This project explores notions of seeing and knowing, underpinned by performative and phenomenological fields of enquiry that relate this exploration to the sensate experience of the viewer. A specific interest considers ideas of embodied vision with an aim at generating events that vacillate in the bodies of the audience. A primary focus is on the arena of encounter as a multi-sensory experiential event, and within this context this project proposes a temporal and spatial framework for exploration. Studio methods develop a cinematic-body of video work negotiating performative practice involving video projection and temporality. Pivotal goals are to explore the significance of the ‘chiasm’ between seeing and knowing, raising questions about how humans see, and how humans make how they see matter. Therefore, this thesis project progresses along experimental approaches to video installation, particularly in relation to the phenomena of encounter, the viewer, and film experience. The central motivation of this video practice is aimed at corporeal affect in the body/s of the audience. This thesis project is constituted as 80% practice-based work accompanied by a 20% exegesis.

This master´s thesis deals with blood vessel segmentation in retinal image data. The theoretical part is focused on the basic description of anatomy and physiology of the eye and methods of observing the back of the eye. This thesis also describes the principles of classical and convolutional neural networks and segmentation techniques that are used to segment blood vessel in retinal images. In the practical part, a segmentation method using convolutional neural network U-net is implemented. This neural network is trained on the three datasets. Two datasets include images from experimental video ophthalmoscope. Because it impossible to compare the results of these two datasets with any other methods of retinal blood vessel segmentation, U-net is trained on other dataset that is HRF database. This dataset includes fundus images. The results of testing on this dataset serves for comparing results with other methods of retinal blood vessel segmentation.