&lt;title&gt;Deformable models for object recognition in aerial images&lt;/title&gt;

Reno, A.L.; Gillies, Donald; Booth, David T.

doi:10.1117/12.323852

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, we can write I-A/PTGTGR b=phase()VPR= 32 (17) (4rr) ' RTRR To make this approach to recognition practical, a large database of 's must be generated and stored for each target, with each database consisting of different incident and observation angles for the carrier frequencies of interest. The computational complexity of generating such databases, especially with FISC, is horrendous, so it is prudent to go about it in carefully thought out manner.…”

Section: Target Reflectancementioning

confidence: 99%

Tracking and recognition of airborne targets via commercial television and FM radio signals

Lanterman

1999

SPIE Proceedings

View full text Add to dashboard Cite

We formulate a Bayesian approach to the joint tracking and recognition of airborne targets via reflected commercial television and FM radio signals measured by an array of sensors. Such passive systems may remain covert, whereas traditional active systems must reveal their presence and location by their transmissions. Since the number of aircraft in the scene is not known a priori, and targets may enter and leave the scene at unknown times, the parameter space is a union of subspaces of varying dimension as well as varying target classes. Targets tracks are parameterized via both positions and orientations, with the orientations naturally represented as elements of the special orthogonal group SO(3). A prior on target tracks is constructed from Newtonian equations of motion. This prior results in a coupling between the position and orientation estimates, yielding a coupling between the tracking and recognition problems.A likelihood function is formulated which incorporates the sensor array geometry, the doppler shift associated with moving targets, the frequency and angle dependent complex reflectance of the particular target types, and the energy loss predicted by the radar equation. The likelihood and prior are combined to form a posterior distribution. Monte Carlo random sampling algorithms are proposed for exploring the posterior distribution; these methods avoid the need for linearizations associated with extended Kalman filtering or explicit representations of the density required by traditional nonlinear filters.

show abstract

Section: Target Reflectancementioning

confidence: 99%

Tracking and recognition of airborne targets via commercial television and FM radio signals

Lanterman

1999

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…Recently, different regionbased snake models have been proposed. In concrete applications it is possible to design the snake to classify image features assuming known parameters of the statistical laws [12]. More general approaches treat unknown statistics parameters of image regions.…”

Section: Introductionmentioning

confidence: 99%

Region-based approach for discriminant snakes

Radeva

Vitrià²

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)

View full text Add to dashboard Cite

This paper proposes a statistic framework for segmenting textured areas over real images by discriminant snakes. Our active contour model has the ability to learn different texture prototypes and generate a global statistical model from a multi-valued function. This function is generated by means of filter responses over the texture regions. Linear discriminant analysis is performed to obtain a statistical classifier embodied into the snake scheme. Given an input image composed by different texture types, a likelihood map is built and the discriminant snake deforms on it to delineate regions with similar texture descriptions according to the learned texture patterns. Our method is tested on two different image applications: aerial images and medical (ultrasound) images, and the results are very encouraging.

show abstract