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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Thank you very much for downloading applied longitudinal data analysis modeling change and event occurrence. As you may know, people have look hundreds times for their favorite novels like this applied longitudinal data analysis modeling change and event occurrence, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.

Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Annotation can be a valuable exercise when trying to understand new information. The technique can be used to create a 'condensed' version of the original information for later review and to add additional information into the existing document. The growth in web-based learning materials and information sources has created requirement for systems that allow annotations to be attached to these new sources and, potentially, shared with other learners. This paper discusses annotation in an educational context and introduces some of the web annotation systems currently available. It also provides an overview of the development of a new system, eLAWS, by the authors, based upon the Web Service architecture. Finally, the paper provides suggestions for the future development of e-Learning Annotation tools.

Online annotation - Research and practices

Spatio-temporal texture modelling for real-time crowd anomaly detection

Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television (CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gabor-filtered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns (signatures) is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.

/pdf/effective-crowd-anomaly-detection-through-spatio-temporal-myvsl3d0bg.pdf

Effective crowd anomaly detection through spatio-temporal texture analysis

The quality loss function proposed by Taguchi provides a quantitative measurement of product quality when product’s quality characteristic value deviates from the ideal target at an arbitrary time. However, product use causes degradation on its quality characteristic, and since such a deviation can be changing over time, so can its quality loss. The quality loss caused by degradation on quality characteristic has not been considered in most research. In this paper, the time value of money for quality loss and product degradation over time is integrated into the total cost model, and a new optimization model for the tolerance design of products with correlated characteristics is established. The discussions focus on the multivariate quality loss function as an extension of the Taguchi loss function, which is used to model quality loss due to product degradation as a continuous cash flow function under continuous compounding. The optimal tolerance design is achieved by minimizing the total cost, which is the sum of manufacturing cost and the present worth of expected quality loss. An illustrative example is presented to demonstrate the effectiveness of the proposed model.

Optimal tolerance design for products with correlated characteristics by considering the present worth of quality loss

Virtual environments (VE) are becoming an important tool for simulating manufacturing systems. A problem in their adoption is the lack of an easy and effective approach to building large and complex virtual environments that are required by many manufacturing simulation tasks. This paper reports on research into this problem and presents a novel approach - domain-analysis-based top-down construction - to constructing complex virtual manufacturing environments. Based on this approach, a software system is designed and implemented to provide easy-to-use and flexible tools that enable users to construct virtual environments rapidly and with minimum modelling effort.

Zhijie Xu

Papers

Online annotation - Research and practices

Spatio-temporal texture modelling for real-time crowd anomaly detection

Effective crowd anomaly detection through spatio-temporal texture analysis

Optimal tolerance design for products with correlated characteristics by considering the present worth of quality loss

Constructing virtual environments for manufacturing simulation