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.
This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image
representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.
This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
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