Sharif Digital Repository

Image segmentation is middle and an important task in image analysis and machine vision applications. The output images of imaging systems are often fuzzy because of noise, limitation in spatial and temporal resolution, blurring and intensity inhomogeneity in the objects. The goal of this thesis is exploring the fuzzy methods in multispectral image segmentation and proposing a new one to solve some of the recent difficulties and problems. The difficulties and problems such as simultaneous utilization of spatial and spectral information, necessity for dimension reduction, spatial and spectral and intra-cluster image information redundancy, existence of regions with widely varying size,... 

In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.
 

In recent years, molecular communications have received considerable attentions for communication between nano-machines. In this communication scheme, which uses molecules as information carriers, nano transmitter and receiver are in distance apart of nano meter to meter and information is encoded into type, concentration or releasing time of molecules. Since nano transmitter and receiver can do very simple operations, such as adding, sensing, and etc., for more complicated operations, a network of nano transceivers is required. In this thesis, a small scale imaging of an abnormality in the environment is investigated. To this end, a network of sensors is assumed to be placed in the...