&lt;title&gt;Unified approach to feature extraction for model-based ATR&lt;/title&gt;

Stach, John; Sharma, Rajesh; Shaw, Scott

doi:10.1117/12.242043

Cited by 10 publications

(4 citation statements)

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“…Automated real-time identification of identical features in two separate images is the subject of much investigation [6] [7] and although much progress has been made, the general capability allowing operation in general terrain environment encountered in military scenarios does not exist. At the current state of the art a human is still required to look at two photographs and select common points.…”

Section: Color Difference Image Control Point Entrymentioning

confidence: 99%

Multi-eye input experiments for UAV image navigation and control

Baer

2010

SPIE Proceedings

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Real time Unmanned Arial Vehicle (UAV) image registration is achieved by stimulating one eye with a live video image from a flying UAV while stimulating the other eye with calculated images. The calculated image is initialized by telemetry signals from the UAV and corrected using the Perspective View Nascent Technology (PVNT) software package model-image feedback algorithm. Live and registered calculated images are superimposed allowing command functions including target geo-location, UAV sensor slewing, tracking, and way point flight control. When the same equipment is used with the naked eye the forward observer function can be implemented to produce accurate target coordinates.The paper will then discuss UAV mission control and forward observer target tracking experiments conducted at Camp Roberts, California. .

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Section: Color Difference Image Control Point Entrymentioning

confidence: 99%

Multi-eye input experiments for UAV image navigation and control

Baer

2010

SPIE Proceedings

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“…The jX(i; j) ? n (i; j)j term in equation (2) can rapidly determine the spatial locations of poor template ts. The hypotheses which provide n 0 (i; j) over the local area are now searched.…”

Section: Mbv For Template Perturbationmentioning

confidence: 99%

Hybrid Template- and Model-Based ATR Formulation

Subotic¹,

Gorman²,

Welby³

1998

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Template-based automatic target recognition (ATR) algorithms such as the Synthetic Aperture Radar Target Location and Recognition System (STARLOS) algorithm typically use separate templates to represent target signatures for ranges of articulations, aspect, depression, and squint angles. There is a performance tradeo between ATR accuracy and the number of templates used. We use a hybrid model/template with target models to augment a small set of target templates. The basic idea will be to determine the transformation or perturbations required to modify a given template so that it accurately represents the signature of a neighboring sensor geometry or target articulation. By incorporating a model for these perturbations into the ATR algorithm, we can reduce the total number of templates required and provide robustness to new collection geometries, obscuration, and articulation.

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“…Some techniques segment images to obtain targets, which are then modeled, [1]- [3]. Others implicitly recognize targets by employing feature extraction, [4], [5]. Furthermore, neural networks, statistical methods, etc., or a combination thereof have also been used, [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

An efficient quadratic correlation filter for automatic target recognition

et al. 2007

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Quadratic Correlation Filters have recently been used for Automatic Target Recognition (ATR). Among these, the Rayleigh Quotient Quadratic Correlation Filter (RQQCF) was found to give excellent performance when tested extensively with Infrared imagery. In the RQQCF method, the filter coefficients are obtained, from a set of training images, such that the response to the filter is large when the input is a target and small when the input is clutter. The method explicitly maximizes a class separation metric to obtain optimal performance. In this paper, a novel transform domain approach is presented for ATR using the RQQCF. The proposed approach, called the Transform Domain RQQCF (TDRQQCF) considerably reduces the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. Since the dimensionality of the data points is reduced, this method also overcomes the common problem of dealing with low rank matrices arising from the lack of large training sets in practice. This is achieved while retaining the high recognition accuracy of the original RQQCF technique. The proposed method is tested using IR imagery, and sample results are presented which confirm its excellent properties.

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<title>Unified approach to feature extraction for model-based ATR</title>

Cited by 10 publications

References 0 publications

Multi-eye input experiments for UAV image navigation and control

Multi-eye input experiments for UAV image navigation and control

Hybrid Template- and Model-Based ATR Formulation

An efficient quadratic correlation filter for automatic target recognition

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