Learn More
Less constrained iris identification systems at a distance and on the move suffer from poor resolution and poor quality of the captured iris images, which significantly degrades iris recognition performance. This paper proposes a new signal-level fusion approach which incorporates a quality score into a reconstruction-based super-resolution process to(More)
In recent years, Content Delivery Networks (CDN) and Peerto-Peer (P2P) networks have emerged as two effective paradigms for delivering multimedia contents over the Internet. An important feature in CDN and P2P networks is the data redundancy across multiple servers/peers which enables efficient media delivery. In this paper, we propose a network coding(More)
In this paper, we introduce a novel approach to grade prostate malignancy using digitized histopathological specimens of the prostate tissue. Most of the approaches proposed in the literature to address this problem utilize various textural features computed from the prostate tissue image. Our approach differs in that we only focus on the tissue structure(More)
Recent years have witnessed an explosive growth in multimedia streaming applications over the Internet. Notably, Content Delivery Networks (CDN) and Peer-to-Peer (P2P) networks have emerged as two effective paradigms for delivering multimedia contents over the Internet. One salient feature shared between these two networks is the inherent support for path(More)
The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the(More)
The well-known Gleason grading method for an H&E prostatic carcinoma tissue image uses morphological features of histology patterns within a tissue slide to classify it into 5 grades. We have developed an automated gland segmentation and classification method that will be used for automated Gleason grading of a prostatic carcinoma tissue image. We(More)
The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non-ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First,(More)
A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue(More)
A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These(More)
Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed(More)