This dissertation studies two pattern detection problems in images with complex background, in which standard segmentation techniques do not provide good results: the detection of structural units (SU`s) in images obtained through High resolution transmission Electron Microscopy and the detection of frontal human faces in images. The methods employed in the solution of both problems have many similarities - a neighborhood operator, basically composed of pre-processing, dimensionality reduction and classification steps, scans the input image searching for the patterns of interest. For SU detection three dimensionality reduction methods - Principal Component Analysis (PCA), PCA of the balanced training set (PACEq), and a new method, axis that maximize the distance to a given class centroid (MAXDIST) -, and two classifiers - Euclidean Distance (EUC) and back-propagation neural network (RN). The MAXDIST/EUC combination, with just one component, provided a detection rate of 82% with less false detections. For face detection a new approach was employed, using a back-propagation neural network as classifier. It takes as input a representation in the so-called face space and the reconstruction error (DFFS). In comparison with benchmark results from the literature, the proposed method reached similar detection rates.