This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and
severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded
as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities
in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF)
and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and
True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge
(ROC) competition website and we conclude our method to be effective and efficient.
This paper presents an image understanding approach to monitor human movement and identify the abnormal circumstance
by robust motion detection for the care of the elderly in a home-based environment. In contrast to the conventional
approaches which apply either a single feature extraction scheme or a fixed object model for motion detection and tracking,
we introduce a multiple feature extraction scheme for robust motion detection. The proposed algorithms include 1) multiple
image feature extraction including the fuzzy compactness based detection of interesting points and fuzzy blobs, 2) adaptive
image segmentation via multiple features, 3) Hierarchical motion detection, 4) a flexible model of human motion adapted
in both rigid and non-rigid conditions, and 5) Fuzzy decision making via multiple features.