&lt;title&gt;Automatic detection of minelike targets in severely cluttered images including other man-made objects&lt;/title&gt;

Katz, Philip L.; Eggen, Christian J.; Haralick, Robert M.; Rystrom, Larry R.

doi:10.1117/12.211373

Cited by 3 publications

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“…Via thresholding and morphological operations, the target is isolated in the imaged scene, as described in [23]. We assume that each stereo pair image is comprised of MxN pixels.…”

Section: Variablementioning

confidence: 99%

Stereoscopic imaging through the sea surface: I. Theory and error analysis

Schmalz

1998

SPIE Proceedings

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The detection and recognition of submerged objects via airborne or spaceborne imaging platforms can be problematic, due to distortions resulting from refraction at the sea surface as well as absorption and scattering in atmospheric and aqueous media. In previous research, we showed that, given point-to-point measurement of interfacial topography and estimates of media optical parameters, it is often possible to partially invert refractive effects and compensate for first-order optical effects such as absorption and single scattering, thereby producing a visually acceptable, image-based reconstruction of a submerged target. Additionally, information concerning target depth or surface topography is obtained.Although potentially useful for human-in-the loop viewing scenarios, this method is not universally applicable to automated target recognition (ATR) due to reconstruction errors that tend to cluster in regions of detail.In this paper, we present theory and error analysis pertaining to stereoscopic viewing through a corrugated refractive interface and scattering media. Via stereoscopic imaging, one can produce a stereo pair image from which target depth and salient features can be extracted mathematically. Estimation of errors involved in this process is facilitated by partial knowledge of interfacial topography and is further enhanced by knowledge of media optical parameters. Analyses emphasize effects of sea state, topography estimation error, media optical parameters, and submergence depth. Reconstruction algorithm complexity is discussed in terms of work requirement applicable to sequential workstations and parallel computers.

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“…Via thresholding and morphological operations, the target is isolated in the imaged scene, as described in [23]. We assume that each stereo pair image is comprised of MxN pixels.…”

Section: Variablementioning

confidence: 99%