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2019第十一届数字图像处理国际会议

作者： Xiangyu Li Tianjie Lei Chun Zhao Jintao Huang Man Yuan Jiabao Wang China Institute of Water Resources and Hydropower Research(IWHR) North China University of Water Resources and Electric Power(NCWU)

In order to solve the problems of poor stability and multiple mismatching points in image registration,most scholars have used Random Sample Consensus(ransac) algorithm to optimize the matching ***,because of the randomness of the ransac algorithm itself,the matching algorithm has poor stability,low registration efficiency and poor *** solve this problem,an improved SIFT(Scale-invariant feature transform) image registration optimization algorithm based on PROSAC(Progressive Sampling Consensus) was *** experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image *** the same image to test,the average correct registration rate of the traditional ransac improved SIFT algorithm was 82%,and the average running time was 36 *** average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%,the average running time was 26.51 seconds,and the running efficiency was increased by 36%.Therefore,the improved SIFT image registration algorithm based on PROSAC has higher robustness,can meet the needs of fast image mosaic,and has broad application prospects.

关键词： Image stitching SIFT algorithm ransac algorithm PROSAC algorithm

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Real-time camera orientation estimation based on vanishing point tracking under Manhattan World assumption

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2017年第4期13卷 669-684页

作者： Elloumi, Wael Treuillet, Sylvie Leconge, Remy Univ Orleans Lab PRISME 12 Rue Blois F-45067 Orleans France

This paper proposes a real-time pipeline for estimating the camera orientation based on vanishing points for indoor navigation assistance on a Smartphone. The orientation of embedded camera relies on the ability to find a reliable triplet of orthogonal vanishing points. The proposed pipeline introduces a novel sampling strategy among finite and infinite vanishing points with a random sample consensus-based line clustering and a tracking along a video sequence to enforce the accuracy and the robustness by extracting the three most pertinent orthogonal directions while preserving a short processing time for real-time application. Experiments on real images and video sequences acquired with a Smartphone show that the proposed strategy for selecting orthogonal vanishing points is pertinent as our algorithm gives better results than the recently published RNS optimal method, in particular for the yaw angle, which is actually essential for the navigation task.

关键词： Vanishing point Real-time camera orientation ransac algorithm Navigation assistance Smartphone

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Feature Points Extraction and Matching Based on Improved Surf algorithm

Feature Points Extraction and Matching Based on Improved Sur...

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IEEE International Conference on Mechatronics and Automation

作者： Wei Chen Qian Cao Institute of automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong Province China

Speed up Robust Features (SURF) algorithm is an effective feature point extraction algorithm and its high efficiency and high stability are superior to similar algorithms, so it is widely used in image feature point extraction. However, due to the shortcomings of the SURF algorithm itself, it is easy to produce a mismatch phenomenon when performing feature point matching. When the image to be matched and the template image are quite different, the mismatch phenomenon is particularly obvious. In order to eliminate the mismatch, this paper improves the SURF algorithm by adding the ransac algorithm that eliminates the mismatched points. Experimental results show that the improved SURF algorithm can effectively eliminate false matching points.

关键词： SURF algorithm ransac algorithm Feature point extraction Feature points match

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AUTOMATIC CENTERLINE EXTRACTION OF COVERD ROADS BY SURROUNDING OBJECTS FROM HIGH RESOLUTION SATELLITE IMAGES 2

AUTOMATIC CENTERLINE EXTRACTION OF COVERD ROADS BY SURROUNDI...

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ISPRS Int Joint Conf of 2nd Conf on Geospatial Information Research GI Research, 4th Conf on Sensors and Models in Photogrammetry and Remote Sensing (SMPR), 6th Conf on Earth Observation of Environmental Changes (EOEC)

作者： Kamangir, Hamid Momeni, Mehdi Satari, Mehran Univ Isfahan Fac Civil Engn & Transportat Dept Geomat Esfahan Iran

This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on ransac algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

关键词： Maximum Likelihood Classification Connected Component ransac algorithm Morphological Operators Road Extraction Minimum Map Unit (MMU) concept

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Improving Track Initiation Performance by Track Validation algorithms for Multi-Target Tracking in Heavy Clutter 25

Improving Track Initiation Performance by Track Validation A...

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Sonmez, Hasan Huseyin Turgut, Kubra Hocaoglu, Ali Koksal Gebze Tekn Univ Elekt Muhendisligi Kocaeli Turkey

ISBN: (纸本)9781509064946

In this study, for multi-target tracking in an heavy clutter environment, line detection-based validation algorithms are proposed for improving track initiation performance by reducing false tracks. Track validation algorithm is applied to the results of the base track initiation algorithm periodically and deviation from line evaluated based on a defined measure. Then, by thresholding the deviation value, true and false tracks are discriminated, even for maneuvering targets. Thus, performance of both track initiation and tracker are improved by means of number of false tracks.

关键词： multi target tracking track initiation Hough transform ransac algorithm

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Real - time Monitoring for the Mining Robot Based On an Improved SIFT Matching algorithm 10

Real - time Monitoring for the Mining Robot Based On an Impr...

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10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： He Kai Ma Xiamin Xian Univ Sci & Technol Sch Elect & Control Xian Shaanxi Peoples R China

ISBN: (纸本)9781538619377

In this paper, aiming at the problem of 3D real - time Monitoring for the coal inspection robot in the environment of coal underground. An improved SIFT matching algorithm to improve the correctness of the feature matching and the rate of the feature matching is put forward, which combines the simplified SIFT matching operator, the geometric constraint condition and the ransac algorithm. Firstly the simplified SIFT matching operator is used to match the feature, then the wrong matching pair is removed by combining the geometric constraint condition and the ransac algorithm. The experimental simulation results show that the improved SIFT matching algorithm has a good effect on the 3D real - time Monitoring for the coal underground inspection robot.

关键词： Coal inspection robot SIFT matching algorithm ransac algorithm geometric constraint

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ransac algorithm with Sequential Probability Ratio Test for Robust Training of Feed-Forward Neural Networks

RANSAC Algorithm with Sequential Probability Ratio Test for ...

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International Joint Conference on Neural Networks (IJCNN)

作者： El-Melegy, Moumen T. Assiut Univ Dept Elect Engn Assiut 71516 Egypt

ISBN: (纸本)9781424496365

This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network (MFNN). Almost all previous efforts to solve this problem have focused on using a training algorithm that minimizes an M-estimator based error criterion. However the robustness gained from M-estimators is still low. Using a training algorithm based on the RANdom SAmple Consensus (ransac) framework improves significantly the robustness of the algorithm. However the algorithm typically requires prolonged period of time before a final solution is reached. In this paper, we propose a new strategy to improve the time performance of the ransac algorithm for training MFNNs. A statistical pre-test based on Wald's sequential probability ratio test (SPRT) is performed on each randomly generated sample to decide whether it deserves to be used for model estimation. The proposed algorithm is evaluated on synthetic data, contaminated with varying degrees of outliers, and have demonstrated faster performance compared to the original ransac algorithm with no significant sacrifice of the robustness.

关键词： Adaptation models Approximation algorithms Computational modeling Data models M-estimator based error criterion Neural networks ransac algorithm Robustness Training learning (artificial intelligence) multilayer perceptrons multilayered feedforward neural network random sample consensus algorithm robust neural network training sequential probability ratio test statistical analysis

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Rough Surface Wear Analysis Using Image Processing Techniques

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IFAC-PapersOnLine 2016年第31期49卷 7-12页

作者： Takimoto, Rogério Yugo de Sales Guerra Tsuzuki, Marcos Ueda, Edson Kenji Sato, André Kubagawa de Castro Martins, Thiago Cousseau, Tiago Tanaka, Deniol Sinatora, Amilton Escola Politécnica da Universidade de São Paulo Mechatronics and Mechanical Systems Engineering Department Computational Geometry Laboratory São Paulo Brazil Escola Politécnica da Universidade de São Paulo Mechatronics and Mechanical Systems Engineering Department Surface Phenomema Laboratory São Paulo Brazil United States

Due to the great interest of the automotive industry to quickly and systematically evaluate low wear quantities a new approach based on image processing is presented. By comparing the captured rough surface point cloud using optical interferometry before and after wear, the proposed algorithm was able to estimate the amount of volume wear and mass loss. The proposed algorithm has two main steps. In the first step, the 3D point clouds obtained before and after wear are mapped to 2D images, and conventional edge detection algorithms are applied. Keypoints are determined using the SIFT algorithm on both images (before and after the wear), followed by the use of the ransac algorithm to create a good 2D registration between both 2D images. In the second step an enhanced ICP algorithm is used to perform a 3D registration. The main advantage of this method is the visualization of the wear geometry provided by the wear topography. The proposed algorithm was tested and the results are presented. © 2016

关键词： Edge detection Automotive industry Image processing Image registration Optical data processing Surface measurement 3D registration Edge detection algorithms Image processing technique Optical interferometry Point cloud registration ransac algorithm SIFT SIFT algorithms

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An algorithm for Fast Corner Points Extraction with Reducing Mistaken Matching Based on Harris Operator

An Algorithm for Fast Corner Points Extraction with Reducing...

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2017 5th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering(ICMMCCE 2017)

作者： Xiaoyuan Chen Yong Zhu Bo Xiao Yuqiang Dai Jin Duan School of Electronic and Information Engineering Changchun University of Science and Technology School of Computer Science and Technology Changchun University of Science and Technology

The paper proposes a new methods which used in image matching Harris corner extraction and corner matching. On the basis of the original Harris algorithm, the efficiency of extracting corners is improved by reducing the number of computing the corner response value. Then the NCC algorithm is used to match the extracted corners, and the matching accuracy is improved by choosing the threshold(auto correlation coefficient). The experimental results show that the proposed algorithm reduces the time of corner extraction, and is superior to the traditional Harris corner extraction method. Moreover, when matching the two images with NCC matching algorithm, the mismatching is reduced. Finally, the ransac algorithm is used to remove the false matches.

关键词： Harris operator corner detection NCC algorithm ransac algorithm

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Kannada Handwritten Word Conversion to Electronic Textual Format Using HMM Model 1

Kannada Handwritten Word Conversion to Electronic Textual Fo...

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1st IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS)

作者： Sushma, A. Veena, G. S. M S Ramaiah Intstitute Technol Dept Comp Sci Bangalore Karnataka India

ISBN: (纸本)9781509010226

This project specifies a Hidden Markov model-based approach which is considered for identifying off-line words of kannada language which is written by hand. After preprocessing method, an image of word will be segmented to letters or each word in a line is segmented into frames. The procedure of this segmentation technique is used for transforming express image into frames which are in sequences. Preprocessing techniques includes binarization. From a line which consists of about 3 to 4 words in a line a technique is used which identifies each word from that line which uses green formula. The words are classified based on aspect ratio. Each word image is represented by its contour information. The system is used to first extract a set of robust features on binary handwritten images by using SIFT as well as feature detection and description( ORB). Later the system learns word HMM models using training samples from the Feature extraction. Finally, best word which maximizes the a posterior will be located through HMM. Because of the nature of the writing kannada handwritten word recognition is a challenging task. The recognition task of kannada words is prone to problems because of problems of many difficulties, such as the overlap, variability of character shape and the presence of ligatures in the word.

关键词： Word segmentation Line segmentation Feature detector and descriptor(ORB) feature extraction method ransac algorithm HMM model

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