A Probabilistic approach to sparse multi scale phase based stereo

Halıcı, Uğur
In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching are proposed. The disparity algorithm and the probabilistic approach are verified on various stereo image pairs.


A statistical approach to sparse multi-scale phase-based stereo
Ulusoy, İlkay (Elsevier BV, 2007-09-01)
In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
A unifying grid approach for solving potential flows applicable to structured and unstructured grid configurations
Cete, A. Ruhsen; Yuekselen, M. Adil; Kaynak, Uenver (Elsevier BV, 2008-01-01)
In this study, an efficient numerical method is proposed for unifying the structured and unstructured grid approaches for solving the potential flows. The new method, named as the "alternating cell directions implicit - ACDI", solves for the structured and unstructured grid configurations equally well. The new method in effect applies a line implicit method similar to the Line Gauss Seidel scheme for complex unstructured grids including mixed type quadrilateral and triangle cells. To this end, designated al...
A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems
SOYLU, Banu; Köksalan, Mustafa Murat (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01)
In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto...
A new likelihood approach to autonomous multiple model estimation
Söken, Halil Ersin (Elsevier BV, 2020-04-01)
This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....
An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models
TAORMİNA, RİCCARDO; GALELLİ, STEFANO; Karakaya, Gülşah; Ahipasaoglu, S. D. (Elsevier BV, 2016-11-01)
This work investigates the uncertainty associated to the presence of multiple subsets of predictors yielding data-driven models with the same, or similar, predictive accuracy. To handle this uncertainty effectively, we introduce a novel input variable selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), specifically conceived to identify all alternate subsets of predictors in a given dataset. The search process is based on a four-objective optimization problem that m...
Citation Formats
İ. ULUSOY PARNAS, U. Halıcı, and E. HANCOCK, “A Probabilistic approach to sparse multi scale phase based stereo,” 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45903.