1,721,066 research outputs found
Route following based on adaptive visual landmark matching
No full textRoute following based on visual landmark matching may require many models to cover all different situations. This paper describes a system that is able to adapt template's modelling parameters to environmental conditions (lighting, shadows, etc.) by a genetic learning technique. In addition, the mobile robot self-localisation is obtained by a stereo approach that uses the centres of matching in the two images to solve in a simple way the correspondence problem in the 3D position estimation. The experimental results show that the tracking robustness is improved, while using a small set of templates. © 1998 Elsevier Science B.V. All rights reserved
Learning to acquire and select useful landmarks for route following
No full textThe paper describes a prototypical system for optimal landmark acquisition and selection. Our landmark-learning approach does not require any type of environment model to be supplied to the robot in advance, and represents a step towards robots interacting with real environments. The approach is fitted and tested for the TMGA system (P. Zingaretti et al., 1998), which the authors developed for landmark tracking by adaptive, stereo template matching. Two complementary strategies, properly managed, are followed to construct a suitable subset of landmarks: the selection of the more discriminant landmarks and the selection of the landmarks that are more invariant in a neighbourhood. The robustness of the TMGA system in analysing the discriminant power of each landmark and the analysis of the disparity map and of the spatial activity maps of the stereo images are used for identifying discriminant and invariant landmarks. The experimental results show that the number of matching failures, and consequent landmark changes during the following of a route is comparable with (not much greater than) those obtained using an a-priori subset
A comprehensive approach to image-contrast enhancement
No full textThe paper describes a novel comprehensive approach to image-contrast enhancement in the spatial domain. Instead of defining another transformation function our strategy consists of adopting a general functional form, able to map different transformation functions, and in using a learning technique to select the parameter values that are optimal for the image being processed. First, local measures of spatial activity are assigned to each pixel of the image. Second, the local contrast value for each pixel is computed according to a function which is based on human visual response. Third, the parameters of a comprehensive contrast-enhancement function are selected by a genetic algorithm on the basis of the spatial activity of the image resulting from the transformation. The validity of the proposed technique is confirmed both perceptually, that is, higher fitness values correspond to the images that have been judged better by human observers, and by comparative evaluations of our algorithm with respect to classical methods. © 1999 IEEE
Can AI Replace Conventional Markerless Tracking? A Comparative Performance Study for Mobile Augmented Reality Based on Artificial Intelligence
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Optimal stock control and procurement by reusing of obsolescences in manufacturing
Full text linkProduction, procurement planning and logistic organization are complex tasks in companies with multiple production/stocking sites. For companies operating in fashion markets, these tasks are harder due to the high demand variability and frequent turnover of trends that make products outmoded rapidly. In the field of industry informatics under an Industry 4.0 scenario, this paper presents a data and processing framework that mix a Decision Support System (DSS) and a Mixed Integer Linear Programming (MILP) model to maximize the potential revenue of the outmoded products. The solution proposed considers both the investment for missing components and the transportation costs between hubs and takes into account constraints on the overall number of items produced, the budget for new component orders and the minimum lot scheduling thresholds. Moreover, to avoid the increment of obsolescences due to raw material orders, the MILP model bounds purchased components to a percentage of used ones. A real worldwide dataset provided by a fashion company, market leader in the design, manufacture and distribution of fashion, luxury, sports and performance eyewear, was used to test performances and repeatability of the mathematical model under different configurations. The results obtained show a huge positive impact on the financial results
HMM-based activity recognition with a ceiling RGB-D camera
No full textAutomated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available
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