We present an unsupervised method for the fast detection of noisy objects of varying sizes and contrasts, which is implementable on an opto-electronic device. This method consists of first achieving multiresolution representation of the noisy data using successive Gaussian filterings. Then, the resulting set of Gaussian smoothings is compressed using principal component analysis (PCA). This compression is applied within regions of interest (ROI) that are previously detected using a fast technique adapted to the features of analyzed data. The different objects of interest are finally segmented using a standard valley thresholding technique, which is locally applied within each ROI. An experimental evaluation using synthetic images underlines the robustness of this method and its ability to achieve unsupervised detection of strongly noisy objects. Theoritical and experimental estimations of the computing power of a high-speed optical correlator and of a specialized digital processor have shown that it is faster to compute: optically global Gaussian filterings, the PCA-based compression being digitally performed. Experiments have also shown the potential of the proposed method for the fast detection of liver tumors from computer tomography (CT)-scan images. The feasibility of its hybrid opto-electronic implementation is demonstrated using an experimental optical correlator.