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

共 1642 条记录，以下是1571-1580 订阅

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Quality Assessment of AI-Generated image Based on Cross-modal Correlation

Quality Assessment of AI-Generated Image Based on Cross-moda...

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image processing and Media Computing (ICIPMC), international conference on

作者： Yunhao Zhang Menglin Jia Wenbo Zhou Yang Yang Anhui University Hefei China University of Science and Technology of China Hefei China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Artificial Intelligence Generated Content (AIGC), which encompasses various types of content including images, has been developed with the assistance of artificial intelligence technology. Artificial Intelligence-Generated images (AIGIs) are typically generated by inputting text prompt words into image generation models. The ongoing creation of AIGI presents new problems and challenges. A significant challenge is that AIGI compared to natural images may have some unique distortions and the quality of AIGIs is inconsistent and various. So it is of great significance to evaluate AIGI more comprehensively and AIGC image Quality Assessment (AIGCIQA) has emerged as a new topic in computer vision. There are few image Quality Assessment (IQA) methods specifically for AIGIs, and traditional IQA methods ignore the information embedded in the text prompts associated with these images. In this paper, we propose a framework for fusing text and image features. Specifically, we operate by processing text prompts and corresponding generated images. We use the RoBERTa pre-trained language model to extract text features, and we use the CNN+Transformer hybrid network architecture to extract image features, and then add a multi-head attention mechanism when fusing the extracted features to effectively learn the association information between text and images, and input it into the regression network to regress the prediction score. We conduct extensive experiments on three mainstream AIGCIQA databases, including AGIQA-1K, AGIQA-3K, and AIGCIQA2023, and the results demonstrate the effectiveness of our proposed fusion framework.

关键词： image quality Attention mechanisms Databases Predictive models Network architecture Media Feature extraction

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A Method for image Co-segmentation with Seeded Foreground

A Method for Image Co-segmentation with Seeded Foreground

引用

2019 3rd international conference on Machine vision and Information Technology (CMVIT 2019)

作者： Jiaji Wang Zhenjiang Miao Tianyu Zhou Qiang Zhang Miaomiao Li Institute of Information Science School of Computer and Information TechnologyBeijing Jiaotong University

image segmentation is a key technology from image processing to analysis. Without proper segmentation, it is impossible to recognize correctly. In this paper, we propose a method for image co-segmentation based on the biased normalized cuts using a semi-supervised way to deal with foreground regions. In order to take advantage of biased normalized cuts to solve problem, we use 2 D adaptive Wiener filter to smooth the seeded parts of images, then divide images into a set of super-pixels, after that take super-pixels as vertices to form a weighted undirected graph. Thus, the co-segmentation can be seen as an issue of graph partition that solved by biased normalized cuts. The experiments on image data sets show the superior performance of our method.

关键词： image A Method for image Co-segmentation with Seeded Foreground image

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GAN-based End-to-End Unsupervised image Registration for RGB-Infrared image

GAN-based End-to-End Unsupervised Image Registration for RGB...

引用

international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Kanti Kumari Ganapathy Krishnamurthi Indian Institute of Technology Madras Chennai India

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160795

image registration is a pre-processing step used in various computer vision applications. This paper presents an unsupervised image registration for a given pair of RGB and infrared images, with the RGB image used as the reference for infrared. This method exploits the GAN architecture with a spatial transformer module, to synthesize the transformed image using an unsupervised loss criterion. This loss used for error backpropagation taken as a linear combination of adversarial loss; Mean Squared Error (MSE) loss between the input RGB image and image synthesized by the generator; KL divergence loss between the IR image and the synthesized image; and another MSE loss estimated using features maps extracted from pretrained VGG-16. The adversarial loss forces the generator to output an IR like image, with the input IR image and the generated IR image labelled as real and fake respectively. The other three losses are backpropagated through the generator network to learn the transformation as well as to preserve the structure and resolution of the generated image. This unsupervised learning process is stopped after a specified number of iterations based on a validation set. A supervised method has also been developed for comparison with the presented method. The SSIM and PSNR values estimated between predicted registered image and its ground truth has been used as evaluation criteria. The unsupervised method has scored 0.8351±0.06 and 35.2723±0.68 for SSIM and PSNR respectively, while supervised scored as 0.7620±0.08 and 15.8978±2.21.

关键词： Generators image registration Feature extraction Training Computational modeling Gallium nitride computer architecture

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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IEEE computer Society conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Bach Hoang Ngo Dat Thanh Nguyen Nhat-Tuong Do-Tran Phuc Pham Huy Thien Minh-Hung An Tuan-Ngoc Nguyen Loi Nguyen Hoang Vinh Dinh Nguyen Quang-Vinh Dinh AI Vietnam Research Vietnam FPT Telecom Vietnam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology School of Computing and Information Technology Eastern International University Vietnam

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm’s efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2.

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An Adaptive Light Algorithm for Extracting Track Information and Its Control

An Adaptive Light Algorithm for Extracting Track Information...

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第三届机电控制技术与交通运输国际学术会议

作者： Dong Liu Yujun Wang School of Computer and Information Science Southwest University

On the path-tracking problem of autonomous vehicle and the extraction threshold of track,which is affected by the light, An unpowered intelligent vehicle and the corresponding control algorithm are designed in the paper. First, the vehicle's mechanism is designed. Then, some structures for mounting the camera and the steering gear are optimized. Next, the camera named LQ1286 is used to detect the path information in which the algorithm is adapted for image binarization processing that extracting the white line for track recognition. Following, PID algorithm is designed to adjust the angle of the steering servo mounted on the vehicle which is driven forward by external fan wind. Thus the patrol is completed intelligently. Experimental results show that the design of intelligent vehicle is able to complete the desired objectives.

关键词： Unpowered vehicle path tracking PID algorithm

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Infrared and Visible images Registration Using BEMD and MI

Infrared and Visible Images Registration Using BEMD and MI

引用

2010 3rd IEEE international conference on computer Science and Information Technology—ICCSIT 2010

作者： Xiuqiong Zhang,Tao Men,Chun Liu Laboratory of Intelligent Information processing and Application,computer Science College,Leshan Normal University,Leshan 614000,China Jian Yang Criminal Investigation Division,Leshan Public Security Bureau,Leshan 614000,China

Enhanced vision especially in poor weather conditions can be achieved by image fusion between the infrared and visible *** registration is a crucial step in image *** order to register the infrared and visible images captured in poor weather conditions,the registration method combing the Bidimensional Empirical Model Decomposition (BEMD) with mutual information is proposed in this *** the infrared and visible images are decomposed into a set of Intrinsic Mode Functions (IMFs) and residue respectively by BEMD,the parameters of affine transform can be obtained through using the maximum mutual information registration between IMFs ***,the transform matrix defined by these parameters is used to the source infrared image and the final registered image can be obtained through interpolation and resampling for the transformed *** method is used to register two video sequences captured in dense *** results show that this method gives better performance compared to other two methods.

关键词： image registration Mutual Information (MI) bidimensional empirical model decomposition (BEMD) infrared image visible image

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Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

Feature Extraction using Convolution Neural Networks (CNN) a...

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Manjunath Jogin Mohana M S Madhulika G D Divya R K Meghana S Apoorva R. v. College of Engineering Bengaluru India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

The image classification is one of the preliminary processes, which humans learn as infants. The fundamentals of image classification lie in identifying basic shapes and geometry of objects around us. It is a process which involves the following tasks of pre-processing the image (normalization), image segmentation, extraction of key features and identification of the class. The current image classification techniques are much faster in run time and more accurate than ever before, they can be used for extensive applications including, security features, face recognition for authentication and authorization, traffic identification, medical diagnosis and other fields. The idea of image classification can be solved by different approaches. But the machine learning algorithms are the best among them. These algorithms are based on the idea proposed years ago, but couldn't be implemented due to lack of computational power. With the idea of deep learning, the models are trained better and are able to identify different levels of image representation. The convolutional neural networks revolutionized this field by learning the basic shapes in the first layers and evolving to learn features of the image in the deeper layers, resulting in more accurate image classification. The idea of Convolutional neural network was inspired by the hierarchical representation of neurons by Hubel and Wiesel in 1962, their work was based on the study of stimuli of the visual cortex in cat. It was a fundamental breakthrough in the field of computer vision in understanding the working of visual cortex in humans and animals. In this paper feature of an images is extracted using convolution neural network using the concept of deep learning. Further classification algorithms are implemented for various applications.

关键词： Training Neurons Classification algorithms Support vector machines Biological neural networks Feature extraction image classification

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Time Efficient Design and FPGA Implementation of Distributed Canny Edge Detector Algorithm

Time Efficient Design and FPGA Implementation of Distributed...

引用

IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： V Raghavendra Lakshmi Shrinivasan Affiliated to VTU Bangalore India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Edge detection is one of the basic operations carried out in image processing and object identification. As it plays an important role in image processing which needs to be optimized, accurate and less latency architecture. This paper focuses on Edge detection using Adaptive threshold technique. The efficient Canny edge detector has been used in real time application. In this paper, the Gaussian filter is applied as preprocessing block to remove the high frequency edges. The Canny coefficients are approximated to suite the hardware requirements with less LUT's and further the adaptive threshold technique is applied to obtain the finer details of edges. The proposed method of edge computation using adaptive threshold reduces memory elements significantly, because it occupies less area, reduction in delay and increased efficiency without effecting detection performance. The proposed edge detection architecture is implemented using Xilinx system generator tool on Spartan6 ATLYS (XC6SLX45) board. It is observed that the proposed method is better in terms of time performance as compared to existing architectures.

关键词： image edge detection Kernel computer architecture Detectors Thresholding (Imaging) Field programmable gate arrays

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The Application of computer vision Target Recognition Technology in Autonomous Driving

The Application of Computer Vision Target Recognition Techno...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yaning Wang Xin Bi TongJi University Shanghai China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In recent years, the rapid growth of Internet of Things (IoT) technology and computer vision (CV) technology has provided strong support for the application of deep learning (DL) in various fields. Among them, DL based object detection algorithms have attracted much attention due to their excellent performance. Vehicle target detection, as an important branch of object detection, is crucial for the construction of intelligent driving and intelligent transportation systems. In autonomous driving technology, using machines to detect and recognize traffic signs, vehicles, and pedestrians is of great significance for improving driving safety, reducing labor costs, and demonstrating broad application prospects. In this paper, a target recognition algorithm based on CV and DL is proposed to improve the target detection capability of auto drive system. This algorithm combines the image processing technology of CV and the feature extraction and classification ability of DL to achieve fast and accurate recognition of targets such as vehicles and pedestrians in road environments. Experimental results show that through training and optimizing the DL model, the algorithm can perform well in complex and changeable traffic scenes and provide reliable target detection support for the auto drive system.

关键词： Training Pedestrians Accuracy Target recognition Roads Object detection Feature extraction Classification algorithms Safety Autonomous vehicles

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Improving the Precise Identification of Animal Species through the use of vision Transformers

Improving the Precise Identification of Animal Species throu...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Soundarya M Lingesh Kumar K Pawan Prasath S M Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

When it comes to the precise recognition of animal species, fine-grained classification proves to be particularly difficult since there are often minute differences between species as in substantial variation in the same species. Convolutional Neural Networks (CNNs), which are predominantly used in image classification, have an edge over these networks because image details are compressed into high dimensional embeddings and thus fine-grained classification becomes a problem. An innovative approach can be found in the use of vision Transformers (ViTs), which employ self-attention mechanisms and model long-range dependencies. In this study, we put ViTs to a more complex task of identifying closely related animal species, in which the conveyance of the minute differences and context specifics is of utmost importance. The benchmarks datasets are employed along with a series of comparative research predictive of the classification performance competing with the best-in-class CNNs. The results indicate that in fine-grained classification tasks, ViTs performance is more accurate and stable than alternatives. In addition, the research demonstrates that ViTs can transfer well in the classification of datasets containing different animal species, which suggests their capability for use in practical ecology. The present method enhances the efficiency of species identification but more importantly offers a revolutionary device for biodiversity conservation, ecological data processing and research planning. This contribution to knowledge places vision Transformers at the forefront of technology advancement in fine-grain visual recognition and enables the automation of ecological research.

关键词： computer vision Visualization Automation Accuracy Animals Biological system modeling Wildlife Transformers Biodiversity Monitoring

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