We present a technique for extracting the buildings from high resolution satellite imagery using adaptive fuzzy-genetic approach. The technique was inspired from the genetic image exploitation system, GENIE PRO, conducted by Perkins et al., (2005) but brings an important novelty, which is an adaptive-fuzzy module that fine-tunes the genetic algorithm parameters aiming to improve the feature ext...

Adaptive binarization methodologies threshold the intensity of pixels with respect to adjacent exploiting integral images. In turn, images are generally computed optimally using summed-area-table algorithm (SAT). This document presents a new adaptive technique based on fuzzy through an efficient design modified SAT for integrals. We define this methodology as FLAT (Fuzzy Local Thresholding). Th...

Lung cancer is the common cause of death among people. Detection of the tumor at the early stage can increase the chance of survival. Survival rate for lung cancer patients can increase from 14% to 49% if the disease is detected in time. Problem seemed to be increased while using x-ray for detection of tumor in lung cancer. Computed tomography is more efficient than x-ray. Hence, a lung cancer ...

since the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, instead of an ensemble of an n–d realization. although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to better results. in this paper, first a method is proposed to extract th...

The use of image fusion techniques has gained significant popularity in recent years; image fusion is one of important prime method in image processing. The term fusion means in general an approach to extraction of information acquired in several domains. Image fusion has been used in many application areas. In this paper, fuzzy based Image fusion using wavelet transform has been presented.

Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy the...

Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy the...

This contribution is intended as a survey of the existing fuzzy (or fuzzy related) ltering techniques for multichannel (and color in particular) images. We propose a classi cation of all these approaches to color image processing into four categories, based on the importance that fuzzy theory receives during the lter design: crude fuzzy, fuzzy paradigm based, fuzzy aggregative and fuzzy inferen...

Image segmentation—the process of defining objects in images—remains the most challenging problem in image processing despite decades of research. Many general methodologies have been proposed to date to tackle this problem. An emerging framework that has shown considerable promise recently is that of fuzzy connectedness. Images are by nature fuzzy. Object regions manifest themselves in images ...