Showing papers on "Automatic target recognition published in 1986"
TL;DR: A review of the techniques used to solve the automatic target recognition (ATR) problem is given, with emphasis on algorithmic and implementation approaches.
Abstract: In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.
518 citations
4 Apr 1986
TL;DR: This paper presents a new methodology based on the Fisher linear discriminant method, but for the underdetermined case; that is, for the case of having only relatively small amounts of training data for each cluster of objects.
Abstract: An important technique for object recognition in electro-optics, signal processing, and image understanding is to use a training algorithm to create a data base against which to compare data for objects being tested. The data for each training and test object is represented as a vector in a space of possible high dimension, perhaps in the hundreds or thousands. It is usually desired to project this data onto a space of much lower dimension in such a way that separation of object clusters is preserved. The difficulty with using this approach as it is usually presented is that it leads to inordinately large generalized matrix eigensystems that must be analyzed. Just as drastic is the large amount of data required for implementation. For instance, Fisher's linear discriminant method usually requires having at least as many training vectors as the dimension of the representation space. This is a severe limitation in that it would be preferable to train on reasonable amounts of data, say on samples of 20 data vectors in each class of objects. In this paper, we present a new methodology based on the Fisher linear discriminant method, but for the underdetermined case; that is, for the case of having only relatively small amounts of training data for each cluster of objects. The new algorithm is based partly on the original Fisher algorithm and partly on more recent fast algorithms for matrix factorizations. We also present examples showing application of this algorithm to the problem of automatic target recognition using images from FLIR data.
4 citations
26 Mar 1986
TL;DR: Flexible template matching as mentioned in this paper automatically brings the unknown target image and each one, in turn of a set of class-defining template images (one per class) into mutual registration, without requiring any prior knowledge of the differences of view that may exist between them.
Abstract: The target recognition problem is complicated by the fact that target is doubly "unknown". The sensed image is obviously a function of target class: a fundamental problem of target recognition has been that the form of the sensed image is also a strong function of target "geometry". The differences of class, upon which classification necessarily depends, may therefore be com-pletely swamped by the irrelevant differences of view. Human recognition of targets is generally unaffected by this problem. A new method of Automatic Target Recognition, called Flexible Template Matching, is described. It eliminates "geometry" from the recognition problem, by means of a new registration algorithm. This automatically brings the unknown target image and each one, in turn, of a set of class-defining template images (one per class) into mutual registration, without requiring any prior knowledge of the differences of view that may exist between them. The elimination of all irrelevant differences of view (scale, position, rotation, aspect, etc) allows for an optimum match-decision to identify the one true template, based upon computation (using Bayes Formula) of the probability, for each template, that the observed match differences are a typical sample of the match-differences known to occur in a (registered) true match of the target and its template. Since the range and aspect of the target are provided as a by-product of the registration action, the match-error statistics used can be selected according to the observed position and orientation of the target.
Patent•
28 Jan 1986
TL;DR: The method involves comparing 6 sequentially spectra signal "Doppler" retransmitted by each target sequences of spectra corresponding to known target corresponding toknown target stored 4 in a dictionary memory and to indicate the type of target 7 when the spectra of the signal retransmission by the target were recognized.
Abstract: The method involves comparing 6 sequentially spectra signal "Doppler" retransmitted by each target sequences of spectra corresponding to known target stored 4 in a dictionary memory and to indicate the type of target 7 when the spectra of the signal retransmitted by the target were recognized 6. Application: radar, sonar. (CF DRAWING IN BOPI)