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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Color based subject identification for virtual museums, the case of fish

Color based subject identification for virtual museums, the ...

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international conference on Telecommunications and Multimedia (TEMU)

作者： Kalochristianakis, Michael Malamos, Athanasios Vassilakis, Kostas Inst Educ Technol Dept Informat Engn Multimedia Content Lab Iraklion Greece

ISBN: (纸本)9781467384094

The identification of fish subjects in controlled underwater environments, such as museum, falls into the category of image processing and recognition problems. An interesting aspect of such problems is that they require real time processing and thus effective processing methods must be evolved for the implementation. Although there have been a lot of approaches that solve the identification problem using specialized methods such as contour identification, recognition of distinctive characteristics, besides others, our team at the Multimedia Content lab of TEI Crete has focused on analyzing the color of still images taken from pools in order to rapidly identify subjects.

关键词： content-based image retrieval image processing computer vision virtual museums multimedia

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image processing techniques in vision-based robot navigation

Image processing techniques in vision-based robot navigation

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international conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO)

作者： Munguia, Rodrigo Bolea, Yolanda Grau, Antoni Univ Guadalajara Dept Comp Sci CUCEI Guadalajara Jalisco Mexico Tech Univ Catalonia UPC Automat Control Dept Barcelona Spain

ISBN: (纸本)9781538695760

Motivated by the problems of vision-based mobile robot map building and localization, we present a comparative study of statistical methods for matching image features in a wide base line between learning and recognition phases. A general methodology called feature-class method for the problem of fast matching image features in a wide base line is described in the context of mobile robots. The objective of this work is to discuss and to show the performance of such methods in an example of visual SLAM, with experiments done with real data.

关键词： computer vision robot navigation image processing

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Graph-Based Depth Estimation in a Monocular image Using Constrained 3D Wireframe Models 5th

Graph-Based Depth Estimation in a Monocular Image Using Cons...

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5th international conference on computer vision and image processing, cvip 2020

作者： Das, Bishshoy Dutta, H. Pallab Jyoti Bhuyan, M.K. Department of Electronics and Electrical Engineering IIT Guwahati Guwahati781039 India

ISBN: (纸本)9789811611025

In this paper, the problem of estimating the depth of an object from its monocular image is addressed. Here, basically, an algorithm is developed, which performs shape matching, and as a result, achieve accurate depth maps of objects. In the algorithm, first, an optimal camera position is determined. Then, the 3D model is projected onto the image plane of the camera, yielding a projected 2D image of the 3D model. An objective function determines a score based on graph-based feature matching, and the depth map is extracted from the geometrical information of the 3D model. Finally, the depth map and the original image are combined to create a 3D point cloud simulation of the object. Experimental analysis shows the efficacy of the proposed method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Multi-frame and multi-scale conditional generative adversarial networks for efficient foreground extraction 4th

Multi-frame and multi-scale conditional generative adversari...

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4th international conference on computer vision and image processing, cvip 2019

作者： Didwania, Himansu Ghatak, Subhankar Rup, Suvendu Image and Video Processing Lab Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Bhubaneswar751003 India

ISBN: (纸本)9789811540172

Alongside autonomous submissions, foreground extraction is considered to be the foundation for various video content analysis technologies, like moving object tracking, video surveillance and video summarization. This paper proposes an efficient foreground extraction methodology based on conditional Generative Adversarial Network. The proposed generator, which is made up of two networks working in series- Foreground Extractor and Segmentation Network, maps the video frames to corresponding foreground masks. The discriminator aids the learning of generator by learning to differentiate between seemingly real and fake foreground maps. The method used a multi-scale approach in order to capture robust features across multiple scales of input using the Feature Extractor Network, which are then used by the successive Segmentation Network to produce the final foreground map. In addition, a multi-frame approach is also used to facilitate capturing of appropriate temporal features. The performance of the proposed model is evaluated on CDnet 2014 Dataset and outperforms existing methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： Generative adversarial networks

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Color image processing Using Reduced Biquaternions with Application to Face Recognition in a PCA Framework 16

Color Image Processing Using Reduced Biquaternions with Appl...

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16th IEEE international conference on computer vision (ICCV)

作者： El-Melegy, Moumen T. Kamal, Aliaa T. Assiut Univ Comp Vis Lab Assiut Egypt Assiut Univ Math Dept Assiut Egypt

ISBN: (纸本)9781538610343

In this paper, we present the theory of reduced biquaternion algebra to represent color images and to develop efficient vector processing methods. We apply this theory to the field of face recognition in a principal component analysis (PCA) framework. We develop a novel PCA method based on reduced biquaternion to make full use of the face color cues. Moreover, we derive new mathematical results on the computation of the eigenvalues/eigenvectors of the data scatter matrix. We also extend this method to two-dimensional color PCA to combine the face spatial and color information. Experiments on several public-domain color face benchmark datasets demonstrate the higher performance of the proposed methods compared to regular PCA and like methods.

关键词： Quaternions image color analysis Principal component analysis Eigenvalues and eigenfunctions Color Face recognition Algebra

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Reduction of Building Facade Model Complexity using computer vision

Reduction of Building Facade Model Complexity using Computer...

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IEEE international conference on Imaging Systems and Techniques (IST) / IEEE international School on Imaging

作者： Paravolidakis, Vasileios Bacharidis, Konstantinos Sarri, Froso Ragia, Lemonia Zervakis, Michalis Tech Univ Crete Sch Elect & Comp Engn Kounoupidiano Chania Greece Tech Univ Crete Sch Environm Engn Kounoupidiano Chania Greece

ISBN: (纸本)9781509018178

3D Building facade reconstruction enables the implementation of delicate actions related to restoration, support and presentation of architectural structures. In particular, model complexity can be significantly reduced through the incorporation of computer vision and image processing tools, aiming towards the estimation of structural 3D feature characteristics instead of their actual GPS specification. In this paper we present a strategy for constructing reduced-complexity 3D models, which combines stereo vision images and morphological processing methods. Initially, the building skeleton is extracted and modeled with the use of Hough transformation and Active Contours. Subsequently, the building's facade depth map is derived using the stereo camera layout, for the refinement of inner skeleton details, such as the protrusion of windows or doors. These image-driven features are fused with Georeferenced data in order to complete the full building's skeleton model. The proposed reconstruction framework is tested on the site of Neoria buildings, a historical (Venetian) site in Chania, Crete, Greece.

关键词： 3D building reconstruction stereo vision fusion of camera with geo-reference data active contours model

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An autoencoder-based image descriptor for image matching

An autoencoder-based image descriptor for image matching

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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Zhao, Chenyang Goshtasby, A. Ardeshir Zhai, Shaodan Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1601324421

— Local image features are needed in various computer vision applications. For this purpose, a large number of point detectors and descriptors have been developed during recent years. Nonetheless, creation of effective descriptors is still a topic of research. The Scale Invariant Feature Transform (SIFT) proposed by David Lowe is a widely used image descriptor in image analysis and image matching. SIFT is found to provide a high matching rate and is robust under various image transformations;however, it is relatively slow in image matching. Autoencoder is an effective computational method for representation learning. In this paper, autoencoder is used to construct a low-dimensional representation for a high-dimensional data while preserving the structural information within the data. In many computer vision applications, a high dimensional data implies a high computational cost. The main motivation in this work is to significantly improve the speed of image descriptors without reducing their match ratings noticeably. A new descriptor is designed that is based on the autoencoder concept. The proposed descriptor can reduce the size and complexity of a descriptor significantly, considerably reducing the time required to find an object of interest in an image. © CSREA Press.

关键词： image matching

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Study of zone-based feature for online handwritten signature recognition and verification in Devanagari script

Study of zone-based feature for online handwritten signature...

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international conference on computer vision and image processing, cvip 2016

作者： Ghosh, Rajib Roy, Partha Pratim Department of Computer Science & Engineering National Institute of Technology Patna India Department of Computer Science & Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789811021060

This paper presents one zone-based feature extraction approach for online handwritten signature recognition and verification of one of the major Indic scripts–Devanagari. To the best of our knowledge no work is available for signature recognition and verification in Indic scripts. Here, the entire online image is divided into a number of local zones. In this approach, named Zone wise Slopes of Dominant Points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are supplied to two different classifiers;Hidden Markov Model (HMM) and Support Vector Machine (SVM) for recognition and verification of signatures. An exhaustive experiment in a large dataset is performed using this zone-based feature on original and forged signatures in Devanagari script and encouraging results are found. © Springer Science+Business Media Singapore 2017.

关键词： Support vector machines

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Rain Streak Removal via Spatio-Channel Based Spectral Graph CNN for image Deraining 1

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7th international conference on computer vision and image processing, cvip 2022

作者： Ragini, Thatikonda Prakash, Kodali Department of Electronics and Communication Engineering National Institute of Technology Telangana Warangal506004 India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Removing rain streaks from the captured single rainy images plays a dominant role in high-level computer vision (CV) applications. Since, many existing deraining methods ignores long range contextual information and utilize only local spatial information. To address this issue, a Spatio-channel based Spectral Graph Convolutional Neural Network (SCSGCNet) for image deraining was proposed and two new modules were introduced to extract representations along spatial and channel wise dimensions. Therefore, we integrate deep Convolutional neural network (CNN) with spatial based spectral graph convolutional neural network (SSGCNN) and channel based spectral graph convolutional network (CSGCNN) modules into a single end-to-end network. Therefore, our network was able to model feature representations from local, global spatial patterns and channel correlations. Experimental results on five synthetic and real-world datasets shows that the proposed network achieves state-of-the-art (SOTA) results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Attending Local and Global Features for image Caption Generation 7th

Attending Local and Global Features for Image Caption Genera...

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7th international conference on computer vision and image processing, cvip 2022

作者： Meghwal, Virendra Kumar Mittal, Namita Singh, Girdhari Malaviya National Institute of Technology Rajasthan Jaipur India

ISBN: (纸本)9783031314063

Attention based deep learning architectures have been used extensively for image captioning. Early methods used attention over global features for generating captions. After the advent of local or object-level features, most researchers started using local features as they provided better performance than global features. Thus the importance of global features in caption generation has been ignored in most current methods. Though the local features provide good performance, they may not adequately capture the underlying relations between different local features. On the contrary, the global features can capture the relation-ship between different object/local features of the image and may provide essential cues for better caption generation. This work proposes a method that utilizes mean global features to attend to salient local features while generating caption. The global features are extracted using a CNN, and local features are extracted using a Faster RCNN. The proposed decoder utilizes these features to generate the caption. Experiments carried out on the MSCOCO dataset indicate that our approach generates better captions indicating the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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