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

共 52943 条记录，以下是4711-4720 订阅

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A computer-aided diagnosis system based on feature extraction enhanced multiple instance learning 5

A computer-aided diagnosis system based on feature extractio...

引用

5th International conference on computer vision, Image and Deep Learning, CVIDL 2024

作者： Li, Bingle Zhang, Jinhao Shi, Changfeng Northeastern University College of Medicine and Biological Information Engineering Shenyang China

ISBN: (纸本)9798350373820

Digital pathology has revolutionized cancer diagnosis by transitioning from traditional microscopic examination to the analysis of whole slide images (WSIs). This paper focuses on the application of computer-aided diagnosis systems in the detection of skin melanoma, a cancer with a significant impact on younger demographics and a sharp drop in survival rates upon metastasis. We propose a novel Feature Extraction Enhanced Multiple Instance Learning (Fee-MIL) approach to address the challenges associated with the digital transition in pathology, particularly the scarcity of annotated datasets and the large size of WSIs. Our methodology combines weakly supervised learning and self-supervision learning to enhance the recognition of tumor patches within WSIs. By employing a dual-channel feature extraction enhancement mechanism, our model significantly improves the identification of malignant tumor patches, leading to more accurate classification outcomes. The Fee-MIL model is evaluated on the TCIA-CM dataset and compared with several state-of-the-art algorithms, demonstrating superior performance in melanoma WSI classification. We have already made the code publicly available at https://***/reprocess4733/Fee-MIL to facilitate further research and development in the field. © 2024 ieee.

关键词： Feature extraction

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X-MAN: Explaining multiple sources of anomalies in video

X-MAN: Explaining multiple sources of anomalies in video

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Szymanowicz, Stanislaw Charles, James Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9781665448994

Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.

关键词： Training Deep learning Decision making Detectors Probabilistic logic Feature extraction pattern recognition

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Pain Detection in Masked Faces during Procedural Sedation 17

Pain Detection in Masked Faces during Procedural Sedation

引用

17th ieee International conference on Automatic Face and Gesture recognition, FG 2023

作者： Zarghami, Y. Mafeld, S. Conway, A. Taati, B. Kite Research Institute Toronto Rehabilitation Institute University Health Network TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Division of Vascular and Interventional Radiology Department of Medical Imaging University of Toronto TorontoON Canada Lawrence S. Bloomberg Faculty of Nursing University of Toronto TorontoON Canada Peter Munk Cardiac Centre University Health Network TorontoON Canada

ISBN: (纸本)9798350345445

Pain monitoring is essential to the quality of care for patients undergoing a medical procedure with sedation. An automated mechanism for detecting pain could improve sedation dose titration. Previous studies on facial pain detection have shown the viability of computer vision methods in detecting pain in unoccluded faces. However, the faces of patients undergoing procedures are often partially occluded by medical devices and face masks. A previous preliminary study on pain detection on artificially occluded faces has shown a feasible approach to detect pain from a narrow band around the eyes. This study has collected video data from masked faces of 14 patients undergoing procedures in an interventional radiology department and has trained a deep learning model using this dataset. The model was able to detect expressions of pain accurately and, after causal temporal smoothing, achieved an average precision (AP) of 0.72 and an area under the receiver operating characteristic curve (AVC) of 0.82. These results outperform baseline models and show viability of computer vision approaches for pain detection of masked faces during procedural sedation. Cross-dataset performance is also examined when a model is trained on a publicly available dataset and tested on the sedation videos. The ways in which pain expressions differ in the two datasets are qualitatively examined. © 2023 ieee.

关键词： computer vision

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ViT-Mixer: A Robust Automatic Modulation Classification Model Based on vision Transformer 4

ViT-Mixer: A Robust Automatic Modulation Classification Mode...

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4th International conference on computer Systems, ICCS 2024

作者： Xu, Boyu Gao, Xinfeng Guo, Li Qingdao266107 China College of Computer Science & Technology Qingdao University Shandong Qingdao China

ISBN: (纸本)9798350366341

Recently, an increasing number of automatic modulation classification techniques are utilizing deep learning to classify the modulation types of input shortwave signals. Existing research has made significant progress in feature extraction from signals using deep learning. However, many deep learning networks require high computational and parameter costs, making them expensive for practical deployment. Therefore, we chose the vision Transformer (ViT) as the foundation for our signal recognition model. We improved it with a new structure based on basic matrix multiplication, data dimension transformation, and scalar nonlinear operations, resulting in a new model: vision Transformer Mixer (ViT-Mixer). Moreover, existing models are often validated only on public datasets that lack several common signal types, limiting their effectiveness in real-world applications. To address this, we constructed a new dataset through actual collection and simulation methods to adapt the model to our task. We tested several models on this new dataset. Experimental results show that our proposed model, ViT-Mixer, achieves satisfactory accuracy in classifying specific signals in our dataset, while significantly reducing the computational and parameter costs compared to the original ViT. © 2024 ieee.

关键词： Mixers (machinery)

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A pattern recognition Model: Hand Gestures recognition using Convolutional Neural Networks

A Pattern Recognition Model: Hand Gestures Recognition using...

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2023 International conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Ayyappa Reddy, D. Esther Jyothi, V. Kasi Viswanath, J. Sampreeth Chowdary, N. Swapna, D. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350333602

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven't acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes. © 2023 ieee.

关键词： Gesture recognition

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Instance Localization for Self-supervised Detection Pretraining

Instance Localization for Self-supervised Detection Pretrain...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Ceyuan Wu, Zhirong Zhou, Bolei Lin, Stephen Chinese Univ Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. The objective of this paper is to advance self-supervised pretrained models specifically for object detection. Based on the inherent difference between classification and detection, we propose a new self-supervised pretext task, called instance localization. Image instances are pasted at various locations and scales onto background images. The pretext task is to predict the instance category given the composited images as well as the foreground bounding boxes. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. In addition, we propose an augmentation method on the bounding boxes to further enhance the feature alignment. As a result, our model becomes weaker at Imagenet semantic classification but stronger at image patch localization, with an overall stronger pretrained model for object detection. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection on PASCAL VOC and MSCOCO1.

关键词： Location awareness computer vision Transfer learning Semantics Object detection computer architecture pattern recognition

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A Document Image Moiré pattern Reduction Method Based on Convolutional Neural Networks 2

A Document Image Moiré Pattern Reduction Method Based on Co...

引用

2nd International conference on Image Processing, computer vision and Machine Learning, ICICML 2023

作者： Liu, Limei Cao, Jiajian Luo, Xin Hunan University of Technology and Business Department of Frontier and Interdisciplinary Changsha China

ISBN: (纸本)9798350331417

In recent years, the removal of Moiré patterns from images has been a challenging problem in computer vision, especially for document images. Many deep learning-based image denoising methods have been proposed to address the problem of Moiré pattern removal, but these methods still have issues with recovering textural details and globally removing Moiré patterns. This paper proposes a coarse-to-fine two-stage convolutional neural network to address the problems of incomplete Moiré pattern removal and loss of textural details. In the first stage, the model estimates the positions of the Moiré patterns and performs preliminary removal. In the second stage, the focus is on optimizing the preliminary removal results, which avoids the problems of incomplete removal and loss of textural details. In particular, we replace the loss function in the second stage with a focus on improving image quality, and add a Non-Local module to increase the receptive field, paying more attention to the information around the Moiré patterns. Finally, experimental results on the public dataset of Baidu PaddlePaddle demonstrate that our model outperforms other convolutional neural network models. © 2023 ieee.

关键词： Convolutional Neural Network Document Image Moire pattern removal

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Application of Pyramid Scene Parsing Network in leaf segmentation for Wheat Stripe Rust 5

Application of Pyramid Scene Parsing Network in leaf segment...

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5th International conference on computer vision, Image and Deep Learning, CVIDL 2024

作者： Shen, Yaxuan Sun, Xiaoyong Cui, Jiajun Lu, Yao Shandong Agricultural University College of Information Science and Engineering Taian271018 China Weifang University Seed and Facility Agricultural Engineering Weifang261061 China Shandong Intelligent Engineering Laboratory of Agricultural Equipment Shandong Agricultural University College of Mechanical and Electrical Engineering Taian271018 China

ISBN: (纸本)9798350373820

This paper addresses wheat stripe rust leaf segmentation using the Pyramid Scene Parsing Network algorithm (PSPNet). Unlike previous YOLO-guided GrabCut and DeepLabV3 approaches, PSPNet excels in mining global contextual information, embedding challenging scenarios in a Fully Convolutional Network (FCN)-based pixel prediction framework for precise segmentation. Notably, PSPNet achieves a recognition accuracy of up to 9 5. 0 4%, surpassing the limitations of image-dependent methods. The study's background discusses current segmentation shortcomings, motivating the exploration of PSPNet. The research aims to enhance accuracy, comparing PSPNet against YOLO-guided GrabCut and DeepLabV3, showcasing its superior performance. In conclusion, this research establishes PSPNet as a robust solution, effectively addressing challenges in wheat stripe rust leaf segmentation, offering improved accuracy, and demonstrating enhanced generalization capabilities. © 2024 ieee.

关键词： computer vision

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Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks

Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neu...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Williams, Francis Trager, Matthew Bruna, Joan Zorin, Denis NYU New York NY 10003 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we demonstrate, can also be viewed as a type of kernel method). Because our approach is based on a simple kernel formulation, it is easy to analyze and can be accelerated by general techniques designed for kernel-based learning. We provide explicit analytical expressions for our kernel and argue that our formulation can be seen as a generalization of cubic spline interpolation to higher dimensions. In particular, the RKHS norm associated with Neural Splines biases toward smooth interpolants.

关键词： Surface reconstruction Interpolation computer vision Three-dimensional displays Neural networks Fitting Surface fitting

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Handwritten Chinese Character Guidance System 5

Handwritten Chinese Character Guidance System

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5th International conference on Robotics and computer vision, ICRCV 2023

作者： Chen, Haoyang Huang, Runxiang Li, Yurui Li, Fangqing Institute of System Science National University of Singapore Beijing China

ISBN: (纸本)9798350326369

With the general trend of informatization, the importance of handwritten Chinese characters is gradually being ignored. Due to the differences in the education levels and the writing habits of the public, problems such as confusion of stroke order, distorted fonts, and wrong postures for holding the pen always exist. Whether from the perspective of cultural inheritance or practicality, the learning of handwritten Chinese characters is an indispensable and important link in contemporary education. In this project, we hope that by introducing artificial intelligence and computer vision technology, we can monitor and guide students' writing ability from the two aspects of pen grip posture and stroke order detection. The VGG16 Model was utilized to implement the function of pen grip posture classification. For the recognition of stroke order, it is hoped that a single Chinese character can be recognized first through real-time pen writing trajectories, so as to further perform the stroke extraction. CRNN (Convolutional Recurrent Neural Network) + CTPN (Connectionist Text Proposal Network) were applied for Handwriting Chinese characters recognition. SiamRPN++ model was implemented to track the pen tip region in the writing video, and through pen ballpoint detection and pen up/down classification, to achieve stroke segmentation of a single character. While in the last algorithm, the strokes would be classified as horizontal, vertical, oblique upwards, and oblique downwards according to the absolute value of the slope, and a length threshold and finally achieve the correctness judgment. © 2023 ieee.

关键词： computer vision

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