Road and Traffic Signs Recognition using Vector Machines

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.

Characters Extraction for Traffic Sign Destination boards in video and still images

Traffic Control Signs or destination boards on roadways offer significant information for drivers. Regulation signs tell something like your speed, turns, etc; Warning signs warn drivers of conditions ahead to help them avoid accidents; Destination signs show distances and directions to various locations; Service signs display location of hospitals, gas and rest areas etc. Because the signs are so important and there is always a certain distance from them to drivers, to let the drivers get information clearly and easily even in bad weather or other situations. The idea is to develop software which can collect useful information from a special camera which is mounted in the front of a moving car to extract the important information and finally show it to the drivers. For example, when a frame contains on a destination drive sign board it will be text something like "Linkoping 50",so the software should extract every character of "Linkoping 50", compare them with the already known character data in the database. if there is extracted character match "k" in the database then output the destination name and show to the driver. In this project C++ will be used to write the code for this software.

Developing Optical Character Recoginition for Ethiopic Scripts

The Amharic language is the Official language of over 70 million people mainly in Ethiopia. An extensive literature survey and the government report reveal no single Amharic character recognition is found in the country. The Amharic script has 33 basic characters each with seven orders giving 310 distinct characters, including numbers and punctuation symbols. The characters are visually similar; there is a typeface, but no capitalization. Beside this there is no any standard font to use the language in the computer but they use different fonts developed by different stakeholders without keeping a standard on their own way and interest and this create a problem of incompatibility between different fonts and documents.This project is to investigate the reason why Amharic optical character recognition is not addressed by local and international researchers and developers and finally to develop Amharic optical character recognition uses the features and facilities of Microsoft windows Vista or 7 using Unicode standard.

Variabeltryck med inkjet i dagspress : Möjligheten att införa anpassade upplagor

På uppdrag av Tidningsutgivarna har en studie utförts angående olika möjligheter att införa tryck av variabeldata med tryckmetoden inkjet i svensk dagspress. Målet var att undersöka tekniska möjligheter och begränsningar samt att utreda om det fanns något intresse på marknaden som skulle kunna ge någon avkastning. Studien utfördes med hjälp av noggranna källstudier och ett fortlöpande samarbete med olika företag med intresse för dagspress. Rapporten beskriver också företag som arbetar med den här typen av innovationer idag och diverse tidigare projekt med variabeltryckta tidningar. Nya teknologier som eventuellt kan vara av intresse för framtida utveckling har också beskrivits och hur tidningens framtid kommer att se ut ur ett kortare och ett längre perspektiv. Studien visar att inkjetpressar inte klarar av den hastighet som de moderna tidningspressarna håller idag. Men i takt med minskade upplagor och utvecklad teknologi, samt ett stort marknadsmässigt intresse så tyder det på att det kommer att finnas möjlighet att tillverka anpassade digitaltryckta tidningsupplagor inom en överskådlig framtid. Inkjet kan fungera som ett bra komplement till offset. Inlinepressar ger möjligheten att införa variabeltryck i stora tidningsupplagor medan separata inkjetpressar passar bra för tryck av mindre lokala upplagor.

Investigation of the drying process of linseed oil using FTIR and ToF-SIMS

The drying process of linseed oil, oxidized at 80 oC, has been investigated with rheology measurements, Fourier transformation infrared spectroscopy (FTIR), and time of flight secondary ion mass spectrometry (ToF-SIMS). The drying process can be divided into three main steps: initiation, propagation and termination. ToF-SIMS spectra show that the oxidation is initiated at the linolenic (three double bonds) and linoleic fatty acids (two double bonds). ToF-SIMS spectra reveal peaks that can be assigned to ketones, alcohols and hydroperoxides. In this article it is shown that FTIR in combination with ToF-SIMS are well suited tools for investigations of various fatty acid components and reaction products of linseed oil.

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.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.