ABSTRACT
In this paper, we present two recently proposed efficient methods for human segmentation from video in indoor environments: the illumination sensitive background method and the self-organizing background subtraction (SOBS) method. Both methods maintain multiple background models. The SOBS method has been modified in this work for gray-scale frames, in order to decrease processing times. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The paper presents the algorithmic implementation and modifications details, while results are also presented for a small number of video sequences.
Subject(s)
Algorithms , Image Interpretation, Computer-Assisted/methods , Movement/physiology , Pattern Recognition, Automated/methods , Photography/methods , Self-Help Devices , Video Recording/methods , Humans , Monitoring, Ambulatory/methods , Reproducibility of Results , Sensitivity and Specificity , Subtraction TechniqueABSTRACT
In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the Self Organized Maps (SOMs) and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.
Subject(s)
Angiography/instrumentation , Retina/pathology , Retinal Vessels/pathology , Algorithms , Angiography/methods , Automation , Humans , Image Processing, Computer-Assisted , Imaging, Three-Dimensional/methods , Microscopy/methods , Models, Statistical , Pattern Recognition, Automated , Reproducibility of Results , Video RecordingABSTRACT
Retinal image registration is commonly required in order to combine the complementary information in different retinal modalities. In this paper, a new automatic scheme to register retinal images is presented and is currently tested in a clinical environment. The scheme considers the suitability and efficiency of different image transformation models and function optimization techniques, following an initial preprocessing stage. Three different transformation models--affine, bilinear and projective--as well as three optimization techniques--downhill simplex method, simulated annealing and genetic algorithms--are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of Fluoroscein Angiography and Indocyanine Green Chorioangiography images with the corresponding Red-Free retinal images, showed the superiority of combining genetic algorithms with the affine and bilinear transformation models. A comparative study of the proposed automatic registration scheme against the manual method, commonly used in the clinical practice, is finally presented showing the advantage of the proposed automatic scheme in terms of accuracy and consistency.