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A new approach for distorted image restoration by distance-based correction and cubic spline interpolation 2

A new approach for distorted image restoration by distance-b...

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2nd international conference on Watermarking and image processing, ICWIP 2019

作者： Qi, Min Yu, Guanrong Xu, Yuelei School of Electronics and Information Northwestern Polytechnical University No. 1 of Dongxiang Road Dongda Town Chang'an District Xi'an Shaanxi China Unmanned System Research Institute Northwestern Polytechnical University 127 Youyi west Road Beilin District Xi'an Shaanxi China

ISBN: (纸本)9781450372800

Fish-eye lenses are used more and more widely in the field of photography and computer vision nowadays. However, images captured with these lenses suffer from high barrel-type spatial distortion, so severely errors are brought into the camera calibration, feature extraction, parameter calculation and following analyses. This paper proposes a new and effective method to correct the high distortion. With the improved model in this paper, the degree of correction changes in accordance with the distance from the center pixel location. And the inverse mapping from corrected images space to the distorted images space based on the cubic spline interpolation method is taken to eliminate the vacant pixels in the corrected images caused by forwarding mapping. The final correction results are satisfactory by using this improved method. © 2019 Association for Computing Machinery.

关键词： image reconstruction

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3D Reconstruction of Plants Under Outdoor Conditions Using image-Based computer vision 2nd

3D Reconstruction of Plants Under Outdoor Conditions Using I...

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

作者： Paturkar, Abhipray Gupta, Gaurab Sen Bailey, Donald School of Engineering and Advanced Technology Massey University Palmerston North New Zealand

ISBN: (纸本)9789811391866

3D reconstruction of plants under outdoor conditions is a challenging task, for applications such as plant phenotyping which needs non-invasive methods. With the availability of new sensors and reconstructions techniques, 3D reconstruction is improving rapidly. However, sensors are still expensive for researchers. In this paper, we propose a cost-effective image-based 3D reconstruction approach which can be achieved by off-the-shelf cameras. This approach is based on the structure-from-motion method. We implemented this approach in MATLAB and Meshlab is used for further processing to achieve an exact 3D model. We also investigated the effect of different adverse outdoor scenarios which affect quality of 3D model such as movement of plants because of strong wind, drastic change in light condition while capturing the images. We have decreased the appropriate number of images needed to get precise 3D model. This method gives accurate results and it is a fast platform for non-invasive plant phenotyping. © 2019, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Research Progress in image Denoising Algorithms Based on Deep Learning 2

Research Progress in Image Denoising Algorithms Based on Dee...

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2nd international conference on computer Information Science and Application Technology, CISAT 2019

作者： Ruikai, Cai College of Electronic and Information Engineering Electronic Information Engineering Shenzhen university518061 China

images constitute the main source of information to people. Digital images are vital means of obtaining, processing, analyzing, and sharing information in the era of information. Now they have been deeply incorporated into every aspect of people's production and life, generating considerable social and economic benefits. Thus improving the quality of images and reducing the negative impact of image noises to subsequent image processing have been two important research topics. image processing technology has been combined with such research fields as cognitive psychology, machine learning, machine vision, and deep learning in recent years, which lead to an unprecedented development level and breakthroughs. Therefore, the study of image denoising technology has profound theoretical significance and promising prospects in practical application. The paper mainly discusses the development course of applying deep learning technology to the image denoising field. Meanwhile, it introduces various classical denoising algorithms and focuses on the thoughts, advantages, and disadvantages of each algorithm. Furthermore, it discusses the challenges faced by deep learning in the denoising field and puts forward possible solutions. © Published under licence by IOP Publishing Ltd.

关键词： image denoising

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An Adaptive image processing Model of Plant Disease Diagnosis and Quantification Based on Color and Texture Histogram

An Adaptive Image Processing Model of Plant Disease Diagnosi...

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international conference on computer & Information Science (ICCIS)

作者： Waqar Ismail Muhammad Attique Khan Syed Adnan Shah Muhammad Younus Javed Amjad Rehman Tanzila Saba HITEC University Taxila Pakistan University of Engineering and Technology Taxila Pakistan AIDA Lab CCIS Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

In this paper, a new approach for the detection and classification of potato plant disease is implemented using computer vision techniques. Most of the existing algorithms based on plant disease detection and classification are limited to common types of feature extraction methods. However, feature extraction is an important area as the classification of diseases of any leaf. The proposed method is based on color and texture features. The implemented method processed in four steps- In the preprocessing and segmentation, LAB color space and Delta E color difference method are applied. Later, features are extracted based on RGB, HSV and Local Binary Patterns (LBP). The extracted patterns are finally classified by Multi Support Vector Machine (SVM). Moreover, we compare the results of feature subsets of RGB and HSV color features with the addition of LBP texture features and found a classification difference of 3.6% between RGB and HSV color feature extractors. The overall results show our method outperforms as compared to existing techniques.

关键词： image color analysis Feature extraction Diseases image segmentation Support vector machines Histograms Mathematical model

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The Biometrics System Based on Iris image processing: A Review 2

The Biometrics System Based on Iris Image Processing: A Revi...

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2nd international conference of computer and Informatics Engineering (IC2IE) - Artificial Intelligence Roles in Industrial Revolution 4.0

作者： Khoirunnisaa, Alfi Zuhriya Hakim, Lutfi Wibawa, Adhi Dharma Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia Politekn Negeri Banyuwangi Dept Informat Engn Banyuwangi Indonesia Inst Teknol Sepuluh Nopember Dept Comp Engn Surabaya Indonesia

ISBN: (纸本)9781728123844

Recently, it reported that the cases of cybercrime had increased by about 13% in 2017, especially in data fraud crime, due to the poor security system. One of the solutions to this problem is by implementing a biometric system as a security platform. Biometric is a method of personal ID authentication by using human recognition using unique human biometric values such as fingerprint, voice, face, or iris. This review paper focused on the iris biometric system since Iris biometric widely used as a system for maintaining data security, such as ATM, cellular phone, etc. Moreover, the biometric has very high sensitivity and accuracy for recognition than the other. In this paper, we will review some experiments of iris recognition that was done by researchers. As a result, we will present some comparisons among several studies, including in feature extraction technique, performance, and time computation required on the matching process. From the paper review, we can analyze that a good result of iris recognition depends on the selection of the right features and the needed time computation. The use of Haar Wavelet (texture analysis) with three different iris databases showed the best performance, such as MMU (ACC = 96.44%, time = 0.34s), UTIRIS (ACC = 96.21%, time = 0.68s), and IITD (ACC = 97.14%, time = 0.64s).

关键词： iris recognition system iris biometric system iris feature extraction iris image processing iris authentication

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A driverless vehicle vision path planning algorithm for sensor fusion 2

A driverless vehicle vision path planning algorithm for sens...

引用

2nd IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2019

作者： Wang, Xinyu Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728150291

Visual orientation can extend human vision to machines. Combined with high-precision devices, it achieves high-precision, strong-intelligence, and fast-responding location awareness. It faces the problem that processing capacity cannot adapt to dynamic environment changes, and the single vision system cannot perceive the distance in non-line-of-sight scene. Therefore, this thesis carries out the research on a driverless vehicle visual path planning algorithm for sensor fusion. Firstly, via the analysis of satellite timing, inertial devices, navigation and positioning, it provides a research basis for the time perception, orientation perception and location perception of driverless vehicles. Combined with the practical application scenarios of driverless vehicles, this thesis analyzes the causes of errors in driverless vehicle positioning. It also designs a normalized pseudo-range processing method based on the fusion of heterogeneous sensor to realize the visual positioning of multiple sensor fusion. Through MATLAB simulation verification, it improves the visual positioning accuracy performance based on the proposed algorithm. © 2019 IEEE.

关键词： Motion planning

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Research on Textual Classification of Medical History in Electronic Patient Records based on LSTM 2

Research on Textual Classification of Medical History in Ele...

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2nd international conference on image and Video processing, and Artificial Intelligence (IPVAI)

作者： Zhuo, Yirong Wu, Haimei Cao, Dong Ye, Hui Guangzhou Univ Chinese Med Sch Med Informat Engn Guangzhou Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Natural Language processing (NLP) is an important direction in the field of computer science and artificial intelligence. Combining with deep learning, NLP can effectively transform unstructured natural language into structured data. The electronic medical records of hospitals are mainly used in clinic, and the data of electronic medical records need to be reorganized to carry out research. This paper mainly studies the automatic classification and extraction of medical history information fields based on Convolutional neural network (CNN) and Long Short-Term Memory network (LSTM), aiming at solving the problem of traditional Chinese medicine. The classification problem of automatic extraction of all medical history information from mixed text information of medical records. The experimental results show that the F value is 0.8506 based on Convolutional Neural Network ( CNN) and 0.8810 based on LSTM, which has good classification effect.

关键词： Word2Vec LSTM Medical Records History Extraction

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image retrieval based on convolutional neural network and linear discriminate analysis 2

Image retrieval based on convolutional neural network and li...

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2019 2nd international conference on Advanced Materials, Intelligent Manufacturing and Automation, AMIMA 2019

作者： Xia, Wei Hu, Cong Cheng, Xianghui Chen, Peng Jin, Xing School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval. © Published under licence by IOP Publishing Ltd.

关键词： Search engines

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Direct-ORB-SLAM: Direct Monocular ORB-SLAM 2

Direct-ORB-SLAM: Direct Monocular ORB-SLAM

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2nd international conference on computer Information Science and Application Technology, CISAT 2019

作者： Cui, Linyan Ma, Chaowei Wen, Fei Image Processing Center School of Astronautics Beihang University Beijing102200 China

Simultaneous Localization and Mapping (SLAM) plays an important role in the computer vision and robotics field. The well-known ORB-SLAM extracts ORB features and uses feature matching to estimate the camera pose and construct a sparse map. However, the features extracted in the common frame (relative to the keyframe) are not used again except in this camera pose estimation stage, which will cause the waste of computation resources. In this work, we propose a new SLAM framework, i.e. Direct-ORB-SLAM. It uses image intensity for data association in common frames to avoid the redundant computations and performs the relocalization and loop closure detection in ORB-SLAM2 for keyframes. The photometric calibration is also employed to benefit for accuracy and robustness. The popular public dataset EuRoC is adopted to approve the efficiency of proposed method. Results indicate that the proposed Direct-ORB-SLAM algorithm runs two times faster than the well-known ORB-SLAM at the expense of a slight reduction in accuracy. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Cameras

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Real-time Lane Detection, Fitting and Navigation for Unstructured Environments 2

Real-time Lane Detection, Fitting and Navigation for Unstruc...

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2nd international conference on image and Video processing, and Artificial Intelligence (IPVAI)

作者： Singhal, Apoorve Mohta, Vibhakar Jha, Arvind Khandelwal, Yash Agrawal, Deepank Kowshik, Shreyas Agarwal, Siddhant Shrivastava, Shrey Lodhi, Vaibhav Chakravarty, Debashish IIT Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India IIT Kharagpur Dept Mech Engn Kharagpur W Bengal India IIT Kharagpur Dept Met Mat Engn Kharagpur W Bengal India IIT Kharagpur Dept Chem Engn Kharagpur W Bengal India IIT Kharagpur Dept Math Kharagpur W Bengal India IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Dept Min Engn Kharagpur W Bengal India

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In the outdoor environment, robot perception is a challenging task encompassing several layers of abstractions like lane detection, object detection and avoidance, and way-point navigation. Intelligent Ground Vehicles are becoming popular and having an efficient perception stack is quintessential to its scaling for different tasks. Several issues like illumination variance, shadows, occlusions, etc. cause researchers to adopt computationally heavy approaches for improving generalization. We present a novel, real-time approach for combined lane detection, obstacle detection, and way-point navigation using features from a 2D-LiDAR and camera. A robust curve fitting algorithm has been implemented, adhering to the minimization of computation. The overall processing pipeline has been tested and validated to work well in outdoor conditions.

关键词： image processing Lane Detection computer vision RANSAC Low computation Lane Classification Robotics Autonomous Driving

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