12 resultados para Semi-supervised segmentation

em Dalarna University College Electronic Archive


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In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

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This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

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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.

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Studiens syfte var att undersöka byråanställda art directors och originalares olika perspektiv på och förhållningssätt till sin yrkesroll med särskilt fokus på kreativitet och kundrelationer.Nio semi-strukturerade djupintervjuer genomfördes med sex art directors och fyra originalare. Respondenterna valdes ut genom ett kombinerat strategiskt bekvämlighetsurval från nio reklambyråer.Professionalism inom yrkesrollen visade sig vara något som respondenterna inte reflekterat över men det framkom att det främst handlar om hur art directors och originalare förhåller sig till kunden. Dessutom framkom det att respondenterna såg variation i arbetet som en positiv aspekt av yrket medan de såg prestationsångest och utomståendes oförståelse av yrket som negativa.Det framkom även att art directors och originalare i första hand ser på kreativitet som problemlösning, dock såg de olika på problemlösning i sina respektive roller. De använde sig av samma metoder för att finna inspirationsamt hanterade ångest på liknande sätt.Studien visade även att kunden och kundrelationen spelade en stor roll för yrket och ämnet berördes återkommande i respondenternas svar under intervjuernas gång. De använde sig av samma metoder för att finna inspiration

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Propremiar or pro-president? On the distinction between parliamentarism, presitentialism and semi-presidentialism. From comparative research on the constitutional development in Central and Eastern Europe and also from the longstanding debate on whether parliamentarism or presidentialism best facilitates democracy, it is apparent that there has been and continues to be, a certain degree of confusion concerning the concepts of semi-presidentialism and presidentialism. Different scholars mean different things by the terms and therefore classify countries differently. In this article I argue that the conceptual dichotomy between pro-premiär (premier-presidentialism) and pro-president systems (presidentparliamentary systems) provide the best solution to several of the problems related to categorising constitutional types, most importantly perhaps to the presidential power dilemma. I, furthermore, employ these concepts on the post-communist constitutional systems and try to reveal patterns with regard to presidential power, geographical region and democratisation.

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Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

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Small-scale enterprises face difficulties in fulfilling the regulations for organising Systematic Work Environment Management. This study compared three groups of small-scale manufacturing enterprises with and without support for implementing the provision. Two implementation methods, supervised and network method, were used. The third group worked according to their own ideas. Twenty-three enterprises participated. The effects of the implementation were evaluated after one year by semi-structured dialogue with the manager and safety representative. Each enterprise was classified on compliance with ten demands concerning the provision. The work environment was estimated by the WEST-method. Impact of the implementation on daily work was also studied. At the follow-up, the enterprises in the supervised method reported slightly more improvements in the fulfilment of the demands in the provision than the enterprises in the network method and the enterprises working on their own did. The effect of the project reached the employees faster in the enterprises with the supervised method. In general, the work environment improved to some extent in all enterprises. Extensive support to small-scale enterprises in terms of advise and networking aimed to fulfil the regulations of Systematic Work Environment Management had limited effect especially considering the cost of applying these methods.

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This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

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This article sets out to analyse recent regime developments in Ukraine in relation to semi-presidentialism. The article asks: to what extent and in what ways theoretical arguments against semi-presidentialism (premier-presidential and president-parliamentary systems) are relevant for understanding the changing directions of the Ukrainian regime since the 1990s? The article also reviews the by now overwhelming evidence suggesting that President Yanukovych is turning Ukraine into a more authoritarian hybrid regime and raises the question to what extent the president-parliamentary system might serve this end. The article argues that both kinds of semi-presidentialism have, in different ways, exacerbated rather than mitigated institutional conflict and political stalemate. The return to the president-parliamentary system in 2010 – the constitutional arrangement with the most dismal record of democratisation – was a step in the wrong direction. The premier-presidential regime was by no means ideal, but it had at least two advantages. It weakened the presidential dominance and it explicitly anchored the survival of the government in parliament. The return to the 1996 constitution ties in well with the notion that President Viktor Yanukovych has embarked on an outright authoritarian path.