This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm, which extracts object length, width, area, aspect ratio, orientation and boundary from suitably thresholded images. In addition to being insensitive to scale and rotation, these simulations indicate that the features derived from angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. We will discussed these architectures, stressing the micro-optical approach. Test and evaluation using simple synthetic object data will be described. We will also describe the results of a study that uses object boundary (derivable from angular correlation) to classify objects using a neural network.