Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Developing Box-Pushing Behaviours Using Evolutionary Robotics

The context of this report and the IRIDIA laboratory are described in the preface. Evolutionary Robotics and the box-pushing task are presented in the introduction.The building of a test system supporting Evolutionary Robotics experiments is then detailed. This system is made of a robot simulator and a Genetic Algorithm. It is used to explore the possibility of evolving box-pushing behaviours. The bootstrapping problem is explained, and a novel approach for dealing with it is proposed, with results presented.Finally, ideas for extending this approach are presented in the conclusion.

Attraktivt arbete i offentlig sektor bland enhetschefer inom vård och omsorg

HR-funktionens uppgift i en organisation är att tillvarata den mänskliga resursen och se till att goda arbetsförhållanden uppnås. Detta för att kunna attrahera, rekrytera, behålla och utveckla kompetens (Kira 2003). På senare år har arbetsförhållanden och arbetsvillkor uppmärksammats på kommuner i Sverige för bland annat enhetschefer i äldreomsorgen. Arbetsförhållandena, arbetsvillkoren och framför allt den höga personalomsättningen bland dessa, har lett till en problematik som var en av valets viktigaste frågor 2014. Syftet med denna studie är att beskriva arbetsförhållanden för enhetschefer inom mindre kommuner i Sverige, inom den sociala sektorn och belysa hur dessa skulle kunna förbättras. Som studieobjekt har Orsa kommun använts. Resultatet av undersökningen visade att arbetssituationen för enhetschefer är övermäktig, då det är hög arbetsbelastning samt dålig struktur i arbetet. Enhetscheferna själva skulle gynnas av en assistent samt en arbetsbeskrivning för att minska arbetsbelastningen och få struktur i arbetet. Vår slutsats är att kommuner i Sverige borde arbeta med att skapa bättre arbetsförhållanden för enhetschefer samt arbeta för att underlätta arbetsbördan. Vårt förslag till Orsa kommun är att ta hjälp av vår handlingsplan och därmed anställa assistenter till enhetscheferna samt skapa arbetsbeskrivningar. Vidare forskning i ämnet skulle kunna belysa mentorskapets betydelse i den offentliga sektorn inom kommuner i Sverige samt organisationsstrukturens bemärkelse för arbetets attraktivitet.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.