Sandor Z. Der

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This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking Infrared (FLIR) imagery using a large database of real FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA),(More)
A probe-based approach combined with image modeling is used to recognize targets in spatially resolved, single frame, forward looking infrared (FLIR) imagery. A probe is a simple mathematical function that operates locally on pixel values and produces an output that is directly usable by an algorithm. An empirical probability density function of the probe(More)
A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification(More)
Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as(More)
A pulsed ladar based object-recognition system with applications to automatic target recognition (ATR) is presented. The approach used is to fit the sensed range images to range templates extracted through a laser physics based simulation applied to geometric target models. A projection-based prescreener filters out more than 80% of candidate templates. For(More)