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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

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image Scoring: Patch Based CNN Model for Small or Medium Dataset 3

Image Scoring: Patch Based CNN Model for Small or Medium Dat...

引用

3rd ieee International conference on Computer and communications (ICCC)

作者： Cheng, Jianmei Zhang, Chao Sichuan Police Coll Luzhou Peoples R China

ISBN: (纸本)9781509063529

image scoring focuses on visual classification or regression which assigns each image a category or precise score. As deep learning is becoming more and more popular, large manually labeled data sets are required. This makes the task time-consuming, and is difficult for small or medium dataset. In this paper, we give a simple yet effective oversampling technique that considers both entire image and local patches. Oversampling is a well-known trick in deep learning, while in this paper it mainly focuses on the small-size patches instead of some large-size patches that cover the entire object of the image. We first crop an image into many small-size patches that can augment the initial dataset and also partially decrease the over-fitting in the training. The initial dataset and expanded dataset can separately be seen as global and local information for each image. Based on the expanded dataset, we can train standard Convolutional Neural Network (CNN) and patch based CNN (patCNN). In order to integrate the global and local information, we further combine two models to get better performance, called comCNN. The experimental results show the effectiveness of patCNN and comCNN compared with the state-of-the-art methods.

关键词： deep learning image scoring convolutional neural network (CNN)

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A Novel visual Similarity-based Phishing Detection Scheme using Hue Information with Auto Updating Database 25

A Novel Visual Similarity-based Phishing Detection Scheme us...

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25th Asia-Pacific conference on communications (APCC)

作者： Haruta, Shuichiro Yamazaki, Fumitaka Asahina, Hiromu Sasase, Iwao Keio Univ Dept Informat & Comp Sci Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (数字)9781728136790

ISBN: (纸本)9781728136790

In this paper, we propose a novel visual similarity-based phishing detection scheme using hue information with auto updating database. Since a PWS (Phishing Website) is created based on targeted legitimate website or other subspecies whose hue information is similar each other, many PWSs can be exhaustively detected by tracing similar colored subspecies. Based on this notion, the proposed scheme detects a new PWS which has similar hue information to already detected PWSs. By repeating this procedure, the detection scope can be effectively expanded. In order to avoid the misdetection of legitimate websites which have similar hue information to database 's ones, the proposed scheme utilizes the fact that the combination of used colors is hard to be similar among legitimate websites and PWSs. By the computer simulation with real dataset, we demonstrate that the proposed scheme improves the detection performance as the number of detected PWSs increases.

关键词： Phishing Color image color analysis visualization visual databases Uniform resource locators

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MAV Urban Localization from Google Street View Data

MAV Urban Localization from Google Street View Data

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Majdik, Andras L. Albers-Schoenberg, Yves Scaramuzza, Davide Univ Zurich Artificial Intelligence Lab Robot & Percept Grp CH-8006 Zurich Switzerland

ISBN: (纸本)9781467363587

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.

关键词： autonomous aerial vehicles computational complexity geographic information systems image matching microrobots object recognition query processing robot vision visual databases

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Simulation and assessment of bioinspired visual processing system for epi-retinal prostheses

Simulation and assessment of bioinspired visual processing s...

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28th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society

作者： Vurro, Milena Baselli, Giuseppe Orabona, Francesco Sandini, Giulio Dept Biomed Engn Politecn Milan I-20133 Milan Italy

ISBN: (纸本)9781424400324

Retinal prosthesis represent the best near-term hope for individuals with chronic blinding disease of the outer retina. However the small number of stimulating electrodes produces a poor, low resolution image. We propose a new preprocessing method for epi-retinal implants and validate it through a novel simulation of the implanted blind perception. Twenty-one normally sighted, untrained subjects performed a face recognition test. Three different electrodes grids were simulated: rectangular, hexagonal and log-polar. The results show that the proposed pre-processing, method has a good and statistically significant performance improvement.

关键词： image processing

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AUTOMATIC image ANNOTATION VIA LOCAL SPARSE CODING

AUTOMATIC IMAGE ANNOTATION VIA LOCAL SPARSE CODING

引用

ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhang, Wenbo Tian, Dongping Hu, Hong Zhao, Xiaofei Shi, Zhongzhi Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

ISBN: (纸本)9781479903566

Sparse coding is an active research topic in machine learning and signal processing community. In this paper, we propose a novel local sparse model for multi-label image annotation. Existing feature descriptors and extraction algorithms pay less attention to semantic information and extracted feature dimension usually is high, which leads to heavy computation. Noise and redundant information often reduce the performance of sparse model. To address these issues, we combine label and visual information for feature selection while most previous work only utilizes labels and ignores visual information itself. First of all, we make use of label sets to seek images neighbor relations and generate the Gaussian kernel matrix over these neighbor images, then use LLP(Local Learning Projection) algorithm to get minimal local estimation error. After that, for each query image, we find its K nearest neighbors in the transformed space and use these neighbors to reconstruct it via sparse coding. Moreover, during coding, we penalize the corresponding reconstruction coefficients to implicitly reflect the neighbor relations. Finally, propagating tags from training data to test data. image annotation experiments on the Corel5k dataset show the performance of our approach is comparable to several state-of-the-art algorithms.

关键词： image annotation feature selection KN-N local sparse coding

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Improvements of image Retrieval-based visual Localization Using Structured Database

Improvements of Image Retrieval-based Visual Localization Us...

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2019 ieee International conference on Pervasive Computing and communications Workshops, PerCom Workshops 2019

作者： Akada, Ayari Takahashi, Junji Yong, Yu Dept. of Mechanical Engineering Kagoshima University Kagoshima Japan Dept. of Mechanical Engineering Graduate School of Science and Engineering Kagoshima University Kagoshima Japan

ISBN: (纸本)9781538691519

In this paper, we propose a method to improve image retrieve for visual localization by structuring the database. We are studying cloud-based positioning infrastructure system that we call Universal Map. It can reduce various cost as compared with the conventional technique. However, it takes time to estimate the position because the retrieval process is performed from a large amount of images in the database. To solve this problem, we reduce the retrieval time by structuring the database. We designed feature vector representing each image in the database and classified them using clustering method called K-means. We also made virtual sensing image and measured the Euclidean distance to each cluster in order to evaluate the classification results. As a result, the correct cluster was selected up to the third closest cluster. Therefore, we could reduce the retrieval time to 20% so far. © 2019 ieee.

关键词： Database systems

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Experimental Analysis of visual Inverted Pendulum Servoing System 16th

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Joint conference of the 16th Asia Simulation conference / SCS International Autumn Simulation Multi-conference (AsiaSim/SCS AutumnSim)

作者： Du, Dajun Zhan, Bin Li, Wangpei Fei, Minrui Yang, TaiCheng Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Univ Sussex Dept Engn & Design Brighton BN1 9QT E Sussex England

ISBN: (纸本)9789811026690;9789811026683

This paper is concerned with experimental analysis of visual inverted pendulum servoing system. Firstly, visual inverted pendulum servoing system is introduced, and three typical image processing algorithms are described. These three algorithms are then employed to process the image of inverted pendulum captured by camera. Comparative experiments are operated, and the detection precision and real time performance are analyzed. This lays a solid foundation for future control research of visual inverted pendulum servoing system.

关键词： Machine vision image processing Inverted pendulum Computational time Computational error

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Reconfigurable Peer-to-Peer network image Retrieval

Reconfigurable Peer-to-Peer network Image Retrieval

引用

2011 ieee visual communications and image processing, VCIP 2011

作者： Su, Chun-Rong Chen, Jiann-Jone Liu, Jun-Lin Shiue, De-Hui Electrical Engineering Department National Taiwan University of Science and Technology Taipei 10673 Taiwan Information and Communications Research Labs. Industrial Technology Research Institute HsinChu 310 Taiwan

ISBN: (纸本)9781457713200

Performing Content-Based image Retrieval (CBIR) on Internet connected databases through Peer-to-Peer (P2P) network (P2P-CBIR) effectively explores the large-scale image database distributed over connected peers. In additional to enlarge the retrieval scale from server-client to P2P networks, the required computation and network traffics for performing CBIR can also be distributed. Decentralized unstructured P2P framework is adopted to perform the P2P-CBIR in considering control flexibility, under which reconfiguration is feasible to improve the retrieval performance. Based on this framework, the proposed P2P-CBIR can progressively refine the accuracy of retrieved images when they are transmitted on the routes toward the query peer. With flexible P2P framework, reconfiguration can be carried out on the P2P-CBIR, which helps to improve retrieval efficiency. Experiments verified that the P2P-CBIR with reconfiguration outperforms the one without, and the latter outperforms server-client approaches in both retrieval accuracy and network traffics. © 2011 ieee.

关键词： Peer to peer networks

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Locally competitive algorithms for sparse approximation

Locally competitive algorithms for sparse approximation

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ieee International conference on image processing (ICIP 2007)

作者： Rozell, Christopher Johnson, Don Baraniuk, Richard Olshausen, Bruno Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA Univ Calif Los Angeles Helen Wills Neurosci Inst Los Angeles CA 90089 USA

ISBN: (纸本)9781424414369

Practical sparse approximation algorithms (particularly greedy algorithms) suffer two significant drawbacks: they are difficult to implement in hardware, and they are inefficient for time-varying stimuli (e.g., video) because they produce erratic temporal coefficient sequences. We present a class of locally competitive algorithms (LCAs) that correspond to a collection of sparse approximation principles minimizing a weighted combination of reconstruction MSE and a coefficient cost function. These systems use thresholding functions to induce local nonlinear competitions in a dynamical system. Simple analog hardware can implement the required nonlinearities and competitions. We show that our LCAs are stable under normal operating conditions and can produce sparsity levels comparable to existing methods. Additionally, these LCAs can produce coefficients for video sequences that are more regular (i.e., smoother and more predictable) than the coefficients produced by greedy algorithms.

关键词： approximation methods visual system image coding video coding nonlinear systems

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Accurate Segmentation of Brain Tumors in Magnetic Resonance images with Pyramid Stage Decomposition Network Approach 32

Accurate Segmentation of Brain Tumors in Magnetic Resonance ...

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32nd ieee Signal processing and communications Applications conference (SIU)

作者： Ari, Berna Gurler Uzen, Huseyin Sengur, Abdulkadir Inonu Univ Bilgisayar Muhendisligi Bolumu Malatya Turkiye Bingo Univ Bilgisayar Muhendisligi Bolumu Bingo Turkiye Firat Univ Elekt Elekt Teknol Bolumu Elazig Turkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978;9798350388961

This study explores the utilization of the Pyramid Scene Parsing Network (PSPNet) architecture to achieve accurate segmentation of brain tumors in magnetic resonance (MR) images. Experimental evaluations were conducted on different pre-trained backbone network models, including Vgg16, Inceptionv3, Mobilenetv2, Efficientnetb0, Resnet18, Resnet34, Resnet50, Resnet101, Resnext50, and Resnext101, assessing the performance of each model in brain tumor segmentation. The results highlight the VGG16-PSPNet model as the most successful, showcasing high F1-score, mIoU, precision, recall, and accuracy values.

关键词： Brain tumors Magnetic Resonance Imaging (MRI) Segmentation Pyramid Scene Parsing Network (PSPNet) Medical image Analysis

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