The hierarchy of levels of ATR in .NET Generator USS Code 39 in .NET The hierarchy of levels of ATR

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18.1 The hierarchy of levels of ATR using barcode creation for .net vs 2010 control to generate, create bar code 39 image in .net vs 2010 applications. History of QR Code Standardization In this section, we de ne a f barcode 39 for .NET ew popularly used terms and acronyms in the ATR [18.57] world, starting with the ve levels in the ATR hierarchy.

Detection. Identifying the presence or absence of a target in a given scene. Classi cation.

This term, at least in Army parlance, originally meant distinguishing between vehicles with tracks and those with wheels. Since this de nition was. The authors are indebted to R ajeev Ramanath, who assisted signi cantly in the generation of this chapter, and in fact wrote some sections, and to Richard Sims, and John Irvine who provided careful reviews and extremely helpful feedforward.. 18.1 The hierarchy of levels of ATR generated however, most ATR d evelopments have bypassed classi cation with respect to performance requirements. Furthermore, the US Army is moving slowly away from tracked vehicles and this de nition will certainly be obsolete when that happens. Recognition.

Distinguish targets from similar kinds. For example, distinguish tanks from front-end loaders, jeeps from automobiles, rocket launchers from school buses, etc. Identi cation.

Identify the type of target such as the type of tank (whether it is T90 or M1, etc.). Characterization.

Describe the identi ed target in more detail. In Army parlance, this level of process characterizes the target based on how many and what types of weapons are on board, e.g.

, a T90 tank with an extra 55 gallon oil drum attached to the back. Each level of the ATR hierarchy is a re nement to the target description. Target characterization reveals the most detail of the target.

. ATR terminology There are a few other terms t hat are often used in the ATR literature. We give the de nitions as follows. Chip.

A small image usually containing the image of a single target, extracted from a large image of a scene. Target cueing algorithms, which identify the likely presence of a target, often produce chips as output. Detection rate.

Fraction of targets correctly detected by the system. Classi cation rate. Fraction of targets classi ed correctly, or more generally, the conditional probability of correct recognition given the target was detected.

Clutter. Objects that are imaged but are not targets. Clutter typically may be trees, houses, and other vehicles anything that is in the picture but is not target.

Cultural clutter. Refers to man-made objects like buildings, as opposed to natural objects. False alarm rate.

Generally, the fraction of the number of detections that do not correspond to actual targets. However, this de nition may be modi ed if the task is classi cation rather than detection. We observe that false alarm rate is not the same as probability of false alarm.

The false alarm rate is usually given in false alarms per square kilometer. See section 18.3.

. Automatic target recognition FLIR (Forward-looking Infrare d). This refers to images formed in the midwave (3 5 m) and longwave (8 14 m) spectral bands. The term forward looking is no longer really meaningful, but the acronym persists.

IFF. Identify friend or foe..

18.2 ATR system components The algorithmic components of an ATR system can be decomposed into preprocessing, detection, segmentation and classi cation (Fig. 18.1), although it is certainly possible that speci c system implementations might not have one or more of these components.

For example, a blob tracker can simply track the center of gravity of the infrared image, if there is just one hot region in the eld of view, and no explicit segmentation is required. ATR systems see images as their inputs. There is a variety of imaging modalities which have inherent advantages as each of them see different properties of these targets under consideration.

For example, a visually well camou aged tank in a eld may be hidden to the visible band, but clearly visible in the infrared bands, simply because its engines are running! Fig. 18.2 illustrates images taken from two different imaging modalities.

Fig. 18.2(a) shows what is captured by a regular video camera.

Fig. 18.2(b) shows the images captured by a FLIR camera.

Note how the engine of the tank and Hummer can be seen as hot. Table 18.1 lists some of the commonly used spectral bands.

The boundaries in wavelength of some of these ranges may vary from one user to another [18.9]..

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