Självet, rollerna och pågående koordinationer av mänskliga handlingar : Om att försöka se ’samhället’ som något annat än fängelse eller marionetteater

The self, roles and the ongoing coordination of human action. Trying to see ‘society’ as neither prison nor puppet theatre In the article it is argued that structural North-American role-sociology may be integrated with theories emphasizing ‘society’ as ongoing processes (f. ex. Giddens’ theory of structuration). This is possible if the concept of role is defined as a recurrence oriented to the action of others standing out as a regularity in a societal process. But this definition makes it necessary to in a fundamental way understand what kind of social being the role-actor is. This is done with the help of Hans Joas’ theory of creativity and Merleau-Pontys concept of ‘flesh’ arguing that Meads concept of the ‘I’ maybe understood as an embodied self-asserting I, which at least in reflexive modernity has the creative power to split Meads ‘me’ into a self-voiced subject-me and an other voiced object-me. The embodied I communicating with the subject-me may be viewed as that role-actor which is something else than the role played. But this kind of role-actor is making for new troubles because it is hard to understand how this kind of self is creating self-coherence by using Meads concept of ‘the generalized other’. This trouble is handled by using Alain Touraines concept of the ‘subject’ and arguing that the generalized other is dissolving in de-modernized modernity. In split modernity self-coherence may instead be created by what in the article is called the generalized subject. This concept means a kind of communicative future based evaluation, which has its base in the ‘subject’ opposing the split powers of both the instrumentality of markets and of life-worlds trying to create ‘fundamentalistic’ self-identities. This kind of self is communicative because it also must respect the other as ‘subject’. It exists only in the battle against the forces of the market or a community. It never constructs an ideal city or a higher type of individual. It creates and protects a clearing that is constantly being invaded, to use the words of the old Frenchman himself. Asa kind of test-case it is by the way in the article shown how Becks concept of individualization may be understood in a deeply social and role-sociological way.

Fingerprint Segmentation

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.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

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.