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1.
IEEE Trans Pattern Anal Mach Intell ; 39(3): 603-616, 2017 03.
Artigo em Inglês | MEDLINE | ID: mdl-27071162

RESUMO

Random refraction occurs in turbulence and through a wavy water-air interface. It creates distortion that changes in space, time and with viewpoint. Localizing objects in three dimensions (3D) despite this random distortion is important to some predators and also to submariners avoiding the salient use of periscopes. We take a multiview approach to this task. Refracted distortion statistics induce a probabilistic relation between any pixel location and a line of sight in space. Measurements of an object's random projection from multiple views and times lead to a likelihood function of the object's 3D location. The likelihood leads to estimates of the 3D location and its uncertainty. Furthermore, multiview images acquired simultaneously in a wide stereo baseline have uncorrelated distortions. This helps reduce the acquisition time needed for localization. The method is demonstrated in stereoscopic video sequences, both in a lab and a swimming pool.

2.
IEEE Trans Pattern Anal Mach Intell ; 35(1): 245-51, 2013 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-23154325

RESUMO

Refraction causes random dynamic distortions in atmospheric turbulence and in views across a water interface. The latter scenario is experienced by submerged animals seeking to detect prey or avoid predators, which may be airborne or on land. Man encounters this when surveying a scene by a submarine or divers while wishing to avoid the use of an attention-drawing periscope. The problem of inverting random refracted dynamic distortions is difficult, particularly when some of the objects in the field of view (FOV) are moving. On the other hand, in many cases, just those moving objects are of interest, as they reveal animal, human, or machine activity. Furthermore, detecting and tracking these objects does not necessitate handling the difficult task of complete recovery of the scene. We show that moving objects can be detected very simply, with low false-positive rates, even when the distortions are very strong and dominate the object motion. Moreover, the moving object can be detected even if it has zero mean motion. While the object and distortion motions are random and unknown, they are mutually independent. This is expressed by a simple motion feature which enables discrimination of moving object points versus the background.


Assuntos
Algoritmos , Artefatos , Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Movimento (Física) , Reconhecimento Automatizado de Padrão/métodos , Refratometria/métodos
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