Analysis of fuzzy clustering and a generic fuzzy rule-based image segmentation technique

Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses some of these issues by introducing a new generic fuzzy rule based image segmentation (GFRIS) technique, which is both application independent and can incorporate the spatial relationships of the pixels as well. A qualitative comparison is presented between the segmentation results obtained using this method and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms using an empirical discrepancy method. The results demonstrate this approach exhibits significant improvements over these popular fuzzy clustering algorithms for a wide range of differing image types

Image segmentation evaluation using an integrated framework

In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation algorithms, and allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding and visualization. We then utilize this framework to automatically evaluate four distinct segmentation algorithms, and present and discuss the results and statistical findings of the experiment

Prototipo para la identificación de cambios visuales en ejercicios de estimulación bioeléctrica neuronal

Auxiliar de InvestigaciónEsta investigación de grado, se llevó a cabo bajo la modalidad trabajo de auxiliar de investigación, el cual se enfocó como un proyecto de desarrollo, bajo los parámetros de la línea de investigación de procesos psicológicos de la Universidad Católica de Colombia. Ante la necesidad de un software a la medida que permita al área de psicología llevar la historia médica de la actividad cerebral en un paciente y poder determinar patrones de hiperactividad a través de la toma y análisis de imágenes encefalografías de los hemisferios de la masa cerebral. Gracias a la implementación del prototipo para la identificación de cambios visuales en ejercicios de estimulación bioeléctrica neuronal se podrán crear mecanismos, actividades o entrenamientos que permita mejorar ese déficit de atención en niños.PregradoIngeniero de Sistema

Unsupervised segmentation evaluation measures for parameter optimization in indicator-Kriging

This work investigates the performance of four unsupervised evaluation measures for the optimization of the user-defined parameter in the indicator-Kriging segmentation algorithm (Oh and Lindquist 1999). We focus on the application of this algorithm to micro-computed tomography (µCT) scans of porous media. Because ground truth segmentations were required for the set of test images, simulated 3D images were created based on the image acquisition in µCT, starting from segmentations of real µCT-scans. The tested unsupervised evaluation measures were the intra-class variance, Otsu's parameter, Zeboudj's parameter and the grey value contrast. The intra-class variance proved to be the most efficient at selecting an optimal segmentation parameter

Segmentation quality evaluation for large scale mapping purposes in Flanders, Belgium

In Flanders the large scale reference database called GRB, takes care of the layout, exchange and management of large scale geographic information with respect to, amongst others, roads, buildings and parcels. As Flanders is extremely urbanized (average population density of about 450 inhabitants per square kilometer), the large scale maps need to be highly accurate. Currently, accuracies at the centimeter level are guaranteed due to topographic field measurements aided by standard photogrammetry based on analogue aerial photographs. In order to speed up the GRB production and to ensure large scale map products at the long term, it is essential to automate this labour-intensive, but highly accurate production process. Segmentation of very high resolution digital images could be an alternative approach for maintaining and updating the Flemish GRB as long as high accuracy segmentation results are obtained. Based on DMC images (8 cm ground resolution) and several reference buildings, a comprehensive sensitivity analysis is performed testing different segmentation parameter settings in order to gain insight into their impact on segmentation accuracy. The segmentation quality is evaluated using similarity measures focusing on aspects of presence, shape and positional accuracy where emphasis is placed on interpretability of the measures with respect to operational conditions put on the reference data. The end user should be able to read the measures and link this to the return-on-investment he will gain by using a given segmentation process on his data

A fuzzy rule-based colour image segmentation algorithm

Most fuzzy rule-based image segmentation techniques to date have been primarily developed for gray level images. In this paper, a new algorithm called fuzzy rule-based colour image segmentation (FRCIS) is proposed by extending the generic fuzzy rule-based image segmentation (GFFUS) algorithm G.C. Karmakar, L.S. Dooley [2002] and integrating a novel algorithm for averaging hue angles. Qualitative and quantitative analysis of the performance of FRCIS is examined and contrasted with the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms for both the hue-saturation-value (HSV) and RGB colour models. Overall, FRCIS provides considerable improvement for many different image types