3 research outputs found
Feature-based object modelling for visual surveillance
This paper introduces a new feature-based technique for im-plicitly modelling objects in visual surveillance. Previous work has generally employed background subtraction and other image or motion based object segmentation schemes for the ſrst step in identifying objects worthy of attention. Given that background subtraction is a notoriously noisy pro-cess, this paper investigates an alternative strategy by instead employing feature (SIFT [1]) clustering to characterise ob-jects. The segmentation step is therefore performed on the sparse feature space instead of the image data itself. The paper also presents an application employing this idea for automatic detection of illegal dumping from CCTV footage. The Viterbi algorithm then allows robust tracking [2] of ob-jects generated from the spatial clustering of these sparse foreground feature maps. Index Terms — visual surveillance, SIFT, background modelling, foreground estimatio
2D and 3D segmentation of medical images.
"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.