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This paper presents a novel Log-Euclidean metric inspired color-to-gray conversion model for faithfully preserving the contrast details of color image, which differs from the traditional Euclidean metric approaches. In the proposed model, motivated by the fact that Log-Euclidean metric has promising invariance properties such as inversion invariant and similarity invariant, we present a Log-Euclidean...
Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal...
Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including uniforming background, using the second-order Gaussian detector and applying...
Human biometrics are regarded as groundbreaking tools for preserving privacy and security in many computer vision applications such as, biometric authentication, secure access control, visa processing, and border checking. Biometric features such as, fingerprint, face, retina, iris patterns, voice waves, palm print and signatures are commonly used in biometric authentication. However, in recent years...
Analysing the retinal colour fundus is a critical step before any proposed computerised automatic detection of eye disease, especially Diabetic Retinopathy (DR). The retinal colour fundus image contains noise and varying low contrast of the blood vessel against its surrounding background. It makes it difficult to analyse the proper order of the vessel's network for detecting DR disease progress. The...
Accurate detection and position estimation of human objects is essential in many security applications including door access control, surveillance monitoring, intrusion detection, alarm monitoring and so on. This paper proposes an efficient approach for human detection and localization in secure access control by analysing facial features. The proposed technique captures the video scenes using a stereo...
This paper is concerned with automatically fusing multiple noisy and partially corrupted source images into a single denoised image. To create the fused image we minimise a convex objective function, which ensures spatial smoothness through total variation regularisation, and similarity to the source images via pixel-wise selective regularisation against each of the source images. We call this approach...
Image segmentation seeks to partition the pixels in images into distinct regions to assist other image processing functions such as object recognition. Over the last few years dictionary learning methods have become very popular for image processing tasks such as denoising, and recently structured low rank dictionary learning has been shown to be capable of promising results for recognition tasks...
Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values...
Image mating is the process of isolating the foreground in images and video. This task is challenging as it is severely under constrained. At each pixel we must estimate the foreground and background colour and the blending between them (alpha value). Most approaches calculate an affinity matrix and then minimise a system of linear equations to find the alpha matte. In this work we propose an extension...
Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent works in natural image matting relies on the local and manifold smoothness assumptions on foreground and background colors on which a cost function is established. In this paper, we present a framework of formulating new regularization for robust solutions and illustrate new algorithms...
Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent work by Levin, Lischinski and Weiss (2008) in natural image matting relies on the local smoothness assumptions on foreground and background colors on which a cost function is established. The closed-form solution has been derived based on certain degree of user inputs. In this paper,...
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