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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Sasaki, Yoko Thompson, Simon Kaneyoshi, Masahito Kagami, Satoshi Natl Inst Adv Ind Sci & Technol Digital Human Res Ctr Koto Ku Tokyo 1350064 Japan

ISBN: (纸本)9781424466757

The paper presents a multiple sound sources mapping system from a robot embedded microphone array. The robot localizes sound direction and recognizes what sound it is while the robot is in motion. Then the system estimates the positions of the sound sources using triangulation from a short time period of directional localization results. Three key components are denoted: 1) accurate directional localization and separation of multiple sound sources using a microphone array 2) separated sound recognition from a several tens of milliseconds input signal 3) sound position estimation using the random sample consensus (RANSAC) algorithm from a tracked sound stream. By combining these techniques, the proposed system provides surrounding sound information: "Where does the sound come from?" and "What is the sound?". It works with short term signal input, and is helpful to initially notice surrounding events.

关键词： RANSAC audio signal processing cartography direction-of-arrival estimation directional localization microphone array microphone array microphone arrays mobile robot mobile robots position measurement random sample consensus algorithm sound position estimation sound recognition sound sources mapping system

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Research on the DSmT Based Model of the Ultrasonic Sensor Detection for Indoor Environment Contour 35

Research on the DSmT Based Model of the Ultrasonic Sensor De...

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第35届中国控制会议

作者： YUAN Shuai MAO Hongmin GUO Song ZHANG Feng YAO Xiao School of Information and Control Engineering Shenyang Jianzhu University

ISBN: (纸本)9781509009107

Due to the existence of the ultrasonic sensor distance error and angle error causing environment contour detection uncertainty,the ultrasonic distance measurement mode based on the foundation of the uncertainty representation was firstly proposed and the data uncertainty of ultrasonic measurement was reduced by the Dezert-Smarandache Theory（DSmT）.Then Hough transform and random sample consensus algorithm are employed to identify the data fusion results to obtain environmental contour ***,the experiment of ultrasonic sensors scanning the indoor environment are performed,and the contours information of indoor environment obtained by using the proposed method is consistent with the size of true *** result validly illustrates the feasibility and effectiveness of the proposed *** proposed method has certain reference value for studying the localization and environment map detection of the mobile robot by using ultrasonic sensor.

关键词： DSmT ultrasonic sensor data fusion Hough transform random sample consensus algorithm

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NO-SHAM: An effective tool based on a novel hybrid approach to detect copy-move forgery in images 4

NO-SHAM: An effective tool based on a novel hybrid approach ...

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4th IEEE-Uttar-Pradesh-Section International Conference on Electrical, Computer and Electronics (UPCON)

作者： Singh, Lal Upendra Pratap Agrawal, Anupam Indian Inst Informat Technol Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781538630044

the need for establishing authenticity of digital images is becoming inevitably important given the ease with which images may be tempered. Moreover, the images are tampered with such expertise that it is impossible, at least visually, to figure out if they are tampered or not. In recent years, copy-move forgery has emerged as one of the most researched topics in the field of image forensics. The common detection techniques used are either block based (for smoothed regions) or keypoint based (for non-smoothed regions), each having its own share of pros and cons. While a block based approach provides higher accuracy, it is computationally taxing and fails to handle geometric transformations. Similarly, a Keypoint based approach fails to deal with smoothed regions. Our hybrid approach handles these limitations in an intelligent and adaptive way. The image fed to our software tool is adaptively segmented into semantically meaningful non-overlapped regions/segments using Simple Linear Iterative Clustering (SLIC) algorithm. Feature points as keypoints are extracted using the Scale Invariant Feature Transform (SIFT) algorithm. Given these keypoints, a segment is classified as either smoothed or non-smoothed depending upon a predetermined threshold. Once done with this classification, the proposed hybrid approach engages one of the aforementioned detection strategies to detect image forgery. Accordingly, a block based approach is implemented using Zernike moments with the matching algorithm based on Euclidean distances, while a keypoint based approach is implemented using SIFT features in tandem with the FLANN matching algorithm for the detection of matching pairs. The proposed hybrid approach has been compared with the individual approaches for an optimal value of threshold. Experimental results on different types of original as well as forged images establish that our proposed approach is able to detect image forgery in smoothed and non-smoothed images with reasonable ac

关键词： Scale Invariant Feature Transform (SIFT) Zernike Moments Superpixel Simple Linear Iterative Clustering (SLIC) FLANN Matching algorithm Precision Recall random sample consensus algorithm

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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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Real Time Static/Dynamic Obstacle Detection for Visually Impaired Persons

Real Time Static/Dynamic Obstacle Detection for Visually Imp...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Tapu, R. Mocanu, B. Zaharia, T. IT Telecom SudParis ARTEMIS Dept UMR CNRS 8145 MAP5 Evry France

ISBN: (纸本)9781479912919

In this paper we introduce a novel framework for detecting static/moving obstacles in order to assist visually impaired/blind persons to navigate safely. Firstly, a set of interest points is extracted base on an image grid and tracked using the multiscale Lucas - Kanade algorithm. Next, the camera/background motion is determined through a set of homographic transforms, estimated by recursively applying the RANSAC algorithm on the interest point correspondence while other types of movements are identified using an agglomerative clustering technique. Finally, obstacles are classified as urgent/normal based on their distance to the subject and motion vectors orientation. The experimental results performed on various challenging scenes demonstrate that our approach is effective in videos with important camera movement, including noise and low resolution data.

关键词： feature extraction handicapped aids motion estimation object detection object tracking pattern clustering recursive estimation video cameras RANSAC algorithm agglomerative clustering technique blind persons camera movement camera-background motion homographic transforms image grid interest point extraction low resolution data motion vector estimation motion vector orientation multiscale Lucas-Kanade algorithm random sample consensus algorithm real-time static-dynamic obstacle detection static-moving obstacle detection visually impaired persons Cameras Classification algorithms Estimation Real-time systems Robustness Tracking Videos

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Robust Video Stabilization based on Motion Intention for Low-Cost Micro Aerial Vehicles

Robust Video Stabilization based on Motion Intention for Low...

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11th International Multi-Conference on Systems, Signals and Devices (SSD)

作者： Aguilar, Wilbert G. Angulo, Cecilio UPC BarcelonaTech Dept Automat Control Barcelona Spain

ISBN: (纸本)9781479938667

Currently, different hand-held devices as domestic cameras, smart-phones, tablets, or on-board cameras for robots are becoming popular for video capturing. A main concern with these gadgets is undesired movement between consecutive frames. Video stabilization is a technique with increasing impact for solving this problem. In this paper, a proposal is introduced for robust video stabilization, in particular for on-board cameras in micro aerial vehicles. It is based on a combination of the RANSAC (random sample consensus) algorithm and gray level differences as cost function for local motion parameter estimation, as well as a low-pass filter for global motion smoothing. Experimentation will illustrate about of the robustness proposed solution.

关键词： autonomous aerial vehicles cameras image motion analysis microrobots mobile robots robot vision smart phones video signal processing RANSAC algorithm cost function domestic cameras global motion smoothing local motion parameter estimation low-cost microaerial vehicles low-pass filter motion intention on-board cameras random sample consensus algorithm robust video stabilization smart-phones tablets video capturing Cameras Cost function Estimation Mathematical model Motion estimation Robot vision systems Robustness

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A Fast Method for Feature Matching Based on SURF

A Fast Method for Feature Matching Based on SURF

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2nd Sino-Foreign-Interchange Workshop on Intelligence Science and Intelligent Data Engineering (IScIDE)

作者： Jiang, Zetao Wang, Qiang Cui, Yanru Nanchang Hangkong Univ Sch Informat Engn Nanchang Peoples R China

ISBN: (纸本)9783642319181;9783642319198

Stereo matching is currently one of the most important research topics in domain of computer vision. The improved SURF based on stereo matching algorithm is proposed in this paper, in order to match feature points more efficiently and accurately. The procedure of this method is following: Firstly, we used the algorithm based on Speeded-Up Robust Features (SURF) to detect and descript the feature points of image sequence, used normalized correlation (NCC) for the initial match. Secondly, we eliminated mismatching points by using random sample consensus algorithm (RANSAC). Lastly, we used the least square method for precision matching. Three Experiments and table analysis show that the matching accuracy of this algorithm is better than the traditional SIFT, SURF based on stereo matching algorithm and the running time is quite fast. So, it can be used in the pure software feature-point-based stereo vision system.

关键词： Rapid matching SIFT Speeded-up robust features (SURF) random sample consensus algorithm The least square method

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Fingerprint pore matching based on sparse representation

Fingerprint pore matching based on sparse representation

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International Conference on Pattern Recognition

作者： Liu, Feng Zhao, Qijun Zhang, Lei Zhang, David Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9780769541099

This paper proposes an improved direct fingerprint pore matching method. It measures the differences between pores by using the sparse representation technique. The coarse pore correspondences are then established and weighted based on the obtained differences. The false correspondences among them are finally removed by using the weighted RANSAC algorithm. Experimental results have shown that the proposed method can greatly improve the accuracy of existing methods. © 2010 IEEE.

关键词： coarse pore correspondence fingerprint identification fingerprint pore matching image matching image representation pore matching random sample consensus algorithm sparse representation sparse representation weighted RANSAC (WRANSAC) weighted RANSAC algorithm

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Research on image feature matching algorithm based on feature optical flow and corner feature

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 529-534页

作者： Song, Tao Chen, Bing Zhao, Fu M. Huang, Zheng Huang, Mu J. Chongqing Univ Technol Chongqing Key Lab Modern Photoelect Detect Techno Chongqing 400054 Peoples R China

Image feature matching is an important part of visual odometry. In order to improve the accuracy of feature point matching for visual odometry, an accurate algorithm combining the pyramid feature optical flow and corner features based on image feature matching is proposed. Firstly, the Oriented Features from Accelerated Segment Test and Rotated BRIEF (ORB) algorithm is used to extract the image feature points quickly. Secondly, the local feature window is utilised to calculate the displacement vector of the image feature points and the pyramid Lucas-Kanade feature optical flow is used to track feature points. Then, to solve the problem of matching alignment and feature loss, the K nearest neighbour radius search is used as feature filter to remove the confused matching. Finally, the random sample consensus algorithm is introduced to eliminate redundant mismatch points and improved the matching rate. The comparison of experimental data shows that the proposed algorithm can get high matching rate. Compared with the traditional ORB feature matching algorithm, the algorithm had a significant improvement in real time and image feature matching accuracy.

关键词： image sequences image matching feature extraction nearest neighbour methods corner feature visual odometry local feature window feature filter image feature matching algorithm feature optical flow feature point matching Oriented Features from Accelerated Segment Test and Rotated BRIEF displacement vector K nearest neighbour radius search random sample consensus algorithm redundant mismatch points matching rate

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A Way Based On Within-class Scatter Matrix to Improve RANSAC algorithm

A Way Based On Within-class Scatter Matrix to Improve RANSAC...

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2012 International Conference on Computer Science and Mathematics,Physical Education and Management（ICCMPE 2012）

作者： Miao Bao Dept. of Software and Microelectronics,NWPU Xi’an, P.R. China Haigang Zhu Information school of electronics,NWPU Xi’an, P.R. China Bo Chen Dept. of Software and Microelectronics,NWPU Xi’an, P.R. China

RANSAC (random sample consensus) algorithm is widely used in the field of image mosaic. However its efficiency is not satisfying when it is processing massive date .So, to improve the efficiency of this algorithm without dropping its accuracy is of great significance. And that is what our paper aims at .We adopt within-class scatter matrix to primarily select date in order to effectively improve the arithmetic speed and reduce the iterations. Our experiment shows that the speed is improved by 20 percent without reducing the accuracy.

关键词： random sample consensus algorithm Within- class scatter matrix Image mosaic

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