Developing an intelligent sign inventory using image processing
|
|
- Valentine Douglas
- 6 years ago
- Views:
Transcription
1 icccbe 2010 Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor) Developing an intelligent sign inventory using image processing Yichang (James) Tsai Georgia Institute of Technology, USA Abstract Roadway signs are assets important for roadway safety and traffic regulation. However, sign inventory data collection is time-consuming, costly, and sometimes dangerous. This paper presents the research results from our research project, sponsored by the National Academy of Science (NAS) NCHRP Innovation Deserving Exploratory Analysis (IDEA) program, to develop a sign detection algorithm using image processing. The sign detection algorithm is crucial for developing an intelligent sign inventory system. It is technically challenging to develop a generalized algorithm for detecting more than 670 types of signs specified by Manual on Uniform Traffic Control Devices (MUTCD). A generalized sign detection algorithm is developed based on color, shape, texture, and sign location Probability Distribution Function (PDF). The developed sign detection algorithm can eliminate the data collection efforts of reviewing the images containing no signs. This paper briefly presents the developed sign detection algorithm. The experimental test uses 1,105 actual video log images provided by the City of Nashville to validate the developed algorithm. Results show that the developed algorithm is very promising in reducing the manual review effort which can save time and money for roadway infrastructure data collection. Keywords: sign inventory, image processing 1 Introduction Collecting roadway infrastructure data, including traffic signs, such as stop signs and speed limit signs, is essential for state and local transportation agencies to plan, design, construct, operate, and manage transportation assets. Traffic signs are vital for roadway safety, and inventorying them is necessary for compliance with the Manual on Uniform Traffic Control Devices (MUTCD). However, the data collection process is time-consuming and costly. Current software reviews one image at a time, so extracting sign information from the millions of images is still time-consuming and hinders effective data collection. To remedy the problem of reviewing images frame by frame, there is a need to develop algorithms that can batch-process video log images and support an intelligent sign inventory and management system. Some algorithms are designed to handle traffic signs with specific shapes, such as rectangles and triangles (Ballerini et al., 2005; Zhu et al., 2006). Algorithms have been developed to detect and recognize traffic signs under unfavorable conditions (de la Escalera et al., 2003; Yang et al., 2003). Other algorithms have been developed to detect and recognize specific sign types, such as stop and speed limit signs (Tsai and Wu, 2002; Wu and Tsai, 2005; Wu and Tsai, 2006). Although these algorithms have been developed for automatically detecting and recognizing
2 some particular signs (e.g. stop signs and speed limit signs), they are not suitable for a comprehensive sign inventory because the algorithms are not generalized, and they are unable to recognize the more than 670 types of traffic signs on U.S roadways, a technically challenging job. Figure 1 shows an example in which a speed limit sign (25 mph) in a video log image was detected and recognized. Figure 1, Traffic sign data inventory using image processing algorithms This paper presents the research results obtained from our research project, sponsored by National Academy of Science (NAS) NCHRP Innovation Deserving Exploratory Analysis (IDEA) program, on developing a generalized sign detection algorithm using image processing. In this research project, two innovative, modularized algorithms, sign detection and sign recognition, are developed. They form a solid foundation for developing an intelligent sign inventory and management system. A twostep sign inventory data collection process is proposed to seamlessly incorporate these two algorithms for batch processing millions of video log images, which can save great amounts of time and significant costs. The generalized sign detection algorithm, the first step in the intelligent sign inventory and management system, is developed to reduce the efforts of reviewing the images containing no signs. The generalized sign recognition algorithm, the second step in the intelligent sign inventory and management system, is developed to extract sign attributes, such as sign type, MUTCD codes, etc. This paper focuses on sign detection. The paper is organized as follows. First, the developed sign detection algorithm is briefly described. The experimental test, using the actual video log images provided by City of Nashville, is then conducted and results are presented. Finally, conclusions and recommendations are made. 2 A generalized sign detection algorithm This section briefly presents the developed sign detection algorithm with a special focus on presenting the features/models that are selective to support sign detection. To detect the more than 670 types of signs, the proposed sign detection algorithm uses several features, including sign color, shape, location PDF, and other sign features, such as width-to-height ratio. Feature extraction is important for sign detection. Traffic signs have dominant color, shape, texture, and other attributes, which makes them different from the background. According to the MUTCD standard, traffic signs can have the following ten MUTCD colors: black, blue, brown, green, orange, red, white, yellow, fluorescent yellow-green (FYG) and fluorescent pink (FP). Also, traffic signs are shaped as triangles, rectangles, pentagons, octagons, circles, and crosses. Video log images, which were collected by state Departments of Transportation (DOTs) by using a survey vehicle, show that the traffic sign has the 2
3 non-uniform, dominant location distribution in an image plane. For example, a traffic sign doesn t appear on the left bottom and right bottom parts in the image. Also, other attributes, such as size, width-to-length ratio, etc. can be used. The following briefly presents the features used for sign detection and the details for sign color feature extraction and shape feature extraction as presented in Tsai et al., Color feature extraction Color is a very important feature for a traffic sign, since sign colors usually receive more attention from the drivers. However, actual sign color may vary because of different lighting, the camera used, and other imaging conditions. For example, the red colors for the same stop sign will have different Red, Blue and Green (RGB) values under different lighting conditions. As a result, sign colors in video log images have much broader color distribution than the MUTCD color specifications. Therefore, it is difficult to use any deterministic segmentation method to recognize the original MUTCD color class. A sophisticated model should be developed to describe the actual sign color distributions in the actual video log images so that we can evaluate the sign color in a more reliable and accurate way. In the algorithm, the Color Statistical Model (SCM), developed in our lab, is used for sign color processing. SCM is based on the specifications of the Manual Uniform of Traffic Control Device (MUTCD, 2008). SCM can successfully process the colors of sign background and legend, thereby providing reliable results for image segmentation and sign color feature analysis. SCM has good ability for general MUTCD sign color processing because it is based on the statistical colors that were collected from video log images and trained by the Artificial Neural Network (ANN) with Function Link Network (FLN) structure. The following paragraph presents the basic introduction to SCM. The SCM color model uses cases with a given input pixel value that has the probability of A to be a MUTCD color X and a probability of B to be an MUTCD color Y. The MUTCD SCM was first built statistically using labeled traffic sign color samples. The dataset for the experiment is excerpted from the LADOTD roadway video image sets. From 45,151 video log images captured under various outdoor lighting conditions in Louisiana, 3,023 images were identified as having a total of 5,052 traffic signs of 62 different types. All of the traffic signs were manually color labeled according to one of the 10 MUTCD colors. Finally, a total of 413,724 distinct samples and each reference count were used to build ground-truth probability. 2.2 Shape feature extraction Sign shape is another important feature for traffic sign detection. In this algorithm, we used the polygon approximation based algorithm for shape detection. We first identify the boundary region of a traffic sign and then analyze the features within the boundary region to determine if it is a candidate for identification as a traffic sign. Such procedures proceed from the fact that 99.4% of traffic sign types are convex, and 99.8% of those convex traffic signs have a limited number of vertices. Besides, even non-convex traffic signs (for example, the cut-out shield type) typically appear within information class traffic signs that have a rectangular boundary with a green background. As a result of such commonalities, strong assumptions can be made about traffic sign detection: (1) A traffic sign is convex, and (2) a traffic sign has a limited number of vertices. These assumptions lead to the conclusions that a traffic sign boundary becomes a polygon because a traffic sign is a twodimensional planar object and that boundary shape is also a plane figure with a limited number of vertices. The non-convex exceptions are rare. One example of such an exception is the X-shaped sign (W10-1) that occurs at rail crossings. However, a proprietary algorithm can be developed to detect such special objects and separate them from their backgrounds. The proposed shape feature extraction algorithm includes two steps. They are image preparation and binarization and nested contour chain detection for polygon approximation. 3
4 2.3 Sign location feature extraction The locations of traffic in the images are usually focused in several specific regions, such as the topright area, in a typical video log images. This is due to the fact that a sensor van usually goes along the roadway direction and the camera is fixed onto the van so that the locations of traffic signs demonstrate some distribution in the video log images. The main objective of sign location Probability Distribution Function (PDF) is to propose a quantities model to describe such location distribution. In such a model, a location, which corresponds to a pixel location in the image, has the probability score ranging from zero to one. High probability represents that it is more likely that traffic sign appear in such location. By introducing sign location PDF, it is useful to evaluate the sign location information. Besides, we can also use different thresholds for different applications. Figure 2 below shows a sign location distribution map, which was generated from various traffic signs in the actual video log images. We can see that the sign location demonstrates highly non-uniform distribution. Among the PDF image shown in Figure 2, it shows the sign location distributions from the manually tagged signs with a number of 1000 sign images. The darker of a location (or pixel) indicates the higher probability a traffic sign is likely to appear. It has demonstrated the phenomena of dominant non-uniform sign location distributions. In some areas, such as the bottom left and bottom right, the traffic sign would never appear. Figure 2, Sign location distribution from 1,000 images Sign location distribution is very usual for us to remove false positive in both traffic sign detection and recognition. The candidate detected in the location, where the sign location PDF is very low, such as on the roadway, at the right corner of the image, will be rejected with high probability by the algorithm. By utilizing such sign location constraints, the detection and recognition rates will be greatly improved. 3 Experiment test The section presents the experimental test on evaluating the performance of the developed sign detection algorithm. There are two basic criteria to evaluate the performance of the developed sign detection algorithm. They are false negative (FN) and false positive (FP). TP refers to true positive and TN refers to true negative. FN means that number of the image that contains sign but is detected as no-sign images. This is an indicator heavily related to system reliability. FP means that number of the non-sign image that is detected as having traffic sign. This is an indicator related to productivity (how much manual effort can be saved). The developed algorithm was tested with actual video log images provided by the City of Nashville. A total of 1,105 video log images are tested. The image resolution is 1300 * The 4
5 images were taken at an interval of 20 ft. (approx. 6 m) between two consecutive images. These images cover a distance of 15km. The testing site for these video log images is in an urban area, where the image backgrounds are very complicated with a lot of sign-like shapes and objects, e.g., the advertisements, the windows on the wall, and other non-traffic signs on the street. Among these images, 183 images have traffic signs, accounting for 12.3% of the total images. We used developed sign features of color, shape, location PDF, sign area, sign distortion angle, for traffic sign detection. The results are presented in Table 1 below. Table 1. Sign detection results from Nashville video log images Sect# TP TP % TN TN % FP FP % FN FN% Total The results show that the algorithm can achieve a zero false negative rate while keeping the false positive rate as low as 27.8%. Therefore, with the proposed algorithm, we can cut more than 72.2% of the images containing no signs that do not need manual review. These results further demonstrate that the proposed sign detection algorithm is very reliable even in the complicated environments. Based on the above discussion, if the algorithm outputs are reliable, agencies need to only review 439( ) out of total 1,105 images, which is approximately 39.7%. In other words, 60.3% of the workload in manual review can be saved with the proposed algorithm even in a very complicated roadway conditions, such as on a unban street. 5
6 4 Conclusions and recommendations This paper presents a generalized sign detection algorithm developed to detect the more than 670 types of sign specified in the MUTCD, which is a technically challenging. Sign detection is for the purpose of eliminating the images containing no sign and keeping the images containing signs to minimizing the efforts of reviewing images for collecting signs. After studying the MUTCD sign standard, we used the features of sign color, sign shape, sign location PDF, and other sign features. We developed an SCM color model to process MUTCD color for video log images. We used the polygon detection based algorithm to analyze sign shape. We statistically studied the sign location distribution in video log images and found that it has a non-uniform distribution. These features are generalized from video log images and MUTCD standard, which provide reliable and effective ways for sign detection. The proposed algorithm has been tested with the video log image provided by the City of Nashville. The results show that the algorithm can achieve 27.8% false positive rate while keeping zero false negative rate, which means that 72.2% of the workload for manually reviewing images are saved. As a result, the algorithm could greatly help users save time and improve efficiency, which could also enhance roadway infrastructure data collection for intelligent sign inventory system. Acknowledgements The work described in this paper was supported by the National Academy of Sciences (NAS), National Cooperative Highway Research Program (NCHRP), Innovations Deserving Exploratory Analysis (IDEA) program. The author would like to thank City Metro for providing the testing data and the data processed and analyzed by Dr. Zhaozheng Hu and Chengo Ai. References BALLERINI, R., CINQUE, L., LOMBARDI, L. and MARMO, R., Rectangular traffic sign recognition. Image Analysis and Processing - Iciap 2005, Proceedings 3617, DE LA ESCALERA, A., ARMINGOL, J. M. and MATA, M., Traffic sign recognition and analysis for intelligent vehicles. Image and Vision Computing 21, MANUAL ON UNIFORM TRAFFIC CONTROL DEVICES. FHWA, U.S. Department of Transportation, TSAI, Y. and WU, J., Shape- and texture-based 1-D image processing algorithm for real-time stop sign road inventory data collection. ITS Journal (Intelligent Transportation Systems) 7, TSAI, Y., KIM, P. and WANG Z., A Generalized Image Detection Model for Developing a Sign Inventory, ASCE Journal of Computing in Civil Engineering, Vol. 23, No. 5, pp WU, J. P. and TSAI, Y., Real-time speed limit sign recognition based on locally adaptive thresholding and depth-firstsearch. Photogrammetric Engineering and Remote Sensing 71, WU, J. P. and TSAI, Y., Enhanced roadway inventory using a 2-D sign video image recognition algorithm. Computer- Aided Civil and Infrastructure Engineering 21, YANG, H. M., LIU, C. L., LIU, K. H., and HUANG, S. M., Traffic sign recognition in disturbing environments. Foundations of Intelligent Systems 2871, ZHU, S. D., ZHANG, Y., and LU, X. F., Detection for triangle traffic sign based on neural network. Advances in Neural Networks - Isnn 2006, Pt 3, Proceedings 3973,
Critical Assessment of Automatic Traffic Sign Detection Using 3D LiDAR Point Cloud Data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Critical Assessment of Automatic Traffic Sign Detection Using 3D LiDAR Point Cloud Data Chengbo Ai PhD Student School of Civil and Environmental Engineering
More informationExtracting Road Signs using the Color Information
Extracting Road Signs using the Color Information Wen-Yen Wu, Tsung-Cheng Hsieh, and Ching-Sung Lai Abstract In this paper, we propose a method to extract the road signs. Firstly, the grabbed image is
More informationVehicle Dimensions Estimation Scheme Using AAM on Stereoscopic Video
Workshop on Vehicle Retrieval in Surveillance (VRS) in conjunction with 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance Vehicle Dimensions Estimation Scheme Using
More informationAN APPROACH OF SEMIAUTOMATED ROAD EXTRACTION FROM AERIAL IMAGE BASED ON TEMPLATE MATCHING AND NEURAL NETWORK
AN APPROACH OF SEMIAUTOMATED ROAD EXTRACTION FROM AERIAL IMAGE BASED ON TEMPLATE MATCHING AND NEURAL NETWORK Xiangyun HU, Zuxun ZHANG, Jianqing ZHANG Wuhan Technique University of Surveying and Mapping,
More informationScene Text Detection Using Machine Learning Classifiers
601 Scene Text Detection Using Machine Learning Classifiers Nafla C.N. 1, Sneha K. 2, Divya K.P. 3 1 (Department of CSE, RCET, Akkikkvu, Thrissur) 2 (Department of CSE, RCET, Akkikkvu, Thrissur) 3 (Department
More informationResearch of Traffic Flow Based on SVM Method. Deng-hong YIN, Jian WANG and Bo LI *
2017 2nd International onference on Artificial Intelligence: Techniques and Applications (AITA 2017) ISBN: 978-1-60595-491-2 Research of Traffic Flow Based on SVM Method Deng-hong YIN, Jian WANG and Bo
More informationTHE SPEED-LIMIT SIGN DETECTION AND RECOGNITION SYSTEM
THE SPEED-LIMIT SIGN DETECTION AND RECOGNITION SYSTEM Kuo-Hsin Tu ( 塗國星 ), Chiou-Shann Fuh ( 傅楸善 ) Dept. of Computer Science and Information Engineering, National Taiwan University, Taiwan E-mail: p04922004@csie.ntu.edu.tw,
More informationLane Detection using Fuzzy C-Means Clustering
Lane Detection using Fuzzy C-Means Clustering Kwang-Baek Kim, Doo Heon Song 2, Jae-Hyun Cho 3 Dept. of Computer Engineering, Silla University, Busan, Korea 2 Dept. of Computer Games, Yong-in SongDam University,
More informationAutomatic generation of 3-d building models from multiple bounded polygons
icccbe 2010 Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor) Automatic generation of 3-d building models from multiple
More informationText Information Extraction And Analysis From Images Using Digital Image Processing Techniques
Text Information Extraction And Analysis From Images Using Digital Image Processing Techniques Partha Sarathi Giri Department of Electronics and Communication, M.E.M.S, Balasore, Odisha Abstract Text data
More informationComputer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 18
Computer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 18 CADDM The recognition algorithm for lane line of urban road based on feature analysis Xiao Xiao, Che Xiangjiu College
More informationAN INTELLIGENT TRAFFIC CONTROLLER BASED ON FUZZY LOGIC
AN INTELLIGENT TRAFFIC CONTROLLER BASED ON FUZZY LOGIC Bilal Ahmed Khan; Nai Shyan Lai Asia Pacific University of Technology and Innovation belalkhn22@gmail.com Abstract Traffic light plays an important
More informationTRAFFIC LIGHTS DETECTION IN ADVERSE CONDITIONS USING COLOR, SYMMETRY AND SPATIOTEMPORAL INFORMATION
International Conference on Computer Vision Theory and Applications VISAPP 2012 Rome, Italy TRAFFIC LIGHTS DETECTION IN ADVERSE CONDITIONS USING COLOR, SYMMETRY AND SPATIOTEMPORAL INFORMATION George Siogkas
More informationSlops and Distances for Regular Shape Image Recognition
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IMPACT FACTOR: 5.258 IJCSMC,
More informationTraffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers
Traffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers A. Salhi, B. Minaoui, M. Fakir, H. Chakib, H. Grimech Faculty of science and Technology Sultan Moulay Slimane
More informationResearch Fellow, Korea Institute of Civil Engineering and Building Technology, Korea (*corresponding author) 2
Algorithm and Experiment for Vision-Based Recognition of Road Surface Conditions Using Polarization and Wavelet Transform 1 Seung-Ki Ryu *, 2 Taehyeong Kim, 3 Eunjoo Bae, 4 Seung-Rae Lee 1 Research Fellow,
More informationStudy on road sign recognition in LabVIEW
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Study on road sign recognition in LabVIEW To cite this article: M Panoiu et al 2016 IOP Conf. Ser.: Mater. Sci. Eng. 106 012009
More informationBus Detection and recognition for visually impaired people
Bus Detection and recognition for visually impaired people Hangrong Pan, Chucai Yi, and Yingli Tian The City College of New York The Graduate Center The City University of New York MAP4VIP Outline Motivation
More informationA New Rutting Measurement Method Using Emerging 3D Line-Laser-Imaging System
Technical Paper ISSN 1997-1400 Int. J. Pavement Res. Technol. 6(5):667-672 Copyright @ Chinese Society of Pavement Engineering A New Rutting Measurement Method Using Emerging 3D Line-Laser-Imaging System
More informationA Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay
A Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay VPQM 2010, Scottsdale, Arizona, U.S.A, January 13-15 Outline Introduction Proposed approach Colors
More informationPedestrian Detection Using Correlated Lidar and Image Data EECS442 Final Project Fall 2016
edestrian Detection Using Correlated Lidar and Image Data EECS442 Final roject Fall 2016 Samuel Rohrer University of Michigan rohrer@umich.edu Ian Lin University of Michigan tiannis@umich.edu Abstract
More informationTraffic sign shape classification evaluation II: FFT applied to the signature of Blobs
Traffic sign shape classification evaluation II: FFT applied to the signature of Blobs P. Gil-Jiménez, S. Lafuente-Arroyo, H. Gómez-Moreno, F. López-Ferreras and S. Maldonado-Bascón Dpto. de Teoría de
More informationCLASSIFICATION FOR ROADSIDE OBJECTS BASED ON SIMULATED LASER SCANNING
CLASSIFICATION FOR ROADSIDE OBJECTS BASED ON SIMULATED LASER SCANNING Kenta Fukano 1, and Hiroshi Masuda 2 1) Graduate student, Department of Intelligence Mechanical Engineering, The University of Electro-Communications,
More informationEXTRACTING TEXT FROM VIDEO
EXTRACTING TEXT FROM VIDEO Jayshree Ghorpade 1, Raviraj Palvankar 2, Ajinkya Patankar 3 and Snehal Rathi 4 1 Department of Computer Engineering, MIT COE, Pune, India jayshree.aj@gmail.com 2 Department
More informationCity of La Crosse Online Mapping Website Help Document
City of La Crosse Online Mapping Website Help Document This document was created to assist in using the new City of La Crosse online mapping sites. When the website is first opened, a map showing the City
More informationResearch on Applications of Data Mining in Electronic Commerce. Xiuping YANG 1, a
International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) Research on Applications of Data Mining in Electronic Commerce Xiuping YANG 1, a 1 Computer Science Department,
More informationNumerical Recognition in the Verification Process of Mechanical and Electronic Coal Mine Anemometer
, pp.436-440 http://dx.doi.org/10.14257/astl.2013.29.89 Numerical Recognition in the Verification Process of Mechanical and Electronic Coal Mine Anemometer Fanjian Ying 1, An Wang*, 1,2, Yang Wang 1, 1
More informationAN AUTOMATIC HORIZONTAL CURVE RADII MEASUREMENT METHOD FOR ROADWAY SAFETY ANALYSIS USING GPS DATA
Ai and Tsai 0 AN AUTOMATIC HORIZONTAL CURVE RADII MEASUREMENT METHOD FOR ROADWAY SAFETY ANALYSIS USING GPS DATA Chengbo Ai (corresponding author) Post-Doctoral Fellow School of Civil and Environmental
More informationSegmentation Framework for Multi-Oriented Text Detection and Recognition
Segmentation Framework for Multi-Oriented Text Detection and Recognition Shashi Kant, Sini Shibu Department of Computer Science and Engineering, NRI-IIST, Bhopal Abstract - Here in this paper a new and
More informationEE368 Project: Visual Code Marker Detection
EE368 Project: Visual Code Marker Detection Kahye Song Group Number: 42 Email: kahye@stanford.edu Abstract A visual marker detection algorithm has been implemented and tested with twelve training images.
More informationAPPLICATION OF AERIAL VIDEO FOR TRAFFIC FLOW MONITORING AND MANAGEMENT
Pitu Mirchandani, Professor, Department of Systems and Industrial Engineering Mark Hickman, Assistant Professor, Department of Civil Engineering Alejandro Angel, Graduate Researcher Dinesh Chandnani, Graduate
More informationHuman Detection. A state-of-the-art survey. Mohammad Dorgham. University of Hamburg
Human Detection A state-of-the-art survey Mohammad Dorgham University of Hamburg Presentation outline Motivation Applications Overview of approaches (categorized) Approaches details References Motivation
More informationDevelopment of system and algorithm for evaluating defect level in architectural work
icccbe 2010 Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor) Development of system and algorithm for evaluating defect
More informationAUGMENTING GPS SPEED LIMIT MONITORING WITH ROAD SIDE VISUAL INFORMATION. M.L. Eichner*, T.P. Breckon
AUGMENTING GPS SPEED LIMIT MONITORING WITH ROAD SIDE VISUAL INFORMATION M.L. Eichner*, T.P. Breckon * Cranfield University, UK, marcin.eichner@cranfield.ac.uk Cranfield University, UK, toby.breckon@cranfield.ac.uk
More informationAdvance Shadow Edge Detection and Removal (ASEDR)
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 2 (2017), pp. 253-259 Research India Publications http://www.ripublication.com Advance Shadow Edge Detection
More informationVehicle Detection and Tracking using Gaussian Mixture Model and Kalman Filter
Vehicle Detection and Tracking using Gaussian Mixture Model and Kalman Filter Indrabayu 1, Rizki Yusliana Bakti 2, Intan Sari Areni 3, A. Ais Prayogi 4 1,2,4 Informatics Study Program 3 Electrical Engineering
More informationLearning the Three Factors of a Non-overlapping Multi-camera Network Topology
Learning the Three Factors of a Non-overlapping Multi-camera Network Topology Xiaotang Chen, Kaiqi Huang, and Tieniu Tan National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy
More informationAutomatic Shadow Removal by Illuminance in HSV Color Space
Computer Science and Information Technology 3(3): 70-75, 2015 DOI: 10.13189/csit.2015.030303 http://www.hrpub.org Automatic Shadow Removal by Illuminance in HSV Color Space Wenbo Huang 1, KyoungYeon Kim
More informationPerformance analysis of robust road sign identification
IOP Conference Series: Materials Science and Engineering OPEN ACCESS Performance analysis of robust road sign identification To cite this article: Nursabillilah M Ali et al 2013 IOP Conf. Ser.: Mater.
More informationUrban Scene Segmentation, Recognition and Remodeling. Part III. Jinglu Wang 11/24/2016 ACCV 2016 TUTORIAL
Part III Jinglu Wang Urban Scene Segmentation, Recognition and Remodeling 102 Outline Introduction Related work Approaches Conclusion and future work o o - - ) 11/7/16 103 Introduction Motivation Motivation
More informationINTELLIGENT MACHINE VISION SYSTEM FOR ROAD TRAFFIC SIGN RECOGNITION
INTELLIGENT MACHINE VISION SYSTEM FOR ROAD TRAFFIC SIGN RECOGNITION Aryuanto 1), Koichi Yamada 2), F. Yudi Limpraptono 3) Jurusan Teknik Elektro, Fakultas Teknologi Industri, Institut Teknologi Nasional
More informationImage retrieval based on region shape similarity
Image retrieval based on region shape similarity Cheng Chang Liu Wenyin Hongjiang Zhang Microsoft Research China, 49 Zhichun Road, Beijing 8, China {wyliu, hjzhang}@microsoft.com ABSTRACT This paper presents
More informationCSE/EE-576, Final Project
1 CSE/EE-576, Final Project Torso tracking Ke-Yu Chen Introduction Human 3D modeling and reconstruction from 2D sequences has been researcher s interests for years. Torso is the main part of the human
More informationA Network Intrusion Detection System Architecture Based on Snort and. Computational Intelligence
2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 206) A Network Intrusion Detection System Architecture Based on Snort and Computational Intelligence Tao Liu, a, Da
More informationTRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK
TRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK 1 Po-Jen Lai ( 賴柏任 ), 2 Chiou-Shann Fuh ( 傅楸善 ) 1 Dept. of Electrical Engineering, National Taiwan University, Taiwan 2 Dept.
More informationReduced Image Noise on Shape Recognition Using Singular Value Decomposition for Pick and Place Robotic Systems
Reduced Image Noise on Shape Recognition Using Singular Value Decomposition for Pick and Place Robotic Systems Angelo A. Beltran Jr. 1, Christian Deus T. Cayao 2, Jay-K V. Delicana 3, Benjamin B. Agraan
More informationAn Intelligent Clustering Algorithm for High Dimensional and Highly Overlapped Photo-Thermal Infrared Imaging Data
An Intelligent Clustering Algorithm for High Dimensional and Highly Overlapped Photo-Thermal Infrared Imaging Data Nian Zhang and Lara Thompson Department of Electrical and Computer Engineering, University
More informationMonocular Vision Based Autonomous Navigation for Arbitrarily Shaped Urban Roads
Proceedings of the International Conference on Machine Vision and Machine Learning Prague, Czech Republic, August 14-15, 2014 Paper No. 127 Monocular Vision Based Autonomous Navigation for Arbitrarily
More informationUnwrapping of Urban Surface Models
Unwrapping of Urban Surface Models Generation of virtual city models using laser altimetry and 2D GIS Abstract In this paper we present an approach for the geometric reconstruction of urban areas. It is
More informationResearch on QR Code Image Pre-processing Algorithm under Complex Background
Scientific Journal of Information Engineering May 207, Volume 7, Issue, PP.-7 Research on QR Code Image Pre-processing Algorithm under Complex Background Lei Liu, Lin-li Zhou, Huifang Bao. Institute of
More informationPreliminary inspection about the profit of 3D data in public works
icccbe 2010 Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor) Preliminary inspection about the profit of 3D data in
More informationFundamental Matrices from Moving Objects Using Line Motion Barcodes
Fundamental Matrices from Moving Objects Using Line Motion Barcodes Yoni Kasten (B), Gil Ben-Artzi, Shmuel Peleg, and Michael Werman School of Computer Science and Engineering, The Hebrew University of
More informationSYSTEM APPROACH TO A RASTER-TO-VECTOR CONVERSION: From Research to Commercial System. Dr. Eugene Bodansky ESRI. Extended abstract
SYSTEM APPROACH TO A RASTER-TO-VECTOR CONVERSION: From Research to Commercial System Dr. Eugene Bodansky ESRI Extended abstract Contact between scientists and the developers who create new commercial systems
More informationImprovement of SURF Feature Image Registration Algorithm Based on Cluster Analysis
Sensors & Transducers 2014 by IFSA Publishing, S. L. http://www.sensorsportal.com Improvement of SURF Feature Image Registration Algorithm Based on Cluster Analysis 1 Xulin LONG, 1,* Qiang CHEN, 2 Xiaoya
More informationAn Efficient Character Segmentation Algorithm for Printed Chinese Documents
An Efficient Character Segmentation Algorithm for Printed Chinese Documents Yuan Mei 1,2, Xinhui Wang 1,2, Jin Wang 1,2 1 Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information
More informationExtracting Characters From Books Based On The OCR Technology
2016 International Conference on Engineering and Advanced Technology (ICEAT-16) Extracting Characters From Books Based On The OCR Technology Mingkai Zhang1, a, Xiaoyi Bao1, b,xin Wang1, c, Jifeng Ding1,
More informationTraffic Signs Recognition Experiments with Transform based Traffic Sign Recognition System
Sept. 8-10, 010, Kosice, Slovakia Traffic Signs Recognition Experiments with Transform based Traffic Sign Recognition System Martin FIFIK 1, Ján TURÁN 1, Ľuboš OVSENÍK 1 1 Department of Electronics and
More informationAutomated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Cadastre - Preliminary Results
Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Pankaj Kumar 1*, Alias Abdul Rahman 1 and Gurcan Buyuksalih 2 ¹Department of Geoinformation Universiti
More informationAutomatic Detection of Change in Address Blocks for Reply Forms Processing
Automatic Detection of Change in Address Blocks for Reply Forms Processing K R Karthick, S Marshall and A J Gray Abstract In this paper, an automatic method to detect the presence of on-line erasures/scribbles/corrections/over-writing
More informationIwane Laboratories, Ltd.
2011 Iwane Laboratories, Ltd. Introduction of a highly accurate threedimensional map making system termed as Map on 3D based on the IMS3 Dual Cam Map on 3D can detect three-dimensional shape of the road
More informationCONTRIBUTION TO THE INVESTIGATION OF STOPPING SIGHT DISTANCE IN THREE-DIMENSIONAL SPACE
National Technical University of Athens School of Civil Engineering Department of Transportation Planning and Engineering Doctoral Dissertation CONTRIBUTION TO THE INVESTIGATION OF STOPPING SIGHT DISTANCE
More informationSegmentation and Tracking of Partial Planar Templates
Segmentation and Tracking of Partial Planar Templates Abdelsalam Masoud William Hoff Colorado School of Mines Colorado School of Mines Golden, CO 800 Golden, CO 800 amasoud@mines.edu whoff@mines.edu Abstract
More informationSurfNet: Generating 3D shape surfaces using deep residual networks-supplementary Material
SurfNet: Generating 3D shape surfaces using deep residual networks-supplementary Material Ayan Sinha MIT Asim Unmesh IIT Kanpur Qixing Huang UT Austin Karthik Ramani Purdue sinhayan@mit.edu a.unmesh@gmail.com
More informationSOME stereo image-matching methods require a user-selected
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 2, APRIL 2006 207 Seed Point Selection Method for Triangle Constrained Image Matching Propagation Qing Zhu, Bo Wu, and Zhi-Xiang Xu Abstract In order
More informationA Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images
A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images Karthik Ram K.V & Mahantesh K Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore,
More informationUnconstrained License Plate Detection Using the Hausdorff Distance
SPIE Defense & Security, Visual Information Processing XIX, Proc. SPIE, Vol. 7701, 77010V (2010) Unconstrained License Plate Detection Using the Hausdorff Distance M. Lalonde, S. Foucher, L. Gagnon R&D
More informationVehicle Detection Method using Haar-like Feature on Real Time System
Vehicle Detection Method using Haar-like Feature on Real Time System Sungji Han, Youngjoon Han and Hernsoo Hahn Abstract This paper presents a robust vehicle detection approach using Haar-like feature.
More informationIntroduction: Design Standards
Introduction: Design Standards The Corps sign system has been designed using a selected group of common graphic elements and visual standards. These graphic elements include: the Corps Signature for agency
More informationAdaptive Learning of an Accurate Skin-Color Model
Adaptive Learning of an Accurate Skin-Color Model Q. Zhu K.T. Cheng C. T. Wu Y. L. Wu Electrical & Computer Engineering University of California, Santa Barbara Presented by: H.T Wang Outline Generic Skin
More informationSensor Fusion-Based Parking Assist System
Sensor Fusion-Based Parking Assist System 2014-01-0327 Jaeseob Choi, Eugene Chang, Daejoong Yoon, and Seongsook Ryu Hyundai & Kia Corp. Hogi Jung and Jaekyu Suhr Hanyang Univ. Published 04/01/2014 CITATION:
More information[10] Industrial DataMatrix barcodes recognition with a random tilt and rotating the camera
[10] Industrial DataMatrix barcodes recognition with a random tilt and rotating the camera Image processing, pattern recognition 865 Kruchinin A.Yu. Orenburg State University IntBuSoft Ltd Abstract The
More informationA parabolic curve that is applied to make a smooth and safe transition between two grades on a roadway or a highway.
A parabolic curve that is applied to make a smooth and safe transition between two grades on a roadway or a highway. VPC: Vertical Point of Curvature VPI: Vertical Point of Intersection VPT: Vertical Point
More informationReal Time Motion Detection Using Background Subtraction Method and Frame Difference
Real Time Motion Detection Using Background Subtraction Method and Frame Difference Lavanya M P PG Scholar, Department of ECE, Channabasaveshwara Institute of Technology, Gubbi, Tumkur Abstract: In today
More informationLicense Plate Recognition (LPR) Camera Installation Manual
License Plate Recognition (LPR) Camera Installation Manual 0 Hikvision LPR Camera Installation Manual About this Manual This Manual is applicable to Hikvision LPR Network Camera. This manual may contain
More informationMobile Camera Based Text Detection and Translation
Mobile Camera Based Text Detection and Translation Derek Ma Qiuhau Lin Tong Zhang Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Mechanical Engineering Email: derekxm@stanford.edu
More informationAttachment No. 10 RW Signs No. 5
1 2 3 4 5 6 7 8 9 Agenda Item III. 5, June 2014 TECHNICAL COMMITTEE; Regulatory & Warning Signs DATE OF ACTION; January 9, 2014 TASK FORCE; Randy McCourt & Jim Pline TECH COM APPROVAL DATE: 1/09/14 TECH
More informationOn-road obstacle detection system for driver assistance
Asia Pacific Journal of Engineering Science and Technology 3 (1) (2017) 16-21 Asia Pacific Journal of Engineering Science and Technology journal homepage: www.apjest.com Full length article On-road obstacle
More informationA New Algorithm for Shape Detection
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 19, Issue 3, Ver. I (May.-June. 2017), PP 71-76 www.iosrjournals.org A New Algorithm for Shape Detection Hewa
More informationStereo Vision Based Traversable Region Detection for Mobile Robots Using U-V-Disparity
Stereo Vision Based Traversable Region Detection for Mobile Robots Using U-V-Disparity ZHU Xiaozhou, LU Huimin, Member, IEEE, YANG Xingrui, LI Yubo, ZHANG Hui College of Mechatronics and Automation, National
More informationTraffic sign detection and recognition with convolutional neural networks
38. Wissenschaftlich-Technische Jahrestagung der DGPF und PFGK18 Tagung in München Publikationen der DGPF, Band 27, 2018 Traffic sign detection and recognition with convolutional neural networks ALEXANDER
More informationKeywords: Thresholding, Morphological operations, Image filtering, Adaptive histogram equalization, Ceramic tile.
Volume 3, Issue 7, July 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Blobs and Cracks
More information[Youn *, 5(11): November 2018] ISSN DOI /zenodo Impact Factor
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES AUTOMATIC EXTRACTING DEM FROM DSM WITH CONSECUTIVE MORPHOLOGICAL FILTERING Junhee Youn *1 & Tae-Hoon Kim 2 *1,2 Korea Institute of Civil Engineering
More informationChapter 4 - Image. Digital Libraries and Content Management
Prof. Dr.-Ing. Stefan Deßloch AG Heterogene Informationssysteme Geb. 36, Raum 329 Tel. 0631/205 3275 dessloch@informatik.uni-kl.de Chapter 4 - Image Vector Graphics Raw data: set (!) of lines and polygons
More informationThe Vehicle Logo Location System based on saliency model
ISSN 746-7659, England, UK Journal of Information and Computing Science Vol. 0, No. 3, 205, pp. 73-77 The Vehicle Logo Location System based on saliency model Shangbing Gao,2, Liangliang Wang, Hongyang
More informationHome. Brand Standards & Guidelines Version 1.0
Home Introduction Brandmark Graphics Photography Brand Standards & Guidelines Version 1.0 Advertising Home Introduction Brandmark Graphics Photography Advertising Brand Introduction Slime Brand Introduction
More informationLesson Polygons
Lesson 4.1 - Polygons Obj.: classify polygons by their sides. classify quadrilaterals by their attributes. find the sum of the angle measures in a polygon. Decagon - A polygon with ten sides. Dodecagon
More informationFACET SHIFT ALGORITHM BASED ON MINIMAL DISTANCE IN SIMPLIFICATION OF BUILDINGS WITH PARALLEL STRUCTURE
FACET SHIFT ALGORITHM BASED ON MINIMAL DISTANCE IN SIMPLIFICATION OF BUILDINGS WITH PARALLEL STRUCTURE GE Lei, WU Fang, QIAN Haizhong, ZHAI Renjian Institute of Surveying and Mapping Information Engineering
More informationKeywords Binary Linked Object, Binary silhouette, Fingertip Detection, Hand Gesture Recognition, k-nn algorithm.
Volume 7, Issue 5, May 2017 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Hand Gestures Recognition
More informationA Method for Identifying Irregular Lattices of Hexagonal Tiles in Real-time
S. E. Ashley, R. Green, A Method for Identifying Irregular Lattices of Hexagonal Tiles in Real-Time, Proceedings of Image and Vision Computing New Zealand 2007, pp. 271 275, Hamilton, New Zealand, December
More information3RD GRADE COMMON CORE VOCABULARY M-Z
o o o 3RD GRADE COMMON CORE VOCABULARY M-Z mass mass mass The amount of matter in an object. Usually measured by comparing with an object of known mass. While gravity influences weight, it does not affect
More informationA NEW STRATEGY FOR DSM GENERATION FROM HIGH RESOLUTION STEREO SATELLITE IMAGES BASED ON CONTROL NETWORK INTEREST POINT MATCHING
A NEW STRATEGY FOR DSM GENERATION FROM HIGH RESOLUTION STEREO SATELLITE IMAGES BASED ON CONTROL NETWORK INTEREST POINT MATCHING Z. Xiong a, Y. Zhang a a Department of Geodesy & Geomatics Engineering, University
More informationA Fast Method of Vehicle Logo Location Honglin Li
6th International Conference on Sensor Network and Computer Engineering (ICSNCE 2016) A Fast Method of Vehicle Logo Location Honglin Li School of Information Engineering, Qujing Normal University, Qujing
More informationBIM-based Indoor Positioning Technology Using a Monocular Camera
BIM-based Indoor Positioning Technology Using a Monocular Camera Yichuan Deng a, Hao Hong b, Hui Deng c, Han Luo d a,b,c,d School of Civil Engineering and Transportation, South China University of Technology,
More informationPrediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai He 1,c
2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 215) Prediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai
More informationA Street Scene Surveillance System for Moving Object Detection, Tracking and Classification
A Street Scene Surveillance System for Moving Object Detection, Tracking and Classification Huei-Yung Lin * and Juang-Yu Wei Department of Electrical Engineering National Chung Cheng University Chia-Yi
More informationAutomatic License Plate Recognition in Real Time Videos using Visual Surveillance Techniques
Automatic License Plate Recognition in Real Time Videos using Visual Surveillance Techniques Lucky Kodwani, Sukadev Meher Department of Electronics & Communication National Institute of Technology Rourkela,
More informationUsing Mobile LiDAR To Efficiently Collect Roadway Asset and Condition Data. Pierre-Paul Grondin, B.Sc. Surveying
Using Mobile LiDAR To Efficiently Collect Roadway Asset and Condition Data Pierre-Paul Grondin, B.Sc. Surveying LIDAR (Light Detection and Ranging) The prevalent method to determine distance to an object
More informationRapid Modeling of Digital City Based on Sketchup
Journal of Mechanical Engineering Research and Developments ISSN: 1024-1752 Website: http://www.jmerd.org Vol. 38, No. 1, 2015, pp. 130-134 J. Y. Li *, H. L. Yuan, & C. Reithmeier Department of Architectural
More informationLast week. Multi-Frame Structure from Motion: Multi-View Stereo. Unknown camera viewpoints
Last week Multi-Frame Structure from Motion: Multi-View Stereo Unknown camera viewpoints Last week PCA Today Recognition Today Recognition Recognition problems What is it? Object detection Who is it? Recognizing
More informationChange detection using joint intensity histogram
Change detection using joint intensity histogram Yasuyo Kita National Institute of Advanced Industrial Science and Technology (AIST) Information Technology Research Institute AIST Tsukuba Central 2, 1-1-1
More information