Since the computer’s evolution in the middle of last century, pattern recognition of digital images based on correlations has been applied in science as well as technology areas. Their applications are broad and variety(Gonzalez & Woods, 2002). The techniques developed are used to identify micro-objects, for example the inclusion of virus bodies, bacteria, chromosomes, etc. (Alvarez-Borrego & Castro-Longoria, 2003; Alvarez-Borrego et al., 2002; Alvarez-Borrego & Solorza, 2010; Bueno et al., 2011; Forero-Vargas et al., 2003; Pech-Pacheco & Alvarez-Borrego, 1998; Zavala & Alvarez-Borrego, 1997). The analysis of those samples requires experience and moreover, the samples analyzed frequently contain material with different fragmentation degrees and this can lead to confusion and loss of information. There are other works in which the pattern recognition are based on probabilistic methodologies; here the objects are represented by their statistical characteristic features (Fergus et al., 2003; Holub et al., 2005). These are used to face identification and image restoration (Alon et al., 2009; Kong et al., 2010a;b; Ponce et al., 2006); fingerprints classification (Jain & Feng, 2011; Komarinski et al., 2005; Moses et al., 2009 ). Also, these works are useful to classify or count micro-objects, where the object variation, background, scale, illumination, etc., are taking into account (Arandjelovic & Zisserman, 2010; Barinova et al., 2010; Lempitsky &Zisserman, 2010).