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Third, sophisticated mathematical models are needed to simulate quantitatively influence of the observation conditions, and parameter and setting of the simulated camera on the generated image. In order to start training program, having a training strategy is a must.

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The success of vehicle or aircraft recognition and identification training depends greatly on the images available for the training.

By success, we mean that soldiers acquire skills transferable to new imagery in the database itself and ideally to field settings with real vehicles as well. Training success does not mean that soldiers simply memorize the images presented during training. To accomplish this goal, a large database must be established, which can be a very costly part of the development process.

An image database with these factors provides soldiers with a wide variety of examples.

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In addition, they are more likely to transfer their skill to field conditions if they have seen vehicles or aircrafts in a variety of conditions during training. It is well known that vehicle or aircraft identification is easiest with flank views, more difficult with oblique aspects and hardest with front and rear views.

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Appreciation for aspect angle has an interdependent effect on vehicle or aircraft identification. For instance, if subjects can determine that they are looking at the left front oblique of a vehicle or aircraft; this has important implications for ascertaining the identity of that vehicle. Knowing the aspect angle also allows subjects to evaluate the level of risk present in a combat situation and to prioritize their attention. Incidental cues in the training imagery present a great threat to ensuring that soldiers can identify vehicles or aircraft in an operational environment.

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An obvious attempt is often made to conceal any incidental markings or cues as insignias, an atypical vehicle or aircraft configuration, the presence of a soldier and distinctive backgrounds in the scene. Subjects are likely to focus on these incidental cues rather than the characteristics of the vehicle itself. Congestion coefficient, target area, area to be analyzed Congestion coefficient represents the attribute of the whole image presented to the subjects.

Ranged from 1 to 10, this factor will be lowered when subjects will have only few targets in the scene and increased when multiple targets will be shown. All of them affect the probability to resolve the target. Thermal sensors are often the primary target acquisition system regardless of time of day, and day and night signatures differ dramatically due to solar loading during the day.

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For instance, parts of the hull or turret may be cool at night and very difficult to detect, whereas during the day these components are quite visible because they have been heated from exposure to the sun. During the day, tracks and wheels often appear less hot relative to the rest of the vehicle. If the vehicle has been operating, the engine and exhaust will still be hot, but their signatures will not be as distinct when the hull and turret are also hot.

Range to target The thermal signature changes with range. More detail is visible at closer ranges. Fewer gradations in vehicle temperature are visible at longer ranges although the hottest spots still 28 stand out. The literature suggests that soldiers can identify vehicles at tactical distances, particularly with flank and oblique views.

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But the most efficient training is on close-up images first, particularly with soldiers who have low initial skills. Advanced skill training should involve vehicles at greater and greater distances. Therefore, the image database must contain images of vehicles at different ranges. We think that 4 ranges will be OK, but 5 or 6 would work as well. More ranges would probably yield unnecessarily redundant information with each increment in range; fewer ranges would probably provide a change too great in the thermal signature with each increment.

A compromise must be found between these characteristics in order to achieve an image with a good fidelity.

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Sensor polarity Most thermal sensors have reverse polarity, where the hotter spots can be displayed as black black-hot or as white white-hot. Switching from one polarity to the other helps the soldiers to discern critical features. By using both polarities, soldiers frequently are able to discern different pieces of information, which, collectively, help them, identify the vehicle.

The minimum observing time is calculated as the time necessary to scan the whole image.

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The value obtained can be an acceptable value for the observing time. Experiments revealed that a value for the observing time greater than 20s is useless and a plot will be presented bellow as example of how the probability of resolving the depends with observing time.

Image scanning techniques The human eye searches a display by aiming the fovea at different locations in an image, rapidly moving from one fixation point to another.

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Each fixation lasts about a third of a second on the average. The large jumps are called saccades; a saccade plus a fixation is termed a glimpse. During the saccadic motion, vision is greatly reduced. The speed and length of the saccade are dependent on the task, being faster for situations where, for example, the allowed time to perform the search is limited to a short interval, and being physically shorter when the density of information in the image is higher.

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For more complicated scenes, suplimente anti-imbatranire pentru barbati peste 40 de ani fovea will tend to interrogate areas of high information content.

The choice of the areas to interrogate will be governed in part by the observer's expectations and by the information desired from the image.

Obviously the characteristic motion of the eye will play a central role in the ability of an observer to perform visual search; therefore, models of visual search must explicitly or implicitly account for some aspects of typical eye motion. There are numerous factors which influence an observer's ability to acquire a target.

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Only some of the major considerations are discussed here. For competition search, as would be expected, the more the target differs from the nontargets in contrast, marpol anexa 2 produse anti imbatranire, or size, the faster the target will be found. This assumes that other factors such as the number of targets and the target density are kept constant.

Any other factors that would act to increase the target discriminability would also lead to decreased mean search times. Also, as might be expected, increasing the number of false targets, their homogeneity, or the size of the search field will generally result in increased mean search times.

Any change 29 AFASES - - to the displayed image that would require an observer either to examine more objects, or to examine the objects in greater detail, will lengthen the search process. Some authors found that an observer, while first training on a set of targets in their case, letters of the alphabetwill scan more slowly while searching through a list for one of many marpol anexa 2 marpol anexa 2 produse anti imbatranire anti imbatranire. Others point out that after learning the target set thoroughly an observer can scan through a list looking for one of many targets in a target set just as fast as when scanning for a single target.

There are also authors who state that even though the scan rate for multiple targets may be the marpol anexa 2 produse anti imbatranire after learning, the error rates increase. Practice also can improve the performance of tasks such as threshold detection that do not involve learning the shapes or characteristic features of targets.

If the observer has been pre-briefed so that he or she knows probable positions or states of the target, mean demachiant lancome times will be shorter. On the other hand, if a target does not appear very much like the mental image of the target that has been learned by the observer, the search process will be more difficult.

If the target is camouflaged or partially occluded, the target discriminability from background decreases and the search process becomes correspondingly more difficult.

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The better subjects understand the nature of FLIR and the parameters that affect what is marpol anexa 2 produse anti imbatranire through a thermal sight, the more adept they become at interpreting thermal signatures. This knowledge will also facilitate transfer from night to day images, to new tactical images, to interpreting objects in the terrain, and to detecting targets.

Knowing thermal features of vehicle or aircraft classes It is obvious that vehicles or aircraft within a certain class or certain type have similarities tanks are tracked, have a main gun and a turret and typically the engine is in the rear. In addition, there are some distinctions among classes or types.

There are many ways to accomplish that goal.

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Another technique can be to mix the images: a visible image of the vehicle or aircraft is shown, which turns then into a series of white-hot and black-hot thermal images, and then back to the original visible image. The program software should point out the specific thermal features as the images are displayed. Conduct this instruction with near images.

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Cues common to most vehicles engine, tread friction and exhaust heat and cues associated with vehicle classes large, prominent turrets on tanks should be pointed out for each vehicle.

And finally, the features that give each vehicle its unique thermal signature must be stressed.