O. Faugeras and K. Price, "Semantic Description of Aerial Images using Stochastic Labeling," IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-3, Nov 1981, pp638-642. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
A Survey of Image Registration Techniques - Brown (1992) (27 citations) (Correct)
.... HIGHER LEVEL FEATURES uses relations and other higher level information, good for inexact and local matching Structural features: graphs of subpattern configurations [Mohr 90] Syntactic features: grammars composed from patterns [Bunke 90] Semantic networks: scene regions and their relations [Faugeras 81] MATCHING AGAINST MODELS accurate intrinsic structure, noise in one image only Anatomic atlas [Dann 89] Geographic map [Maitre 87] Object model [Terzopoulos 87] Table 3: Feature Spaces used in Image Registration 40 ture spaces include: edges, contours, surfaces, other salient features ....
O. Faugeras and K. Price, "Semantic Description of Aerial Images using Stochastic Labeling," IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-3, Nov 1981, pp638-642.
Neural Systems for Motion Analysis: Single Neuron and.. - Huntsberger, Rose.. (Correct)
....matching. Support for the premise that relatively low level features can be used for motion analysis comes from the study by Shariat and Price [56] Point correspondence across frames is done through a color segmentation step [52] followed by region matching based on shape, size and position [11]. Motion analysis was done on the centroids of the regions after correspondence was established over more than two frames. In their system, the type of motion was classi ed using an error measure based on the hypothesis that the motion is pure translation [56] Subsequent steps in their analysis ....
Faugeras, O. D. and K. E. Price (1981). Semantic description of aerial images using stochastic labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 633-642.
Energy Minimization and Relaxation Labeling - Li, Wang, Chan, Petrou (1997) (1 citation) (Correct)
....solution quality by the quantitative gain G(f ) and regard RL as just a mechanism of minimization rather than one of interpretation. The concern here is how well an algorithm can minimize an energy regardless of the problem domain. Price concludes is that the algorithms of Faugeras and Price [7] and Hummel and Zucker [10] which use the gradient projection techniques, are the best a result consistent with ours; that by Peleg [25] converges too fast to yield good result; and that of Rosenfeld et al. performs only adequately. There, the numerical performance measures are obtained ....
O. D. Faugeras and K. Price. "Semantic description of aerial images using stochastic labeling". IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:638--642, November 1981.
Generating Markov Random Field Image Analysis Systems From Examples - Milun (1995) (Correct)
....al. 1975; Hummel and Zucker, 1980; Rutkowski et al. 1981; Rosenfeld and Smith, 1981; Fekete et al. 1981; Hummel, March 1983] were responsible for much of the earlier work on probabilistic models and networks for vision. Another early pioneer in this area is Faugeras [Faugeras and Berthod, 1981; Faugeras and Price, 1981] Price [Price, 1985] and Kittler and Illingworth [Kittler and Illingworth, 1985] survey the early work on relaxation and compare their algorithms on a realistic image analysis problem. More recently Hancock and Kittler have revived the probabilistic relaxation approach to edge relaxation ....
Olivier D. Faugeras and Keith Price. Semantic description of aerial images using stochastic labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-3(6):633--642, 1981.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC