Rough Histograms for Robust Statistics

Applied statistics are widely used in pattern recognition and other computing applications tofind the most likely value of a parameter. The use of classical empirical statistics is based upon assumption about normality of underhing density distribution of data. Whenthe data is corrupted by contaminated noise, then classical tools are usually not robust enough and the estimation of the mode is biased. In this article, we propose to estimate the main mode of a distribution by means of a rough histogram and we show that this estimation is robust to contamination.

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Olivier Strauss, Frederic Comby, Marie-José Aldon
• Keywords: Classical Empirical Statistics, Computer Vision, ICPR 2000, Likely Value, Pattern Recognition