Motivation Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high p...

We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter s...

Owing to considering the distribution of the gray information and the spatial neighbor information with using the two-dimensional (2-D) histogram of the image, The 2-D maximum Tsallis entropy(2DMTE) method often gets better segmentation results, and owing to a controllable parameter, it has better flexibility than other 2-D entropy methods. However, its performance is sensitive to its parameter...

Existing formulations for optical flow estimation and image segmentation have used Bayesian Networks and Markov Random Field (MRF) priors to impose smoothness of segmentation. These approaches typically focus on estimation in a single time slice based on two consecutive images. We develop a motion segmentation framework for a continuous stream of images using inference in a corresponding Dynami...

An important parameter assessed during the semen analysis is the overall morphology, or shape of the sperm. Currently, the morphological analysis of sperm is done manually and is based on visual observation of at least 200 spermatozoa in a microscope followed by a classification stage based on strict criteria. But this method has led to incorrect results due to various factors such as different...

ABSTRACT: In this paper we review aspects of our automatic speech segmentation system that has been utilised in conjunction with our speech synthesis research. The speech segmentation system is based on a hidden Markov model phone recogniser using training strategies optimised for the segmentation task. Our research includes an analysis of the various aspects of the phone recogniser’s design an...