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72 1024x768 The paper presents the research results regarding automatic classification of rock images, taken under an optical microscope in varying lighting conditions and with different polarization states. Classification was conducted with the use of 4 pattern recognition methods: nearest neighbor, K-nearest neighbors, nearest mode, and optimal spherical neighborhoods. The research was conducted on thin sections of 5 rocks. During research the CIELAB color space and a 9D feature space were used. The research results indicate that changing both lighting conditions along with polarization states results in worsening the classification outcome, although not substantially. The conducted research indicates that during automatic classification, of rocks photographed in varying lighting and polarization conditions, the highest percentage of correctly classified rocks apx 97% is given by the nearest neighbor method. The results also indicate the optimal spherical neighborhoods method is the safest method of those tested , which means that it returns the lowest number of incorrect classifications. Normal 0 21 false false false PL X-NONE X-NONE /* Style Definitions */  table.MsoNormalTable  {mso-style-name:Standardowy;  mso-tstyle-rowband-size:0;  mso-tstyle-colband-size:0;  mso-style-noshow:yes;  mso-style-priority:99;  mso-style-qformat:yes;  mso-style-parent:"";  mso-padding-alt:0cm 5.4pt 0cm 5.4pt;  mso-para-margin:0cm;  mso-para-margin-bottom:.0001pt;  mso-pagination:widow-orphan;  font-size:11.0pt;  font-family:"Calibri","sans-serif";  mso-ascii-font-family:Calibri;  mso-ascii-theme-font:minor-latin;  mso-fareast-font-family:"Times New Roman";  mso-fareast-theme-font:minor-fareast;  mso-hansi-font-family:Calibri;  mso-hansi-theme-font:minor-latin;  mso-bidi-font-family:"Times New Roman";  mso-bidi-theme-font:minor-bidi;}

geologia

Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions