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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Liu Jie Du Haiqing Liu Yong Wang Kaili Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing Peoples R China

ISBN: (纸本)9781467390262

The beverage showcase that is used to store cold drinks is ubiquitous in supermarkets, which have brought great convenience. However, a great disadvantage of the beverage showcase is that it needs manual check of remaining volume. Therefore, a recognition system is proposed in this paper to solve this tricky problem and to make our lives more intelligent. Positions, brands and the number of zip-top cans could be identified easily in the recognition system which monitors the dynamics of beverages in the beverage showcase. After starting the system, the key points detector that matches input picture and feature dataset is first executed. It will recognize zip-top cans that are less partially occluded or not covered. Next, the system carries out the rims detector to detect all upper rims of zip-top cans, which determine positions and the number of cans. In the rims detector, we propose an evaluation algorithm for the qualified ellipse. Then the colors detector detects main color blocks near the upper rims of zip-top cans to determine brands of cans covered each other. Three detectors are combined to constitute a recognition system with high accuracy and practical application value. In the system, the recognition rate is above 85%.

关键词： recognition system zip-top cans rims detector ransac algorithm evaluation algorithm

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VIDEO STRUCTURING: FROM PIXELS TO VISUAL ENTITIES

VIDEO STRUCTURING: FROM PIXELS TO VISUAL ENTITIES

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20th European Signal Processing Conference (EUSIPCO)

作者： Tapu, Ruxandra Zaharia, Titus CNRS Inst Mines Telecom Telecom SudParis ARTEMIS Dept UMR MAP5 8145 F-91000 Evry France

ISBN: (纸本)9781467310680

In this paper we propose a method for automatic structuring of video documents. The video is firstly segmented into shots based on a scale space filtering graph partition method. For each detected shot the associated static summary is developed using a leap key-frame extraction method. Based on the representative images obtained, we introduce next a combined spatial and temporal video attention model that is able to recognize moving salient objects. The proposed approach extends the state-of-the-art image region based contrast saliency with a temporal attention model. Different types of motion presented in the current shot are determined using a set of homographic transforms, estimated by recursively applying the ransac algorithm on the interest point correspondence. Finally, a decision is taken based on the combined spatial and temporal attention models. The experimental results validate the proposed framework and demonstrate that our approach is effective for various types of videos, including noisy and low resolution data.

关键词： Saliency maps temporal attention model homography transform ransac algorithm

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Performance Assessment of Robust & Efficient Video Stabilization algorithms based on L1-L2 Optimization and s-R-t Transform 4

Performance Assessment of Robust & Efficient Video Stabiliza...

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4th International Conference for Convergence in Technology (I2CT)

作者： Kulkarni, S. M. Bormane, D. S. Jha, Chankya Kumar JNTU Kakinada Kakinada India PVPIT E&Tc Pune Maharashtra India AISSMS Coll Engn Pune Maharashtra India Savitribai Phule Pune Univ Pune Maharashtra India

ISBN: (纸本)9781538652329

Video stabilization is the method of removing unwanted movement from a video stream. In this paper, we have proposed three algorithms for stabilization of jittery videos. 1. Video stabilization based on L1 norm 2. Video stabilization based on s-R-t transform 3. Video stabilization based on L1&L2 norm The first algorithm is based on L1 norm. L-1 norm is related with Least Absolute Deviation (LAD).It is minimising sum of absolute difference between consecutive video frames. In the second algorithm, hybrid technique which is the combination of ransac (Random Sample consensus algorithm) and s-R-t (scale-rotation-translation) transform is proposed to stabilize jittery videos. ransac algorithm is used to find effective inlier correspondences and afterward it derives the affine transformation to map the inliers in consecutive video frames. This transformation is capable to improve the image plane. This transform makes smoothening of video frames and also removes jitter in video. To obtain the optimal camera path composed of distinct constant, linear and parabolic segments, we have minimised the first, second, and third derivatives of the resulting camera path. The third algorithm based on L1-L2-norm. L2 optimization achieves the best estimation in least square sense. In order to keep the boundary information of original videos as much as possible optimal smooth camera path should be close to the original path. lambda is a weight to adjust the smoothness of path. It can be treated as a factor which controls the degree of stabilization. Comparing the stabilized and shaky video it is confirmed that the processed videos highly satisfy the human perception. Results indicate a remarkable elimination of high jitter from shaky videos.

关键词： Video Stabilization L1 optimization L1-L2 optimization ransac algorithm Inlier and outliers correspondences Affine Transform s-R-t Transform

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A Drug Administration Decision Support System

A Drug Administration Decision Support System

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： You, Wenqi Simalatsar, Alena Widmer, Nicolas De Micheli, Giovanni Ecole Polytech Fed Lausanne Integrated Syst Lab CH-1015 Lausanne Switzerland Univ Lausanne Ctr Hosp CH-1015 Lausanne Switzerland

ISBN: (纸本)9781467327466;9781467327459

Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (ransac) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.

关键词： Decision Support System Support Vector Machine ransac algorithm

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Identification and reading method of pointer meter based on CBAM D2-Net 19

Identification and reading method of pointer meter based on ...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Liu, Hongli Shen, Qingyang Shao, Lei Li, Ji Tianjin Univ Technol Sch Elect Engn & Automat Tiahlin Key Lab Control Theory & Applicat Complic Binshui Xidao 391 Tianjin Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

In order to read the ambiguous and reflective pointer instruments collected on site, a D2-net model based on CBAM is proposed, and an attention mechanism is introduced into the feature extraction network to focus the feature point detection on the salient part to capture more It has a good visual structure, and takes the angle method as the core, and uses the ransac algorithm to fit the angle of the instrument for interpretation. Experiments show that the accuracy of the instrument identification and interpretation method in this paper is within the allowable error range in all cases, and the registration and reading of all samples are successful, and there is no obvious fluctuation in reflective interference and image blurring. The comparison shows that for the highest stability.

关键词： CBAM D2 net pointer meter image registration ransac algorithm

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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 Coarse-to-fine Horizon Detection Method Based on Between-class Variance 9

A Coarse-to-fine Horizon Detection Method Based on Between-c...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Gui, Yang Lin, Dongsheng Liang, Shaojun Xu, Luoting Hong, Junxin Wuhan Mech Technol Coll Wuhan Peoples R China

ISBN: (纸本)9781509035588

A coarse-to-fine horizon detection method based on between-class variance is present. Firstly, the original image is resized to a smaller one and plenty of straight lines are selected acoording to two line parameters which are distance to image center and inclination angle in the resized image. A criterion based on the between-class variance is set up for determining the best straight line which is considered as the coarse horizon. Then several horizontal coordinates are selected in the original image equally. The points corresponding to the horizontal coordinates which are the most likely on the real horizon are found nearby the coarse horizon according to the edge information and between-class variance. Finally, ransac algorithm is applied to preserve interior points which are really on the horizon and eliminate exterior points which are not, and line parameters of the real horizon can be gained by applying least squares line fitting algorithm for all interior points. The experimental results show that the proposed method can detect out the horizons under various complicated backgrounds effectively and has many advantages such as speedy calculation, strong robustness and high engineering value.

关键词： horizon detection coarse-to-fine between-class variance ransac algorithm

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Feature point matching of infrared and visible image 43

Feature point matching of infrared and visible image

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Conference on Applications of Digital Image Processing XLIII

作者： Li, Wuxin Chen, Qian Gu, Guohua Bai, Hong-yang Sui, Xiubao Nanjing Univ Sci & Technol Sch Elect Engn & Opt Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Nanjing 210094 Jiangsu Peoples R China

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

Feature point matching has been widely applied in image registration, image fusion, remote sensing and other fields. The relation between the pixels of infrared images and pixels of visible images is complex due to the images were taken by different sensors. Different sensors images also contain some common information which can been depended to achieve point matching. Scale Invariant Feature Transform (SIFT) algorithm is an effective and popular feature extraction algorithm. SIFT algorithm can be used in point matching, it can get feature descriptor vectors of the feature points which extracted from the images. But in some scenes, SIFT algorithm can't achieve the accurate feature point matching. Different from SIFT algorithm, Edge-Oriented-Histogram (EOH) algorithm characters the orientation information of the edge and EOH feature descriptor can integrate the boundary information in different directions around the feature points, so that we can realize the describe of the edge direction and amplitude by EOH algorithm. To achieve the accurate feature point matching of infrared image and visible image, we propose a feature point matching method based on SIFT algorithm and EOH algorithm. Firstly, we use image enhancement to increase contrast of the image and then we use SIFT algorithm to extract feature points. Next, we utilize the EOH algorithm to get the 80 bins descriptors of the feature points which detected by SIFT algorithm. We calculate Euclidean distance to get the similarity of the descriptors to achieve point matching. In order to improve the matching accuracy, we adopt Random Sample Consensus (ransac) algorithm to eliminate the mismatching points. Nevertheless, ransac can't get all correct points, we use another measure of Euclidean to select correct pairs, then we combine the two parts to match the feature points again. Experimental results demonstrate that our method can effectively improve the accuracy of matching and find more correct matching point pai

关键词： SIFT algorithm EOH descriptor Euclidean distance feature point matching mismatching points feature descriptor ransac algorithm second matching

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A Methodology of Vehicle Speed Estimation Based on Optical Flow

A Methodology of Vehicle Speed Estimation Based on Optical F...

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Xu Qimin Li Xu Wu Mingming Li Bin Song Xianghui Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Minist Transport Res Inst Highway Beijing 100088 Peoples R China

ISBN: (纸本)9781479960583

It has great significance to acquire vehicle speed for active safety system. This paper presents a methodology for identifying vehicle speed by obtaining a sparse optical flow from image sequences. Distinct corners can be detected by Harris corner detector after image enhancement. Then, Lucas-Kanade method for optical flow calculation is utilized to match the sparse feature set of one frame on the consecutive frame. In order to improve the accuracy of optical flow, ransac algorithm is introduced to optimize the matched corners. Finally, the vehicle speed can be determined by averaging all the speeds estimated by every optimized matched corner. The results of field test indicated that the computation time of the developed method to execute for one time was 59ms, and the mean error of speed estimation relative to the measurement of GPS was 0.121 m/s. The developed method can achieve satisfying performance, such as accuracy and output frequency.

关键词： Global Positioning System edge detection estimation theory image enhancement image sequences road safety traffic engineering computing vehicles GPS Harris corner detector Lucas-Kanade method ransac algorithm active safety system optical flow vehicle speed estimation Adaptive optics Computational modeling Integrated optics Optical imaging Optical sensors Optimization Q measurement Global Positioning Systems image sequences Optic Flow image enhancement Active safety systems Q measurement Vehicle speed Speed Estimation estimation theory adaptive optics optical sensors Integrated optics Optical Imaging Road safety Computational modeling

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A Feature Line Extraction Method for Building Roof Point Clouds Considering the Grid Center of Gravity Distribution

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REMOTE SENSING 2024年第16期16卷 2969-2969页

作者： Yu, Jinzheng Wang, Jingxue Zang, Dongdong Xie, Xiao Liaoning Tech Univ Sch Geomat Fuxin 123000 Peoples R China Liaoning Tech Univ Collaborat Innovat Inst Geospatial Informat Serv Fuxin 123000 Peoples R China Chinese Acad Sci Inst Appl Ecol Key Lab Environm Computat & Sustainabil Liaoning P Shenyang 110016 Peoples R China

Feature line extraction for building roofs is a critical step in the 3D model reconstruction of buildings. A feature line extraction algorithm for building roof point clouds based on the linear distribution characteristics of neighborhood points was proposed in this study. First, the virtual grid was utilized to provide local neighborhood information for the point clouds, aiding in identifying the linear distribution characteristics of the center of the gravity points on the feature line and determining the potential feature point set in the original point clouds. Next, initial segment elements were selected from the feature point set, and the iterative growth of these initial segment elements was performed by combining the ransac linear fitting algorithm with the distance constraint. Compatibility was used to determine the need for merging growing results to obtain roof feature lines. Lastly, according to the distribution characteristics of the original points near the feature lines, the endpoints of the feature lines were determined and optimized. Experiments were conducted using two representative building datasets. The results of the experiments showed that the proposed algorithm could directly extract high-quality roof feature lines from point clouds for both single buildings and multiple buildings.

关键词： feature line extraction point clouds virtual grid eight-neighborhood ransac algorithm

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