memory architecture Feature extraction pattern recognition

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Efficient and Flexible Sublabel-Accurate Energy Minimization 26

Efficient and Flexible Sublabel-Accurate Energy Minimization

引用

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

作者： Nurlanov, Zhakshylyk Cremers, Daniel Bernard, Florian Univ Bonn Bonn Germany Tech Univ Munich Munich Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recognition. While discrete optimization methods are able to give theoretical optimality guarantees, they can only handle a finite number of labels and therefore suffer from label discretization bias. Existing continuous optimization methods can find sublabel-accurate solutions, but they are not efficient for large label spaces. In this work, we propose an efficient sublabel-accurate method that utilizes the best properties of both continuous and discrete models. We separate the problem into two sequential steps: (i) global discrete optimization for selecting the label range, and (ii) efficient continuous sublabel-accurate local refinement of a convex approximation of the energy function in the chosen range. Doing so allows us to achieve a boost in time and memory efficiency while practically keeping the accuracy at the same level as continuous convex relaxation methods, and in addition, providing theoretical optimality guarantees at the level of discrete methods. Finally, we show the flexibility of the proposed approach to general pairwise smoothness terms, so that it is applicable to a wide range of regularizations. Experiments on the illustrating example of the image denoising problem demonstrate the properties of the proposed method. the code reproducing experiments is available on https://***/nurlanov-zh/sublabel-accurate-alpha-expansion.

关键词： computer vision Codes Memory management Optimization methods Machine learning Minimization Relaxation methods

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MedViTGAN: End-to-End Conditional GAN for Histopathology Image Augmentation with vision Transformers 26

MedViTGAN: End-to-End Conditional GAN for Histopathology Ima...

引用

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

作者： Li, Meng Li, Chaoyi Hobson, Peter Jennings, Tony Lovell, Brian C. Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia Sullivan Nicolaides Pathol Bowen Hills Qld 4006 Australia

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Deep learning networks have demonstrated competitive performance for various tasks on medical images. However, obtaining promising results requires a large amount of annotated data for supervised training, which is labor-intensive. Recently, the increasing interest in transformers has suggested their robust performance on computer vision tasks, including generative adversarial networks (GANs). In this paper, we propose a conditional GAN built on pure transformer-based architectures, named MedViTGAN, to assist in generating synthetic histopathology images for data augmentation in an end-to-end manner. the presented model adopts a conditioned training strategy by incorporating a transformer-based auxiliary classifier to facilitate the discriminative image generation process. We further introduce an adaptive hybrid loss weighting mechanism to balance multiple losses over sources and classes to stabilize the training. Extensive experiments on the histopathology datasets show that leveraging MedViTGAN generated images results in a significant and consistent improvement in classification performance.

关键词： Generative adversarial networks Deep learning Histopathology medical image analysis Classification vision transformer

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A Hybrid Model for Recognizing Human Activity Using Extreme Learning 26

A Hybrid Model for Recognizing Human Activity Using Extreme ...

引用

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

作者： Rahman, Arafat Nag, Anindya Bairagi, Anupam Kumar Khulna University Computer Science & Engineering Discipline Khulna9208 Bangladesh

ISBN: (纸本)9798350359015

Human activity recognition (HAR) refers to the cognitive process of identifying and labeling various actions individuals perform. HAR has garnered significant attention in recent years due to its multifaceted applications in healthcare, security, human-computer interaction domains, etc. Despite several works for recognizing HAR, this still needs to be improved due to its real-time implications, complex environment, and privacy and ethical issues. this study aims to develop a model for HAR that can accurately differentiate and categorize different human actions from sequences of video images. For this purpose, a Local Binary pattern (LBP)-based Extreme Learning Machine (ELM) is utilized. Experimental results show the quality of the proposed model over other comparing models in terms of accuracy, precision, recall, and F1-score. © 2023 ieee.

关键词： Extreme Learning HAR Local Binary patterns Support vector machine

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AdaDeId: Adjust Your Identity Attribute Freely 26

AdaDeId: Adjust Your Identity Attribute Freely

引用

26th International conference on pattern recognition, ICPR 2022

作者： Ma, Tianxiang Li, Dongze Wang, Wei Dong, Jing University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences CRIPAC & NLPR Institute of Automation China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Face de-identification has drawn increasing attention in recent years. It is important to protect people's identity information meanwhile keeping the utility of the face data in many computer vision tasks. We propose a Adaptive De-identification (AdaDeId) method, a novel approach that can freely manipulate the identity attributes of given faces. We introduce an identity decoupling representation learning method, which is based on the autoencoder decoupling model as well as our proposed Identity Decoupling Representation (IDR) loss and Content Retention (CR) loss. Our method encodes the identity information of a face into a unit spherical space, where we can continuously manipulate the identity representation vector. Various de-identified faces derived from an original face can be generated through our method and maintain high similarity to the original image contents. Quantitative and qualitative experiments demonstrate our method achieves state-of-the-art on visual quality and de-identification validity. © 2022 ieee.

关键词： computer vision

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SIFT-based Approach for Dorsal Hand Veins Images recognition 26

SIFT-based Approach for Dorsal Hand Veins Images Recognition

引用

26th International conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Egorov, Dobroslav P. Mizinov, Pavel V. Kravchenko, Oleg V. Konnova, Natalia S. Osipenko, Gavriil V. Kotel'nikov Institute of Radio Engineering and Electronics of Ras Moscow Russia Bauman Moscow State Technical University Department of Information Security Moscow Russia Computer Science and Control of Ras Federal Research Center Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350361728

In this paper we present a system for person identification using dorsal hand veins pattern images. A simple approach is developed based on scale-invariant feature transform (SIFT) method for image features extraction, fast approximate nearest neighbors algorithm (FLANN) for keypoints matching in a preselected region-of-interest (ROI), and subsequent utilization of dense layer neural network for accurate friend-or-foe predictions. the efficiency of the considered approach is evaluated using an open dataset, and confusion matrices are calculated. the obtained results confirm again that dorsal hand veins identification systems are particularly promising for high-security settings. the proposed approach is implemented in software for Orange pi single-board computers. © 2024 ieee.

关键词： Nearest neighbor search

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Person Adapted Emotion recognition using Deep Learning 26

Person Adapted Emotion Recognition using Deep Learning

引用

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

作者： Sikder, Md Hafiz Shihavuddin, A.S.M. Maruf, Md. Hasan Saaduzzaman, D.M. Rahman, Md. Ashiqur Hasan, Muhammad Abul Green University of Bangladesh Department of EEE Bangladesh Green University of Bangladesh Department of CSE Bangladesh

ISBN: (纸本)9798350359015

Effective communication with humans can be extremely complex and exciting at the same time. through digital representation and using emotion-based visual cues, humans use a communication methodology. In today's world, it has become important for machines to understand these visual cues effectively for possible applications. In recent days, AI has become quite effective in detecting different human emotions with higher accuracy. However, in most of them, AI-based models are generalized for humans sacrificing performance. In this work, a person-adapted model for face detection is proposed as usable in most applications achieving much higher accuracy with fewer examples compared to the general ones. the study found promising results in detecting facial emotion using CNN-based Transfer Learning with a baseline ResNet - 50 architecture. For the experimentation, this work also produced a new person-adapted dataset covering rare emotion classes. © 2023 ieee.

关键词： computer vision Deep Learning Emotion Detection Facial Emotion recognition Transfer Learning

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Aggregating Global Features into Local vision Transformer 26

Aggregating Global Features into Local Vision Transformer

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

作者： Patel, Krushi Bur, Andres M. Li, Fengjun Wang, Guanghui Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Univ Kansas Dept Otolaryngol Head & Neck Surg Kansas City KS 66160 USA Ryerson Univ Dept Comp Sci Toronto ON M5B 2K3 Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Local Transformer-based classification models have recently achieved promising results with relatively low computational costs. However, the effect of aggregating spatial global information of local Transformer-based architecture is not clear. this work investigates the outcome of applying a global attention-based module named multi-resolution overlapped attention (MOA) in the local window-based transformer after each stage. the proposed MOA employs slightly larger and overlapped patches in the key to enable neighborhood pixel information transmission, which leads to significant performance gain. In addition, we thoroughly investigate the effect of the dimension of essential architecture components through extensive experiments and discover an optimum architecture design. Extensive experimental results CIFAR-10, CIFAR-100, and ImageNet-1K datasets demonstrate that the proposed approach outperforms previous vision Transformers with a comparatively fewer number of parameters. the source code and models are publicly available at: https://***/krushi1992/MOA-transformer

关键词： Source coding Computational modeling computer architecture Information processing Performance gain Transformers pattern recognition

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Improving Weakly Supervised Scene Graph Parsing through Object Grounding 26

Improving Weakly Supervised Scene Graph Parsing through Obje...

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

作者： Zhang, Yizhou Zheng, Zhaoheng Nevatia, Ram Liu, Yan Univ Southern Calif Angeles Dept Comp Sci Los Angeles CA 90089 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Weakly supervised scene graph parsing, which learns structured image representations without annotated correspondences between graph nodes and visual objects, has been prevalent in recent computer vision research. Existing methods mainly focus on designing task-specific loss functions, model architectures, or optimization algorithms. We argue that correspondences between objects and graph nodes are crucial for the weakly supervised scene graph parsing task and are worth learning explicitly. thus we propose GroParser, a framework that improves weakly supervised scene graph parsing models by grounding visual objects. the proposed weakly supervised grounding method learns a metric among visual objects and scene graph nodes by incorporating information from both object features and relational features. Specifically, we apply multi-instance learning to learn the object category information and exploit a two-stream graph neural network to model the relational similarity metric. Extensive experiments on the scene graph parsing task verify the grounding found by our model can reinforce the performance of the existing weakly supervised scene graph parsing methods, including the current state-of-the-art. Further experiments on Visual Genome (VG) and Visual Relation Detection (VRD) datasets verify that our model brings an improvement on scene graph grounding task over existing approaches.

关键词： Measurement Visualization computer vision Grounding Genomics Image representation Graph neural networks

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HRI30: An Action recognition Dataset for Industrial Human-Robot Interaction 26

HRI30: An Action Recognition Dataset for Industrial Human-Ro...

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

作者： Iodice, Francesco De Momi, Elena Ajoudani, Arash IIT Human Robot Interfaces & Phys Interact HRI2 Lab Genoa Italy Politecn Milan Dept Elect Informat & Bioengn Milan Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the past years, action recognition techniques have gained significant attention in computer vision and robotics research. Nevertheless, their performances in realistic applications, despite dedicated efforts to collect and annotate medium/large datasets, remain far from satisfactory, especially when it comes to applications in the field of human-robot collaboration. In response to this shortfall, we create a dataset not dispersive in its classes but sectoral, i.e., dedicated exclusively to the industrial environment and human-robot collaboration. Specifically, we describe our ongoing collection of the 'HRI30' database for industrial action recognition from videos, containing 30 categories of industrial-like actions and 2940 manually annotated clips. We test our dataset on multiple action detection approaches and compare it with the HMDB51 and UCF101 public datasets using the best-performing approach. We define a baseline of 86.55% Top-1 accuracy and 99.76% Top-5 accuracy, hoping that this dataset will encourage research towards understanding actions in collaborative industrial scenarios. the dataset can be downloaded at the following link: 10.5281/zenodo.5833411

关键词： Databases Machine vision Collaboration Human-robot interaction Training data pattern recognition Fourth Industrial Revolution

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