In this paper we present a technique for the summarization and spatiotemporal scaling of video content. A Self Organizing Map (SOM) neural network can be used o acquire a rough generalization of the spatiotemporal trajectories of moving objects, in the form of few selected nodes along these trajectories. We introduce a hybrid technique, combining SOM with geometric analysis to properly densify ...

A new Self-Organizing Map is proposed for information parallel processing purpose. In this model, Parallel-SOM, there are two separate layers of neurons connected together. The number of neurons in both layer and connections between them is equal to the number of total elements of input signals. The weight updating is managed through a sequence of operations among some unitary transformation an...

Fingerprint image segmentation is one of the important preprocessing steps in Automatic Fingerprint Identification Systems (AFIS). Segmentation separates image background from image foreground, removing unnecessary information from the image. This paper proposes a new fingerprint segmentation method using Haar wavelet and Kohonen’s Self Organizing Map (SOM). Fingerprint image was decomposed usi...

“The Self-Organizing Map (SOM), commonly also known as Kohonen network [1,2] is a computational method for the visualization and analysis of high-dimensional data, especially experimentally acquired information.” Roughly speaking, SOMs projects high-dimensional data into a two-dimensional map. This procedure is called “mapping” and it preserves the topology of the data in a way that similar dat...

In robotics applications, we often have noisy data that have temporal constraints due to the real world, such as sensory sequences generated by the motion of a robot through the environment. To recover the underlying structure of this noisy stream of data, we can do clustering with a self-organized map (SOM). We can view the SOM as a generative model, and it is seen to be doing maximum-likeliho...

The topography of the peptidergic neuronal subpopulations in the guinea pig celiac-superior mesenteric ganglion was studied analyzing the distribution of immunoreactivity to neuropeptide Y (NPY), somatostatin (SOM), and vasoactive intestinal polypeptide (VIP)/polypeptide HI (PHI). For comparison, the ganglion was also studied using antisera against the 2 catecholamine-synthesizing enzymes tyros...

Analysis and interpretation of large amounts of data has become one of the most important research tasks in earth systems science. Machine learning techniques such as artificial neural networks (ANNs) have several advantages in this regard. They are not only able to replicate the computational power of their biological examples but also are able to represent nonlinear relations, are capable of ...

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two o...

Self –Organising mapping networks (SOM) (Kohonen, 2001) is a specific family of neural networks uses unsupervised training. In unsupervised training no target output is provided and the network evolves until stabilisation. SOM can be used for data visualisation, clustering, estimation, vector projection and a variety of other purposes. It is an effective modelling tool for the visualisation of ...

 Anomaly detection and misuse detection are two major types of network intrusion detection systems.  Machine learning approaches have been used for anomaly detection. In particular, approaches based on self-organizing maps (SOMs) of artificial neural networks have shown effectiveness at identifying “unknown” attacks.  Effectiveness of using traditional SOM models is limited by the static nat...