The self-organizing map SOM is an efficient tool for visualization of multidimensional numerical data. In this paper, an overview and categorization of both old and new methods for the visualization of SOM is presented. The purpose is to give an idea of what kind of information can be acquired from different presentations and how the SOM can best be utilized in exploratory data visualization. Most of the presented methods can also be applied in the more general case of first making a vector quantization e.g. k-means and then a vector projection e.g. Sammon's mapping.

SOM-based data visualization methods

http://eia.udg.es/%7Eqsalvi/papers/2006-MJ.pdf

Review of CMOS image sensors

The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

This paper presents a digital pixel sensor (DPS) array employing a time domain analogue-to-digital conversion (ADC) technique featuring adaptive dynamic range and programmable pixel response The digital pixel comprises a photodiode, a voltage comparator, and an 8-bit static memory The conversion characteristics of the ADC are determined by an array-based digital control circuit, which linearizes the pixel response, and sets the conversion range The ADC response is adapted to different lighting conditions by setting a single clock frequency Dynamic range compression was also experimentally demonstrated This clearly shows the potential of the proposed technique in overcoming the limited dynamic range typically imposed by the number of bits in a DPS A 64 /spl times/ 64 pixel array prototype was manufactured in a 035-/spl mu/m, five-metal, single poly, CMOS process Measurement results indicate a 100 dB dynamic range, a 41-s mean dark time and an average current of 16 /spl mu/A per DPS

/pdf/a-digital-pixel-sensor-array-with-programmable-dynamic-range-3yzmv620cw.pdf

A digital pixel sensor array with programmable dynamic range

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

A high dynamic range (HDR) CMOS image sensor architecture based on spiking pixel cells and its first implementation, a linear 128-pixel sensor, is presented. The pulse rate of the pixel cell is proportional to the local light intensity. The pixel cell features a pixel-level analog-to-digital (A/D) conversion by pulse counting. It allows one to tradeoff conversion precision versus frame rate. Sample sensor chips have been manufactured using a 0.8-/spl mu/m double metal CMOS technology, and the test results confirming the feasibility of the chosen approach are reported.

An HDR CMOS image sensor with spiking pixels, pixel-level ADC, and linear characteristics

Visual quality control is a demanding task of increasing importance in industrial manufacturing. Both speed and flexibility are of paramount importance for viable and competitive inspection systems. We have developed a dedicated neural network architecture for anomaly detection that can easily be trained by a single presentation of examples and is amenable to massively parallel VLSI implementation. We focus here on our ASIC and prototype system design effort for this network. >

Visual inspection in industrial manufacturing

Dimensionality reduction techniques for interactive visualization, exploratory data analysis, and classification

The focus of this work is on systematic methods for the visualization and quality assessment with regard to classificatio n of multivariate data sets. Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution, the degree of compactness and overlap of class regions and class separability, as well as information to identify outliers in the data set and trace them back to data acquisition. Assessment by visualization and numerical criteria can be exploited for interactive or automatic optimization of feature generation and selection/extraction in pattern recognition problems. Further, we provide a novel criterion to assess the credibility and reliability of the visualization obtained from high dimensional data projection. Our methods will be demonstrated using data from visual industrial quality control and mechatronic applications.

/pdf/systematic-methods-for-multivariate-data-visualization-and-pw99w72kt2.pdf

Systematic Methods for Multivariate Data Visualization and Numerical Assessment of Class Separability and Overlap in Automated Visual Industrial Quality Control

This paper describes a novel design environment for cognitive systems tailored to the needs of flexibility, speed and transparence demanded in numerous application domains as, for example, automated visual inspection for quality control, mechatronics, medical applications, and other cognitive tasks. Our QuickCog design environment combines methods from statistical pattern recognition, neural networks, image and signal processing, and exploratory data analysis. The main features of the design environment are visual programming, interactive sample set compilation and sample set oriented processing, automatic feature selection, automatic processing method selection and parameter optimisation, feature space visualisation, and powerful self-configuring classifiers. QuickCog is available on PC/Windows'95.

Andreas König

Papers

An HDR CMOS image sensor with spiking pixels, pixel-level ADC, and linear characteristics

Visual inspection in industrial manufacturing

Dimensionality reduction techniques for interactive visualization, exploratory data analysis, and classification

Systematic Methods for Multivariate Data Visualization and Numerical Assessment of Class Separability and Overlap in Automated Visual Industrial Quality Control

A transparent and flexible development environment for rapid design of cognitive systems