An image segmentation scheme is shown to be exceptionally successful through the application of high-level knowledge of the required image objects (cell nuclei). By tuning the algorithm’s single parameter, it is shown that the performance can be maximised for the dataset, but leads to individual failures that may require alternative choices. A second stage is introduced to process each of the r...

This paper proposes a target-detection scheme based on prior segmentation of the image. Introducing the prior knowledge of image structure provided by the previous segmentation eliminates many false target detections from background structure. The performance of the new scheme is shown to be identical to an ideal one-parameter CFAR for constant background. With real clutter backgrounds the back...

A new framework for adapting common ensemble clustering 9 methods to solve the image segmentation combination problem is pre10 sented. The framework is applied to the parameter selection problem in 11 image segmentation and compared with supervised parameter learning. 12 We quantitatively evaluate 9 ensemble clustering methods requiring a 13 known number of clusters and 4 with adaptive estimati...

Depth inclusion as an important parameter for dynamic selective visual attention is presented in this article. The model introduced in this paper is based on two previously developed models, dynamic selective visual attention and visual stereoscopy, giving rise to the socalled dynamic stereoscopic selective visual attention method. The three models are based on the accumulative computation prob...

Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. Our approach does not require parameter estimation , and is therefore particularly beneecial for cases in which estimation-based methods are most prone to error: when litt...

In order to achieve the very high accuracy rates required in unsupervised automated biomedical applications, it is often necessary to complement a successful segmentation algorithm with a robust error checking stage. The better the segmentation strategy, the less severe the error checking decisions need to be and the fewer correct segmentations that are discarded. These issues are dealt with in...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...