5 resultados para Colour and image sensitive detectors

em Dalarna University College Electronic Archive


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This thesis is an investigation on the corporate identity of the firm SSAB from a managerial viewpoint (1), the company communication through press releases (2), and the image of the company as portrayed in news press articles (3). The managerial view of the corporate identity is researched through interviews with a communication manager of SSAB (1), the corporate communication is researched through press releases from the company (2) and the image is researched in news press articles (3). The results have been deducted using content analysis. The three dimensions are compared in order to see if the topics are coherent. This work builds on earlier research in corporate identity and image research, stakeholder theory, corporate communication and media reputation theory. This is interesting to research as the image of the company framed by the media affects, among other things, the possibility for the company to attract new talent and employees. If there are different stories, or topics, told in the three dimensions then the future employees may not share the view of the company with the managers in it. The analysis show that there is a discrepancy between the topics on the three dimensions, both between the corporate identity and the communication through press releases, as well as between the communication through press releases and the image in news press articles.

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In this article, we discuss ellipsis as an interactive strategy by analysing the author’s textchat corpus and the VOICE corpus of English as a Lingua Franca. It is found that there were fewer repetitions in the textchat data, and this is explained as a consequence of the textchat mode. Textchat contributions are preserved as long as the chat is active or has been saved, and therefore users can scroll through and review the discussion, compared to the more fleeting nature of oral conversation. As a result, repetition is less necessary. The frequency of other functions identified could be attributed to the topic of discourse. Discussions involve much ellipsis used to develop discourse, although some were self-presentations with repetition used to confirm details. Back-channel support and comments were often low because speakers instead used forms like yeah as supportive utterances.

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Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

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This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.   

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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.