393 research outputs found
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES
The analysis of retinal vasculature in digital fundus images is important for
diagnosing eye related diseases. However, digital colour fundus images suffer from
low and varied contrast, and are also affected by noise, requiring the use of fundus
angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to
6 time’s higher contrast. However, FFA is an invasive method that requires contrast
agents to be injected and this can lead other physiological problems. A reported
digital image enhancement technique named RETICA that combines Retinex and ICA
(Independent Component Analysis) techniques, reduces varied contrast, and enhances
the low contrast blood vessels of model fundus images
A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images
This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.Air Force Office of Scientific Research (F496201-01-1-0397); Defense Advanced Research Project and the Office of Naval Research (N00014-95-0409, N00014-01-1-0624
Retinal vessel segmentation:An efficient graph cut approach with Retinex and local phase
Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer
NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM
Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes
mellitus affecting the retina. The pathologies of DR can be monitored by analysing
colour fundus images. However, the low and varied contrast between retinal vessels
and the background in colour fundus images remains an impediment to visual analysis
in particular in analysing tiny retinal vessels and capillary networks. To circumvent
this problem, fundus fluorescein angiography (FF A) that improves the image contrast
is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that
leads to other physiological problems and in the worst case may cause death.
The objective of this research is to develop a non-invasive digital Image
enhancement scheme that can overcome the problem of the varied and low contrast
colour fundus images in order that the contrast produced is comparable to the invasive
fluorescein method, and without introducing noise or artefacts. The developed image
enhancement algorithm (called RETICA) is incorporated into a newly developed
computerised DR system (called RETINO) that is capable to monitor and grade DR
severity using colour fundus images. RETINO grades DR severity into five stages,
namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR
and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image
using RETICA in the macular region and analysing the enlargement of the foveal
avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The
importance of this research is to improve image quality in order to increase the
accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading
through either direct observation or computer assisted diagnosis system
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