Object segmentation, recognition and localization are challenging because of the large amount of input data and because of the invariances required. We discuss strategies to overcome these problems, considering sensors, algorithms and architectures. Specifically, we address neural nets and Hough strategies. The ability of implicit learning makes neural nets interesting for industrial inspection: compared to classical methods they promise robustness against variations of the input data. Furthermore, no expert is necessary for supervision. The inherent parallelity simplifies the design of algorithms. However, the advantages are counterbalanced by a serious drawback: the high computational complexity -- if images are considered. The ability of optics, to help by its inherent parallelity is limited, because neural architectures are usually space variant and cannot simply be implemented optically. We discuss approaching these problems by feature extraction, by sparse algorithms and by space invariant architectures. A competitive strategy for object recognition and localization is based on probability tables, such as the Hough transform uses: a couple of weak but independent hypotheses can give a safe decision about the kind and the locus of an object. This method requires a learning phase prior to the working phase, as the neural strategy does. In that sense it is similar, however, the computational complexity can be much smaller. This makes it possible to segment, localize and recognize objects invariant against shift, rotation and scale.