All talks will be held in lecture hall H6 at the western end of the main hall.
The time for a talk is 20 minutes plus 5 min discussion.
Image Processing and Vision
Tuesday, 2007-09-04 - from 10:30 to 11:45
Vessel Extracting Gas - Using self organization in the extraction of vascular trees for medical image registration
|Presenting Author: Dietlind Zühlke|
|Authors: Dietlind Zühlke|
A network model is introduced that allows the extraction of the topological structure of a set of input vectors corresponding to image voxels from a 3D doppler or contrast enhanced ultrasound. This extraction is a precondition for many medical image registration algorithms. Results on artificial and real ultrasound image data sets are discussed.
Video Summarization with SOMs
|Presenting Author: Mats Sjöberg|
|Authors: Jorma Laaksonen, Markus Koskela, Mats Sjöberg, Ville Viitaniemi, Hannes Muurinen|
Video summarization is a process where a long video file is converted to a considerably shorter form. The video summary can then be used to facilitate efficient searching and browsing of video files in large video collections. The aim of successful automatic summarization is to preserve as much as possible from the essential content of each video. What is essential is of course subjective and also dependent on the use of the videos and the overall content of the collection. In this paper we present an overview of the SOM-based methodology we have used for video summarization, which analyzes the temporal trajectories of the best-matching units of frame-wise feature vectors. It has been developed as a part of PicSOM, our content-based multimedia information retrieval and analysis framework. The video material we have used in our experiments comes from NIST's annual TRECVID evaluation for content-based video retrieval systems.
Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation
|Presenting Author: Aluizio R. F. Araújo|
|Authors: Aluizio R. F. Araújo, Diogo C. Costa|
A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results.