New Meanings to Old Rituals : The Emergence of Mourning Rituals in Shiʿite Islam

This paper will discuss the emergence of Shiʿite mourning rituals around the grave of Husayn b. ʿAli. After the killing of Husayn at Karbala’ in 61/680, a number of men in Kufa feel deep regret for their neglect to come to the help of the grand­son of the Prophet. They gather and discuss how they can best make penitence for this crime. Eventually, they decide to take to arms and go against the Umayyad army – to kill those that killed Husayn, or be killed them­selves in the attempt to find revenge for him. Thus, they are called the Penitents (Ar. Tawwābūn). On their way to the battlefield they stop at Husayn’s tomb at Karbala’, dedicat­ing themselves to remorseful prayer, crying and wailing over the fate of Husayn and their own sin. When the Penitents perform certain ritual acts, such as weeping and wailing over the death of Husayn, visiting his grave, asking for God’s mercy upon him on the Day of Judgment, demand blood revenge for him etc., they enter into already existing rituals in the pre-Islamic Arab and early Muslim context. That is, they enter into rituals that were traditionally performed at the death of a person. What is new is that the rituals that the Penitents perform have partially received a new content. As described, the rituals are performed out of loyalty towards Husayn and the family of the Prophet. The lack of loyalty in connection with the death of Husayn is conceived of as a sin that has to be atoned. Blood revenge thus be­comes not only a pure action of revenge to restore honor, but equally an expression for true religious conversion and penitence. Humphrey and Laidlaw argue that ritual actions in themselves are not bearers of meaning, but that they are filled with mean­ing by the performer. Accord­ing to them, ritual actions are apprehensible, i.e. they can be, and should be filled with meaning, and the people who perform them try to do so within the context where the ritual is performed. The story of the Penitents is a clear example of mourning rituals as actions that survive from earlier times, but that are now filled with new meaning when they are performed in a new and developing move­ment with a different ideology. In later Shiʿism, these rituals are elaborated and become a main tenet of this form of Islam.

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.