Model-based techniques, neural networks, and correlation pattern recognition ͑CPR͒ are three of the most popular approaches to machine vision and pattern recognition. While the former two techniques are being widely pursued by the signal and image processing community, CPR has been mostly investigated by the optical pattern recognition community. As a result, the understanding of CPR and its applications has steadily grown over the last decade. CPR offers several unique advantages in machine vision applications. Correlation is a ''sensor independent'' approach that generally does not require data specific operations ͑such as segmentation, feature extraction, on-line model synthesis, or other processes͒. Consequently, correlation processors and algorithms can be shared by different types of sensors including infrared ͑IR͒, synthetic aperture radar ͑SAR͒, CCDs, and laser radar ͑LADAR͒ to name a few. Thus, CPR is a unifying approach to pattern recognition in a multi-sensor environment that does not require separate software suites to accommodate the different sensor phenomenology. Since the throughput of a correlator generally depends on the dimensions of the image, the algorithm complexity depends on the scene size and not on the number of objects to be detected. Not only does this provide a precise estimate for throughput, but it also avoids the computational bottlenecks faced by some approaches due to scene complexity, clutter, and the number of objects present. Among the many applications of CPR are medical image processing, security systems, fingerprint and face recognition, optical character recognition, surveillance, automatic target recognition, and object tracking. The underlying philosophy of CPR is that the signatures of objects to be recognized may be treated as signals to be detected. The present theory is a unique blend of concepts from the fields of signals and systems, Fourier optics, and statistical pattern recognition. Generally speaking, the algorithms are analytical closed-form solutions that optimize relevant performance criteria. From an implementation standpoint, both digital and optical pro-cessors have matured considerably so that practical realization of CPR is no longer a significant issue. While digital processors offer greater accuracy, optical processors promise far higher throughput rates. The challenge for optical correlators is to improve their fidelity in favor of greater dynamic ranges, and to design optimum algorithms given the limitations of the present devices. This special section on CPR is divided into three sections. The first five papers address fundamental theoretical issues pertaining to the design of correlation filters. Fazlollahi and Javidi discuss optimum techniques for recognizing targets in nonoverlapping noise. Mahalanobis and Kumar describe the properties of the maximum average correlation height ͑MACH͒ filter as an optimum detector. Laude and Formont talk about a Bayesian technique for recognizing targets in images. Guérault et al. propose an optimum technique for identifying the location of a target in a correlated background scene. Sims addresses information loss and distortions introduced by different types of data compression techniques and their implication on automatic target recognition systems including those based on CPR techniques. The next seven papers in the second section cover some novel developments in CPR. Shamir describes techniques for adaptive pattern recognition using hybrid correlators. Javidi, Wang, and Zhang describe a nonlinear filter for distortion-invariant pattern recognition. Fisher and Principe review the recent developments in the area of nonlinear minimum average correlation energy ͑MACE͒ filters. Hassebrook et al. introduce a high signalto-noise ratio technique for estimating orientation of a pattern using correlation. Casasent and Ashizawa report the results of using correlation filters for recognizing targets in SAR imagery, and Casasent and Shenoy introduce the concept of feature space trajectory. A novel approach for robot vision and vehicle navigation using holographic memory is described by Pu, Denkewalter, and Psaltis. The final four papers deal with optical hardware for correlation. Karins et al. review Litton Data System's miniature ruggedized optical correlator ͑MROC™͒ unit as
Correlation filters have been used in many applications such as object detection and recognition REF .