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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1647 条记录，以下是221-230 订阅

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A Review on computer vision - Scene Classification Techniques 3

A Review on Computer Vision - Scene Classification Technique...

引用

3rd international conference on Inventive Systems and Control, ICISC 2019

作者： Shah, Aayushi A. Rana, Keyur Surat Gujarat India

ISBN: (纸本)9781538639504

In today's era, need for automatic response of machines on certain task has been prevalent. Humans want their life easier and automatic in every possible way. However, those tasks require better understanding by the machine to perform human like tasks. Tasks like classification, detection and localization are on high demand and dominant research area. These tasks fall into a domain called computer vision where computers by analyzing and understanding performs human like tasks. This domain provides the automatic inference by machines to make human life easier. In this paper, we focus on one of the difficult computer vision tasks called scene classification. Scene Classification deals with techniques that make machine intelligent and automated by processing given input say image. As machines are made automatic and intelligent to perform various tasks, Artificial Intelligence and image processing comes into the picture. We study and analyze various approaches and methods by which such task can be handled easily and accurately. Furthermore, we compare all the approaches and find out the best approach to opt for this task. © 2019 IEEE.

关键词： computer vision

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Controlling mouse through eyes

Controlling mouse through eyes

引用

3rd international conference on Emerging Technologies

作者： Ali, Naveed Khan, Nabeel Younus Imran, Ali Shariq Natl Univ Sci & Technol Dept Comp Sci Rawalpindi Pakistan

ISBN: (纸本)9781424414932

User-computer dialogues are typically one-sided, with the benefits from computer to user far greater than that from user to computer. The movement of a user's eyes can provide a convenient, natural, and high-bandwidth source of additional user input, to help redress this one-sidedness. We, therefore investigate the introduction of eye movements as a computer input medium. Our emphasis is on the study of interaction techniques that incorporate eye movements into the user-computer dialogue. In this paper, an advanced approach to man machine interaction is proposed, in which computer vision techniques are used for interpreting user actions.

关键词： computer vision image processing artificial intelligence

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TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology images using Local Texture, Appearance, and Gradient Features 3

TAG: Nucleus Detection in Colorectal Adenocarcinomas Histolo...

引用

3rd international conference on Imaging, Signal processing and Communication (ICISPC)

作者： Domoguen, Jansen Keith L. Suarez, Jessie James P. Naval, Prospero C., Jr. Univ Philippines Comp Vis & Machine Intelligence Grp Diliman 1 Quezon City Philippines

ISBN: (纸本)9781728136639

This work considers the problem of detecting nuclei in H&E stained images. The high intra-class and inter-image variability of nuclei of the images itself calls for a more robust method that is able to handle this high variability. Recently, deep learning has become the most popular method in computer vision with many systems achieving state-of-the-art results for a wide array tasks. However, as a trade-off, deep learning methods require tons of data and computational resources that some may not posses. Thus, this work proposes a traditional computer vision pipeline along with TAG, a local feature-based approach that combines texture, appearance, and gradient features which is used in the task of pixel-per-pixel prediction in the virtue of semantic segmentation. Based from the results, the method was shown to be effective despite the challenges of high variability in the dataset.

关键词： nucleus detection nuclei detection histology image texture appearance gradient multi-layer perceptron

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Deep Learning for Gastric Pathology Detection in Endoscopic images 19

Deep Learning for Gastric Pathology Detection in Endoscopic ...

引用

3rd international conference on Graphics and Signal processing (ICGSP)

作者： Khryashchev, V. V. Stepanova, O. A. Lebedev, A. A. Kashin, S., V Kuvaev, R. O. PG Demidov Yaroslavl State Univ Yaroslavl Russia Yaroslavl Reg Canc Hosp Yaroslavl Russia

ISBN: (纸本)9781450371469

computer-aided diagnosis of cancer based on endoscopic image analysis is a promising area in the field of computer vision and machine learning. Convolutional neural networks are one of the most popular approaches in endoscopic image analysis. This paper presents the algorithm of pathology detection in endoscopic images of gastric lesions based on convolutional neural network. Training and testing of the algorithm was carried out on the NVIDIA DGX-1 supercomputer using endoscopic images from the test base, assembled together with the Yaroslavl Regional Cancer Hospital. As a result of experiments, the mAP metric was calculated and the value was 0.875, which is a high result for the task of object detection in images.

关键词： Machine learning convolution neural network endoscopic image analysis gastric cancer

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Real-time 3D Skeletonisation in computer vision-Based Human Pose Estimation Using GPGPU

Real-time 3D Skeletonisation in Computer Vision-Based Human ...

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3rd international conference on image processing Theory, Tools and Applications (IPTA)

作者： Bakken, Rune Havnung Eliassen, Lars Moland Sor Trondelag Univ Coll Fac Informat & E Learning Trondheim Norway

ISBN: (纸本)9781467325851;9781467325837

Human pose estimation is the process of approximating the configuration of the body's underlying skeletal articulation in one or more frames. The curve-skeleton of an object is a line-like representation that preserves topology and geometrical information. Finding the curve-skeleton of a volume corresponding to the person is a good starting point for approximating the underlying skeletal structure. In this paper a GPU implementation of a fully parallel thinning algorithm based on the critical kernels framework is presented. The algorithm is compared to another state-of-the-art thinning method, and while it is demonstrated that both achieve real-time frame rates, the proposed algorithm yields superior accuracy and robustness when used in a pose estimation context. The GPU implementation is > 8 x faster than a sequential version, and the positions of the four extremities are estimated with rms error similar to 6 cm and similar to 98 % of frames correctly labelled.

关键词： Skeletonisation GPGPU Real-time Human Motion Analysis

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A Real-Time Big Data Architecture For Glasses Detection Using computer vision Techniques 3

A Real-Time Big Data Architecture For Glasses Detection Usin...

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3rd international conference on Future Internet of Things and Cloud (FiCloud) / international conference on Open and Big Data (OBD)

作者： Fernandez, Alberto Casado, Ruben Usamentiaga, Ruben Fdn CTIC Technol Ctr Technol Sci Pk Gijon Gijon 33204 Asturias Spain Univ Oviedo Gijon 33204 Asturias Spain

ISBN: (纸本)9781467381031

Automatic glasses detection is a hot topic withing the large-scale face images classification domain, which has impact on face recognition or soft biometrics for person identification. In many practical video surveillance applications, the faces acquired by cameras are low resolution. Therefore, this type of applications requires processing of a large number of relatively small-sized images. However, continuous stream of image and video data processing is a big data challenge. This need fits with the goals of Big Data streaming processing systems. In this paper, we propose a real-time Big Data architecture in order to collect, maintain and analyze massive volumes of images related with the problem of automatic glasses detection. This architecture can be used as an automatic image tagging related with glasses detection on face images.

关键词： big data glasses detection real-time processing big data real-time process images Glass Eyeglasses Data structures video monitoring Facial Recognition Cameras

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A Novel Convolutional Neural Network for image Classification 3

A Novel Convolutional Neural Network for Image Classificatio...

引用

3rd IEEE international conference on Frontiers Technology of Information and computer, ICFTIC 2021

作者： Yang, Yunfeng Guan, Chen Northeast Petroleum University Mathematics and Statistics Daqing China

ISBN: (纸本)9781665406055

image classification technology is the most basic task in computer vision, and it is widely used in medical treatment, online shopping, face recognition, etc. An improved convolutional neural network model is proposed for image classfication in this article. Firstly, the novelty of this model is that principal component analysis is used to replace the pooling layer in convolutional neural networks, which mainly calculates and selects the eigenvectors with large eigenvalues to make linear table of the original data to achieve the purpose of rapid dimensionality reduction. Secondly, a three-layer convolutional layer is used to extract the main features of the image. Through classification experiments on the MNIST data set, kaggle leaves and other data sets, the desired accuracy is achieved. It indicates that the improved convolutional neural network model has a perfect recognition effect, and the improved model is suitable for classification processing of different types of data sets. In addition, the classification of noise images can also achieve higher accuracy. © 2021 IEEE.

关键词： Neural network models

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Multispectral low light level image fusion technique

Multispectral low light level image fusion technique

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3rd international conference on Signal processing (ICSP 96)

作者： Hu, JH Bai, LF Kong, J Liu, B Wang, LP Zhang, BM School of Electronic Engineering & Optoclectronic Technique Nanjing University of Science and Technology of PLA Nanjing China

ISBN: (纸本)7505338900

Multispectral image fusion is a kind of process of synthesis and processing for multispectral image data. On the basis of optoelectronic imaging technique, the imaging process & feature of low light level(LLL) TV system are analyzed & the principle and processing construction of multispectral LLL TV image fusion are discussed. The proper combination of low light level camera and optical filters is used to implement multispectral image fusion experiment at night for outdoor static scene by the aid of computer. The results of experiment indicate that multispectral low light level image fusion has improved the capability for recognizing targets.

关键词： spectral analysis sensor fusion image processing optical filters television cameras military equipment image sensors television applications multispectral low light level image fusion image synthesis image processing multispectral image data optoelectronic imaging technique imaging process low light level TV system low light level camera optical filters multispectral image fusion experiment night vision outdoor static scene target recognition military reconnaissance

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Fast vision Transformer via Query Vector Decoupling 3

Fast Vision Transformer via Query Vector Decoupling

引用

3rd IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Sun, Donghao Liu, Jiashun Liu, Jiaxing Zhang, Zixuan School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (纸本)9781665425186

Although transformer structure has become the defacto standard of the natural language processing task, it still has limited application in computer vision. In vision, attention is either combined with or replaces certain modules in a convolutional network, while keeping its overall framework intact. It proves that this reliance on CNNs is unnecessary for the pure transformer directly applied to image patch sequence can successfully classify the images. When large amounts of data are pre-trained and transmitted to multiple small and medium-sized image recognition benchmark (imageNet, CIFAR-100, VTAB, etc.), the vision Transformer (ViT) achieves remarkable results with less computational resources required than existing convolutional networks. Unlike the previous calculation that weighs attention, the model in this paper has been optimized, reducing the time complexity from 0(n2) to 0(n • logn), largely improving the model's speed. Therefore, a new model is developed-Fast vision Transformer (Fast VIT). © 2021 IEEE

关键词： computer vision

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation 13

Leveraging Swin Transformer for Local-to-Global Weakly Super...

引用

13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Ahmadi, Rozhan Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

关键词： Weakly Supervised Semantic Segmentation Class Activation Map Hierarchical vision Transformer image-level label

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