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Improved ORB Algorithm Based on Binocular Vision 5

Improved ORB Algorithm Based on Binocular Vision

引用

5th IEEE International conference on Computer and communications, ICCC 2019

作者： Li, Chao Jia, Yongxing Wang, Huali Rong, Chuanzhen Zhu, Ying Army Engineering University of PLA College of Communications Engineering Nanjing China

ISBN: (纸本)9781728147437

In digital image processing, feature extraction is a very important step, and ORB algorithm is the best one in real-time performance, but it is not up to the standard in the system with higher real-time requirements. Through binocular vision calibration, we will delve into how to speed up the operation of ORB algorithm with ensuring accuracy. First, the overlapping regions of the left and right images are extracted, and then the images are subjected to morphological operations, and corner detection is performed at the edges. Secondly, the original description method is improved, and the description sub-selects the window value based on the area ring, which is more in line with the characteristics of the human eye and improves the operation speed. Then, a hierarchical matching strategy is adopted, combined with binocular visual constraints, the matching range is defined, and the accuracy is effectively improved. Finally, construct an experimental platform to process two images and compare them with existing algorithms. The experimental results show that the proposed method has much less computational time consumption than the original algorithm under the condition of improved accuracy, and is suitable for fast matching environment. © 2019 IEEE.

关键词： Binocular vision

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SAR image Fast Online ATR Based on visual Attention and Scale Analysis 1

引用

4th International conference on Cognitive Systems and Information processing, ICCSIP 2018

作者： Wang, Hongqiao Cai, Yanning Yao, Junyi Zhang, Shaolei Fu, Guangyuan Xi’an Research Institute of Hi-Tech Xi’anShaanxi710025 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

Aiming at the online automatic target recognition for SAR images, a visual attention based scale analysis method is introduced for the feature extraction and the classifiers construction. By improving the features adaptability and robustness on speckle, a local multi-resolution analysis method is introduced for feature extraction of the SAR targets. Also a non-biased multi-scale LSSVC model is proposed to furthermore improve the classification performance. On this basis, a fast online learning algorithm based on Cholesky factorization is studied and applied to the SAR image recognition, which has smaller computation complexity than the common method with matrix inversion. Experiments testify that the presented method can give better classification precision and online performance. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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A Research of Feature Matching Algorithm Based on Monocular Vision SLAM 4

A Research of Feature Matching Algorithm Based on Monocular ...

引用

4th IEEE International conference on image, Vision and Computing, ICIVC 2019

作者： Tian, Wen Wang, Zhiqiang Zhu, Qing Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9781728123257

In recent years, with the progress of image processing technology, the research on Simultaneous Localization and Mapping (SLAM) has gradually become a hot topic in the field of computer vision. visual SLAM is mainly divided into front end and back end. In this study, aiming at the problem of low efficiency of feature point matching, an improved fast feature approximation nearest neighbor feature matching method is proposed, which can obtain higher precision image matching relationship in a short time. At the same time, an algorithm of Cluster Sampling Consensus (CLUSAC) algorithm is proposed, which reduces the probability of mismatching and makes camera pose estimation more accurate. Finally, a comparative experiment of the algorithm is performed. The results show that the improved feature matching algorithm and mismatching elimination algorithm can effectively improve the accuracy of feature matching and help to provide accurate and effective initial values for the back end. © 2019 IEEE.

关键词： image enhancement

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4th International conference on Cognitive Systems and Information processing, ICCSIP 2018

4th International Conference on Cognitive Systems and Inform...

引用

4th International conference on Cognitive Systems and Information processing, ICCSIP 2018

ISBN: (纸本)9789811379826

The proceedings contain 96 papers. The special focus in this conference is on Cognitive Systems and Information processing. The topics include: How to Understand the Basic Graphic Analysis Method;image Elementary Manifold and Its Application in image Analysis;SAR image Fast Online ATR Based on visual Attention and Scale Analysis;image Artistic Style Transfer Based on Color Distribution Preprocessing;depthwise Separable Convolution Feature Learning for Ihomogeneous Rock image Classification;an Efficient Network for Lane Segmentation;optimize Expert Knowledge Acquisition with Attribute Calculation: How to Understand Twin Turing Machine;an Unsupervised Joint Model for Claim Detection;how to Understand the Basic Structural Unit;a Novel Remote Sensing image Classification Method Based on Semi-supervised Fuzzy C-Means;algorithms for Maximum Lifetime of Covering Path;active Learning-Based Semi-supervised Spectral Clustering Algorithm;secondary Filtering Update Algorithm for Nonlinear Systems—Based on Extended Kalman Filter;probabilistic Context-Aware Ambients;how to Understand Three Types of Cognitive Models;intelligent Measurement and Management of Aircraft Structural Deformation;Design and FPGA Implementation of an High Efficient XGBoost Based Sleep Staging Algorithm Using Single Channel EEG;how to Understand the Mathematical Basis of Two Basic Types of Computational Behavior;improved Direct Chaotic Nonlinear Filters;a Hotel Review Corpus for Argument Mining;vision-Based Joint Attention Detection for Autism Spectrum Disorders;the Formal Understanding Models;Application of PSO-LSSVM in Bias Correction of Shipborne Anemometer Measurement;two-Stage Weighted Regularized Extreme Learning Machine for Class Imbalanced Learning;covering Path with Minimum Movement: Complexity and Algorithms;Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intelligence.

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Feature Fusion Approach for Differently Exposed images with Weighted Guided Filter 2nd

Feature Fusion Approach for Differently Exposed Images with ...

引用

2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Vijayan, Aparna Bindu, V.R. M. G. University KottayamKerala India

ISBN: (纸本)9789811391804

Multi-exposure image fusion methodologies collect image information from multiple images and convey to a single image. Fusion with the aid of edge aware smoothing filters is a new treanding area. The difficultes of multi-scale processing and low level fusion operations are the main problems of the existing algoritms. In this paper we propose a novel multi-exposure image fusion method which uses a feature fusion method based on an edge aware weighted guided filter. Three important image features accounting for the quality of an image viz. contrast, sharpness and exposedness are extracted from the differently exposed input images and fused together to form a single saliency map which holds all the important information. A decision map is constructed for the fused feature and an efficient edge aware filtering technique called weighted guided filter is used for optimizing the obtained decision map. A two scale decomposition of input images is done in parallel with the initial feature extraction procedure. This decomposed image representation is fused with the optimized decision map to get the final result. The proposed method encompasses the advantages of simple two scale decomposition, optimization with edge weighting and simplicity of using a single fused feature. The experimental results and objective evaluations demonstrate that the proposed method can produce more accurate results with very good visual quality. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image fusion

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Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features

Discrimination of Haploid and Diploid Maize Seeds Based on D...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Emrah Dönmez Mühendislik Fakültesi İnönü Üniversitesi Malatya Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

One of the main processes in in vivo maternal haploid breeding method which is widely used in hybrid maize breeding in recent years is the separation of haploid and diploid individuals. Although different approaches have been proposed to make this distinction, the R1-nj color marker is widely and successfully used. The R1-nj color marker allows the visual separation of haploid and diploid individuals during the seed period. Nowadays, this distinction is done manually, causing loss of time and labor as well as high error. In this study, an open access dataset consisting of 3,000 maize seed images was used. The deep features from the FC6, FC7 and FC8 fully connected layers of the AlexNet architecture are classified with the support vector machine. 10-fold cross-validation test was used to evaluate model performances. Experimental results showed that the best classification performance is possible with 89.50% accuracy using deep features obtained from the FC6 fully connected layer.

关键词： Support vector machines Kernel Mathematical model In vivo image color analysis Convolutional neural networks visualization

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14th conference on image and Graphics Technologies and Applications, IGTA 2019

14th Conference on Image and Graphics Technologies and Appli...

引用

14th conference on image and Graphics Technologies and Applications, IGTA 2019

ISBN: (纸本)9789811399169

The proceedings contain 66 papers. The special focus in this conference is on image and Graphics Technologies and Applications. The topics include: Pixel and Channel Attention Network for Person Re-identification;Infrared and Visible image Matching Algorithm Based on SIFT and LDB;the Overview of 2D to 3D Automatic Conversion;sugarcane Node Identification Based on Structured Learning Model;3D Human Pose Estimation with Grouping Regression;vascular Model Editing for 3D Printing Based on Implicit Functions;Design and Implementation of Hardware Accelerator for Gaussian Filter Based on HLS;fusion of Global and Local Gaussian-Hermite Moments for Face Recognition;A visual-Inertial Information Fusion Method for SLAM Front-End Odometry;a Multiscale image Denoising Algorithm Based on Dilated Residual Convolution Network;Improved RPN for Single Targets Detection Based on the Anchor Mask Net;Model-Driven Dynamic visualization of Spatiotemporal Data in GIS;DCNN Transfer Learning and Multi-model Integration for Disease and Weed Identification;3D Human Pose Lifting: From Joint Position to Joint Rotation;estimation of 6Dof Pose Using image Mask and Bounding Box;multi-Attention Network for 2D Face Alignment in the Wild;virtual-Real Fusion processing Based on Parallel Distribution;exploring a Facial Defect Skin Detection Algorithm with Probability Distribution Model;universal Framework of Seals Erasing with Generative Adversarial Network;pupil Segmentation Method Based on Treasure Theory and Region Growing;object Recognition and Localization Base on Binocular Vision;design and Analysis of Object Behavior in Media Content-User Relationship Network Model;Effects of Dynamic Disparity on visual Fatigue Caused by Watching 2D Videos in HMDs.

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26th International conference on Neural Information processing, ICONIP 2019

26th International Conference on Neural Information Processi...

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26th International conference on Neural Information processing, ICONIP 2019

ISBN: (纸本)9783030368012

The proceedings contain 111 papers. The special focus in this conference is on Neural Information processing. The topics include: Dense image Captioning Based on Precise Feature Extraction;improve image Captioning by Self-attention;dual-Path Recurrent Network for image Super-Resolution;attention-Based image Captioning Using DenseNet Features;High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention;image Generation Framework for Unbalanced License Plate Data Set;cross-Domain Scene Text Detection via Pixel and image-Level Adaptation;modified Adaptive Implicit Shape Model for Object Detection;an Expert Validation Framework for Improving the Quality of Crowdsourced Clustering;a Label Embedding Method for Multi-label Classification via Exploiting Local Label Correlations;discriminant Feature Learning with Self-attention for Person Re-identification;the Inadequacy of Entropy-Based Ransomware Detection;attention Network for Product Characteristics Prediction Based on Reviews;semi-supervised Regularized Coplanar Discriminant Analysis;multi-object Tracking with Conditional Random Field;intra-Modality Feature Interaction Using Self-attention for visual Question Answering;reinforcement Learning with Attention that Works: A Self-Supervised Approach;budget Cost Reduction for Label Collection with Confusability Based Exploration;prediction of Refactoring-Prone Classes Using Ensemble Learning;analysis on Dropout Regularization;on a Convergence Property of a Geometrical Algorithm for Statistical Manifolds;SCS: Style and Content Supervision Network for Character Recognition with Unseen Font Style;anomaly Detection Based on the Global-Local Anomaly Score for Trajectory Data;patch Selection Denoiser: An Effective Approach Defending Against One-Pixel Attacks;demographic Prediction from Purchase Data Based on Knowledge-Aware Embedding;addressing Reward Engineering for Deep Reinforcement Learning on Multi-stage Task;High-Speed Synchronization of Pulse-Coupled

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image-based Text Classification using 2D Convolutional Neural Networks

Image-based Text Classification using 2D Convolutional Neura...

引用

IEEE conference on SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation

作者： Merdivan, Erinc Vafeiadis, Anastasios Kalatzis, Dimitrios Hanke, Sten Kropf, Johannes Votis, Konstantinos Giakoumis, Dimitrios Tzovaras, Dimitrios Chen, Liming Hamzaoui, Raouf Geist, Matthieu Austrian Inst Technol GmbH Wiener Neustadt Austria Ctr Res & Technol Hellas Inst Informat Technol Thessaloniki Greece De Montfort Univ Fac Comp Engn & Media Leicester Leics England Univ Lorraine CNRS LIEC F-57000 Metz France Google Brain Mountain View CA USA Univ Lorraine CNRS CentraleSupelec LORIA F-57000 Metz France

ISBN: (纸本)9781728140346

We propose a new approach to text classification in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations of the visual patterns of words. Our approach demonstrates that it is possible to get semantically meaningful features from images with text without using optical character recognition and sequential processing pipelines, techniques that traditional natural language processing algorithms require. To validate our approach, we present results for two applications: text classification and dialog modeling. Using a 2D Convolutional Neural Network, we were able to outperform the state-of-art accuracy results for a Chinese text classification task and achieved promising results for seven English text classification tasks. Furthermore, our approach outperformed the memory networks without match types when using out of vocabulary entities from Task 4 of the bAbI dialog dataset.

关键词： Convolutional neural networks Deep learning Dialogue modelling Text classification

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ctapipe: a low-level data processing framework for the Cherenkov Telescope Array 36

ctapipe: a low-level data processing framework for the Chere...

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36th International Cosmic Ray conference (ICRC)

作者： Kosack, K. Peresano, M. CEA Paris Saclay Astrophys Dept Paris France

The low-level data processing of the Cherenkov Telescope Array (CTA) and indeed all other existing Cherenkov Telescopes can be broken into four general steps: 1) the processing of air-shower event image time-series, 2) the stereo reconstruction of the incident air showers, and 3) the discrimination of gamma-ray induced showers from those from cosmic rays 4) the determination of the overall system response. The final output for science users is a list of reconstructed gamma-ray-like events and their associated parameters, along with a set of instrumental response functions needed for doing astrophysics. We present a python-based framework, ctapipe, for writing the algorithms required for these processing steps along with a reference prototype pipeline. The code is written with a focus on simplicity and usability by developers with a diverse range of skill sets, and leverages existing code from the science community (AstroPy, SciPy/NumPy, SciKit-Learn, etc). This concept is intended to be a prototype for the final CTA low-level data processing pipeline, allowing physicists to quickly explore low-level Cherenkov telescope data and develop new algorithms. Thanks to the framework modularity, computer engineers and data scientists will be able to simultaneously optimize the algorithms and parallelize them using modern computing and big-data architectures to support the high data volumes of CTA.

关键词： Gamma rays

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