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检索条件"任意字段=5th International Conference on Scale-Space and PDE Methods in Computer Vision"

共 182 条记录，以下是51-60 订阅

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Research on Traditional Morphological methods and Deep Learning for Apple Image Recognition 5

Research on Traditional Morphological Methods and Deep Learn...

引用

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

作者： Wan, Changcheng Sun Yat-sen University School of Geospatial Engineering and Science Zhuhai China

ISBN: (纸本)9798350373820

this paper presents a comprehensive study on apple identification and maturity assessment using advanced image processing and deep learning techniques. Initially, it employs bilateral filtering for noise reduction while preserving image edges, followed by multi-channel threshold segmentation to differentiate apples from their backgrounds using color space transformations. the study further develops a mathematical model for analyzing apple maturity based on color ratios in the HSI color model, effectively distinguishing fruit maturity stages. Additionally, it quantifies apple quality by correlating the fruit's pixel area with its actual volume, establishing a linear relationship for accurate quality assessment. Finally, the application of Faster RCNN for transfer learning in MATLAB 2022b demonstrates significant advancements in automated apple recognition, showcasing the potential of these models in agricultural quality control and yield estimation. © 2024 IEEE.

关键词： Noise abatement

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A Robust Contrast Learning Method Based on Semantic Enhancement 5

A Robust Contrast Learning Method Based on Semantic Enhancem...

引用

2023 5th international conference on Artificial Intelligence and computer Applications, ICAICA 2023

作者： Guo, Jian An, Jiaxin Yu, Yuna Liu, Aidi Liu, Yabian Computer and Information Engineering College Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350323313

Recent self-supervised contrast learning methods have made significant progress in the field of computer vision, and these methods aim to learn useful feature representations from large-scale unlabeled data, providing a powerful foundation for a variety of vision tasks. Contrast learning methods drive feature learning by maximizing the similarity of positive sample pairs and minimizing the similarity of negative sample pairs. However, one of the keys to getting a contrast learning framework with superior performance is well-designed contrast pairs. Traditional methods often simply apply random clipping to generate different clipping samples, but this can lead to insufficient semantic information in the clipped area. To solve this problem, the SECL method is proposed to determine the clipping region by measuring the importance of each region in the image, to ensure that the clipped region contains rich semantic information. In addition, convolutional neural networks often have difficulty capturing global information, so self-attention mechanisms are further designed to enhance the feature extraction network's perception of global information. Experiments show that our method can effectively improve the classification accuracy of the classical contrast learning framework on multiple data sets. © 2023 IEEE.

关键词： Image classification

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Relative navigation algorithm based on rodrigues and spacecraft orbit & attitude information

Relative navigation algorithm based on rodrigues and spacecr...

引用

5th international conference on Natural Computation, ICNC 2009

作者： Li, Kezhao Zhang, Qin Zhao, Chaoyin Yuan, Jianping Keke, Xu Henan Polytechnic University JiaoZuo 454003 China College of Geology Engineering and Geomatics Chang'an University China College of Astronautics Northwestern Poly Technical University Xi'an 710072 China

ISBN: (纸本)9780769537368

It is vital to calculate the relative position and pose in many important space missions, such as space formation, rendezvous and docking, space capturing and maintenance etc. It is a direction to get the relative position and pose based on computer vision currently all over the world. And this method is also valid for spacecrafts. In this paper, on the basis of the attitude dynamics of spacecrafts and the theory of computer vision, a relative navigation algorithm based-on Rodrigues and the orbit & attitude information of the spacecrafts is proposed. this algorithm reduces the Jacobian matrix rank. thus, its calculation speed is faster than the quaternion method. Additionally, the iterative numbers of this algorithm are reduced when the orbit & attitude information of the spacecrafts has been used. So the calculation efficiency of this algorithm is improved. Lastly, some simulation results are given to validate the above theoretical conclusions. © 2009 IEEE.

关键词： Iterative methods

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Separable time-causal and time-recursive spatio-temporal receptive fields 5

Separable time-causal and time-recursive spatio-temporal rec...

引用

5th international conference on scale space and Variational methods in computer vision, SSVM 2015

作者： Lindeberg, Tony Department of Computational Biology School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783319184609

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about parameterizing the intermediate temporal scale levels, analysing the resulting temporal dynamics and transferring the theory to a discrete implementation in terms of recursive filters over time. © Springer international Publishing Switzerland 2015.

关键词： Artificial intelligence

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Estimating motion and structure from correspondences of line segments between two perspective images

Estimating motion and structure from correspondences of line...

引用

5th international conference on computer vision

作者： ZHANG, ZG INRIA SOPHIA ANTIPOLIS F-06902 SOPHIA ANTIPOLISFRANCE

ISBN: (纸本)0818670428

Presents an algorithm for determining 3D motion and structure from correspondences of line segments between two perspective images. To our knowledge, the paper is the first investigation of use of line segments in motion and structure from motion. Classical methods use their geometric abstraction, namely straight lines, but then three images are necessary for the motion and structure determination process. We show that two views are in general sufficient when we use line segments. the assumption we use is that two matched line segments contain the projection of a common part of the corresponding line segment in space. Indeed this is what we use to match line segments between different views. Both synthetic and real data have been used to test the proposed algorithm, and excellent results have been obtained with real data containing a relatively large set of line segments. the results are comparable with those obtained using stereo calibration.

关键词： edge detection motion estimation image segmentation computer vision line segment correspondence perspective images structure estimation algorithm 3D motion estimation matched line segments projection real data synthetic data stereo calibration

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Dynamic gesture recognition method based on multi-scale feature fusion 5

Dynamic gesture recognition method based on multi-scale feat...

引用

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

作者： Qiu, Feiyue Zhou, Jian Peng, Delong Zhejiang University of Technology College of Education Science and Technology Hangzhou310023 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China

ISBN: (纸本)9798350373820

Currently, dynamic gesture classification methods based on deep learning have problems such as the inability to extract gesture features thoroughly and difficulty in accurate time series modeling of gesture features. this paper proposes a dynamic gesture recognition method based on multi-scale feature fusion to address these problems. First, dynamic gesture depth image sequences can be converted into 3D point cloud sequences to maintain the gesture's spatial structure and shape features. then, a two-stream neural network structure is introduced to learn local and global features of gesture point cloud data separately, and they are fused into multi-scale features. To more accurately model dynamic gesture features in time series, a BiGRU-Transformer module is constructed by adding a bidirectional gated recurrent unit to the original structure of the Transformer, into which multi-scale features are input to obtain the final recognition results. To validate the effectiveness of our method, we perform experiments on SHREC17 and DHG datasets. the method achieves the highest recognition accuracy on these two datasets compared to other methods, validating the superiority of our proposed method. © 2024 IEEE.

关键词： Gesture recognition

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Artificial intelligence aided analysis of flexible space in landscape environment 5

Artificial intelligence aided analysis of flexible space in ...

引用

5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Liu, Qi School of Art Design and Media Sanda University Shanghai201209 China

ISBN: (纸本)9781665412728

Artificial intelligence aided analysis of the flexible space in smart landscape design is studied in this paper. When recognizing scene data sets, the scene recognition method of convolutional neural network can get a better recognition effect, and the accuracy can be improved by more than the 13.5% compared with traditional methods. In our designed model, the listed aspects are considered. (1) the feature fusion method can comprehensively utilize multiple features of image to achieve feature complementarity. the machine learning is applied to then construct the pipeline. (2) Data enhancement can improve the recognition performance and generalization ability of the network which is applied to the model design. the simulation results have proven the accuracy. © 2021 IEEE.

关键词： computer vision

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A Survey on Multimodal Deep Learning for Image Synthesis Applications, methods, datasets, evaluation metrics, and results comparison 2021

A Survey on Multimodal Deep Learning for Image Synthesis App...

引用

5th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Luo, Sanbi Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9781450388634

In recent years, deep learning technology has made breakthroughs in computer vision. After using large-scale data training, the deep neural network represented by GAN is significantly better than previous technologies in image generation, including generating more reasonable, higher-definition, more complex, and more accurate images. With the continuous development of datasets, models, and applications, the fusion of different modal information, including fusion of natural language, semantic layouts, tags, edge maps, and other different modal information, to generate images has become a new demand and challenge. there are related reviews on image generation and multimodal deep learning. However, there has not been a review dedicated to multimodal deep-learning image generation to discuss the current status, existing problems, and challenges of this task. therefore, this review proposes a survey on multimodal deep learning image generation. It aims to provide readers with an application scenario for multimodal deep learning image generation. Also, it provides readers with new multimodal deep learning image generation technologies, the relevant datasets, evaluation metrics used, and some results comparison. Finally, this article describes some of the challenges and future topics of multimodal deep learning image generation.

关键词： image synthesis multimodal deep learning machine learning computer vision

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MRA-Depth: Multi-Resolution Attention based Monocular Depth Estimation 5

MRA-Depth: Multi-Resolution Attention based Monocular Depth ...

引用

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

作者： Wen, Jing Niu, Lukuan School of Computer Information and Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China School of Computer Information and Technology Shanxi University Taiyuan China

ISBN: (纸本)9798350326369

Delicate feature would become weaker or even disappear as going through deeper network layer, which results in jagged edges of objects in the depth estimation. To address this problem, we propose a multi-resolution attention based monocular depth estimation by capturing and recalibrating the dependencies of channels between different resolutions, including: we develop a multi-resolution attention encoder by embedding resolution and spatial attention, so as to extract the higher quality features in encoder, and we design a reusable multi-resolution fusion model in decoder, in which different scale features are integrated and then extract multi-resolution attention maps, so as to enhance detail information locally and perspective structure features globally. Experimental results illustrate the proposed network is capable to predict more precise depth and sharper contour, and to outperforms related methods on the KITTI benchmark. © 2023 IEEE.

关键词： Network layers

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Bilevel optimization with nonsmooth lower level problems 5

Bilevel optimization with nonsmooth lower level problems

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5th international conference on scale space and Variational methods in computer vision, SSVM 2015

作者： Ochs, Peter Ranftl, René Brox, thomas Pock, thomas Computer Vision Group University of Freiburg Freiburg Germany Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Digital Safety and Security Department AIT Austrian Institute of Technology GmbH Vienna1220 Austria

ISBN: (纸本)9783319184609

We consider a bilevel optimization approach for parameter learning in nonsmooth variational models. Existing approaches solve this problem by applying implicit differentiation to a sufficiently smooth approximation of the nondifferentiable lower level problem. We propose an alternative method based on differentiating the iterations of a nonlinear primal–dual algorithm. Our method computes exact (sub)gradients and can be applied also in the nonsmooth setting. We show preliminary results for the case of multi-label image segmentation. © IFIP international Federation for Information Processing 2015.

关键词： Image segmentation

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