Adaptive moving shadow detection and removal by new semi-supervised learning technique

The efficient application of current methods of shadow detection in video is hindered by the difficulty in defining their parameters or models and/or their application domain dependence. This paper presents a new shadow detection and removal method that aims to overcome these inefficiencies. It proposes a semi-supervised learning rule using a new variant of co-training technique for shadow detection and removal in uncontrolled scenes. The new variant both reduces the run-time through a periodical execution of a co-training process according to a novel temporal framework, and generates a more generic prediction model for an accurate classification. The efficiency of the proposed method is shown experimentally on a testbed of videos that were recorded by a static camera and that included several constraints, e.g., dynamic changes in the natural scene and various visual shadow features. The conducted experimental study produced quantitative and qualitative results that highlighted the robustness of our shadow detection method and its accuracy in removing cast shadows. In addition, the practical usefulness of the proposed method was evaluated by integrating it in a Highway Control and Management System software called RoadGuard.     

On line background modeling for moving object segmentation in dynamic scenes

Fast and accurate moving object segmentation in dynamic scenes is the first step in many computer vision applications. In this paper, we propose a new background modeling method for moving object segmentation based on dynamic matrix and spatio-temporal analyses of scenes. Our method copes with some challenges related to this field. A new algorithm is proposed to detect and remove cast shadow. A comparative study by quantitative evaluations shows that the proposed approach can detect foreground robustly and accurately from videos recorded by a static camera and which include several constraints. A Highway Control and Management System called RoadGuard is proposed to show the robustness of our method. In fact, our system has the ability to control highway by detecting strange events that can happen like vehicles suddenly stopped in roads, parked vehicles in emergency zones or even illegal conduct such as going out from the road. Moreover, RoadGuard is capable of managing highways by saving information about the date and time of overloaded roads.     

Supervisory control based on minimal cuts and Petri net sub-controllers coordination

This paper addresses the synthesis of Petri net (PN) controller for the forbidden state transition problem with a new utilisation of the theory of regions. Moreover, as any method of control synthesis based on a reachability graph, the theory of regions suffers from the combinatorial explosion problem. The proposed work minimises the number of equations in the linear system of theory of regions and therefore one can reduce the computation time. In this paper, two different approaches are proposed to select minimal cuts in the reachability graph in order to synthesise a PN controller. Thanks to a switch from one cut to another, one can activate and deactivate the corresponding?PNcontroller. An application is implemented in a flexible manufacturing system to illustrate the present method. Finally, comparison with previous works with experimental results in obtaining a maximally permissive controller is presented.     

Pleurotusostreatus and Agaricus subrufescens: investigation of chemical composition and antioxidant properties of these mushrooms cultivated with different handmade and commercial supplements

In this study, the chemical composition, total phenolic content and antioxidant activity (DPPH, ORAC and FRAP assays) of A. subrufescens and P. ostreatus, cultivated with handmade and commercials supplements, were compared. Additionally, the compounds ergosterol, saccharopine, and hexitol were identified in A. subrufescens by HPLC-MS/MS. The antioxidant compound p-coumaric acid and dihexoses was found in both mushroom species. A. subrufescens presented higher total phenolic content (73.8 ± 0.6 mg GAE 100 g⁻¹) and antioxidant activity than P. ostreatus (16.6 ± 0.5 mg GAE 100 g⁻¹). The handmade supplement based on the waste of noble grains presented statistically similar phenolic content to the mushrooms cultivated with commercial ones Spawn Mate II SE (86.1 ± 1.4 and 92.9 ± 0.3 mg GAE 100 g⁻¹, respectively). Therefore, the results support the use of handmade supplements based on agro-wastes as a viable alternative to the use of high-cost commercial ones.     

A four year study to determine the optimal harvesting period for Tunisian Chemlali olives

Due to the importance of the stage of maturity on several olive pomological parameters and oil quality indices, and to the interest that Tunisian olive oil production has recently received, the optimal harvesting period for the main Tunisian olive cultivar, Chemlali, was assessed. For the first time, a four‐season crops study, carried out in three representative geographical areas, was focused on both olive fruit pomological parameters and oil chemical composition at different stages of maturity. The stage of maturity was the factor showing the highest influence on the major part of these parameters, followed by the crop year. To guarantee a reasonable fat content and a good chemical quality of oil, in particular a harmonious acidic composition, acceptable UV absorbance, and a content high of antioxidants, unsaponifiable matter and sterols, the optimal harvesting period for Chemlali olives would appears to be between the end of November to the middle of December, which corresponds to a maturity index between 2.5 and 3.5.     

Improving the performance of retail stores subject to execution errors: coordination versus RFID technology

This paper analyzes a Newsvendor type inventory model in which a manufacturer sells a single product to a retailer store whose inventory is subject to errors stemming from execution problems. Hence, within the store, all of the products are not available on shelf for sales either because the replenishment of the shelf from the backroom is subject to execution errors that mainly result in products lost in the backroom or products misplaced on the other shelves of the store. We compare two situations: in the first situation, the two supply chain actors are aware of errors and optimize their ordering decisions by taking into account this issue. The second situation deals with the case where an advanced automatic identification system such as the Radio Frequency Identification technology is deployed in order to eliminate errors. Each situation is developed for three scenarios: in the centralized scenario, we consider a single decision-maker who is concerned with maximizing the entire supply chain’s profit; in the decentralized uncoordinated scenario, the retailer and the manufacturer act as different parties and do not cooperate. The third scenario is the decentralized coordinated scenario where we give conditions for coordinating the channel by designing a buyback contract. The authors would like to thank the editor, Prof. Dr. Hans-Otto Guenther, and two anonymous referees for their constructive comments to improve this article.     

Assessing sensor reliability for multisensor data fusion within the transferable belief model.

This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.