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Vehicle classification is currently a widely implemented component in intelligent vehicles, surveillance systems, and traffic monitoring. The major component of vehicle classification is to learn what feature in the images of vehicle provides the most valuable information, which distinguishes different models. In this work, the study of two different famous feature extraction mechanisms and three classifiers is carefully conducted to provide comparison and analysis. The next important component of this work is the investigation of the effect of viewing angle and lighting conditions on the performance of the classifier. The latter is inspired by previous studies on face-recognition systems with different lighting conditions and poses [1].
Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.
Abstract: The current project presents a method based on image processing techniques for the identification of moving vehicles as they approach a signaled intersection. A set of fixed cameras located before the intersection monitor the street continuously, taking pictures of the approaching object. A feature extraction algorithm is presented, which identifies a set of features in the image, and calculates a distance metric, measuring the difference of the current image from images stored in a database of vehicles. If the calculated distance metric is very small, then the present vehicle is successfully classified as being the same type as one of the vehicles stored in the database. A successful application of this method was implemented using real-time data. In the particular application presented in this project the vehicle to be identified is an ambulance car, whose images have been previously stored in the vehicle database.
th This two-volume set constitutes the Proceedings of the 16 International Conference on Neural Information Processing (ICONIP 2009), held in Bangkok, Thailand, during December 1–5, 2009. ICONIP is a world-renowned international conference that is held annually in the Asia-Pacific region. This prestigious event is sponsored by the Asia Pacific Neural Network Assembly (APNNA), and it has provided an annual forum for international researchers to exchange the latest ideas and advances in neural networks and related discipline. The School of Information Technology (SIT) at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand was the proud host of ICONIP 2009. The conference theme was “Challenges and Trends of Neural Information Processing,” with an aim to discuss the past, present, and future challenges and trends in the field of neural information processing. ICONIP 2009 accepted 145 regular session papers and 53 special session papers from a total of 466 submissions received on the Springer Online Conference Service (OCS) system. The authors of accepted papers alone covered 36 countries and - gions worldwide and there are over 500 authors in these proceedings. The technical sessions were divided into 23 topical categories, including 9 special sessions.
​This book includes extended and revised selected papers from the 10th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2021, and 7th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2021, held as virtual event, in April 28–30, 2021. The conference was held virtually due to the COVID-19 crisis. The 22 full papers included in this book were carefully reviewed and selected from 140 submissions. The papers present research on advances and applications in the fields of smart cities, electric vehicles, sustainable computing and communications, energy aware systems and technologies, intelligent vehicle technologies, intelligent transport systems and infrastructure, connected vehicles.
The threat of terrorist attacks against US civilian populations is a very real, near-term problem that must be addressed, especially in response to possible use of Weapons of Mass Destruction. Several programs are now being funded by the US Government to put into place means by which the effects of a terrorist attack could be averted or limited through the use of sensors and monitoring technology. Specialized systems that detect certain threat materials, while effective within certain performance limits, cannot generally be used efficiently to track a mobile threat such as a vehicle over a large urban area. The key elements of an effective system are an image feature-based vehicle identification technique and a networked sensor system. We have briefly examined current uses of image and feature recognition techniques to the urban tracking problem and set forth the outlines of a proposal for application of LLNL technologies to this critical problem. The primary contributions of the proposed work lie in filling important needs not addressed by the current program: (1) The ability to create vehicle ''fingerprints, '' or feature information from images to allow automatic identification of vehicles. Currently, the analysis task is done entirely by humans. The goal is to aid the analyst by reducing the amount of data he/she must analyze and reduce errors caused by inattention or lack of training. This capability has broad application to problems associated with extraction of useful features from large data sets. (2) Improvements in the effectiveness of LLNL's WATS (Wide Area Tracking System) by providing it accurate threat vehicle location and velocity. Model predictability is likely to be enhanced by use of more information related to different data sets. We believe that the LLNL can accomplish the proposed tasks and enhance the effectiveness of the system now under development.
The three-volume set LNCS 101164, 11165, and 11166 constitutes the refereed proceedings of the 19th Pacific-Rim Conference on Multimedia, PCM 2018, held in Hefei, China, in September 2018. The 209 regular papers presented together with 20 special session papers were carefully reviewed and selected from 452 submissions. The papers cover topics such as: multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.