Segmenting the microcirculation images based on selforganizing map

Hardware Implementations of Kohonen's Selforganizing Feature Map

In this paper we present a hardware realization of Kohonen's selforganizing feature map as a neural coprocessor connected to a personal computer (PC). We compare two concepts for this coprocessor, a vector-component-serial and processing-element-parallel array processor and a vector-component-parallel and processing-element-serial vector processor. Finally we give an overview about the whole sy...

Analysis of Mobile Radio Access Network Using the Selforganizing Map

Abstract: Mobile networks produce a huge amount of spatio-temporal data. The data consists of parameters of base stations and quality information of calls. The Self-Organizing Map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are eas...

Segmenting Dermoscopic Images

We propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentation. We present extensive experimental results achieved by the SDI algorithm on the lesion segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion Analysis ...

Segmenting Workstation Screen Images

A Hardware Supported System for Kohonen's Selforganizing Map

In this paper we present a system which enables easy and fast computation of Kohonen's selfor-ganizing map (SOM). It is a hardware supported system consisting of diierent parts. A neural coprocessor (COKOS) is connected to a personal computer (PC) by a special, asynchronous interface. The neural coprocessor works vector-component-parallel and processing-element-serial and speeds up the performa...